Proceedings of the 14th European Conference on Knowledge Management Kaunas University of Technology, Lithuania 5-6 September 2013
Edited by Brigita JaniĹŤnaitÄ— and Monika Petraite Kaunas University of Technology, Kaunas, Lithuania
Volume Two A conference managed by ACPI, UK www.academic-conferences.org
6th European Conference on Intellectual Capital 11‐12 April 2014 Trnava, Slovak Republic
9th International Conference on e‐Learning 26‐27 June 2014 Santiago, Chile
14th European Conference on e‐ Government 12‐13 June 2014 Brasov, Romania
5th International Conference on Information Management and Evaluation Date and venue tbc
(formally ICIW)
2nd International Conference on Innovation and Entrepreneurship 6‐7 February 2014 Bangkok, Thailand 2nd International Conference on Management, Leadership and Governance 20‐21 March 2014 Wellesley, Massachusetts, USA 9th International Conference on Cyber Warfare & Security 24‐25 March 2014 West Lafayette, Indiana, USA
7th European Conference on Information Management & Evaluation 12‐13 September 2013 Sopot, Poland
14th European Conference on Knowledge Management 5‐6 September 2013 Kaunas, Lithuania
13th European Conference on Information Warfare & Security 3‐4 July 2014 University of Piraeus, Greece
See the website for latest dates & venues www.academic‐conferences.org
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European Conference on Social Media 10‐11 July 2014 Brighton, UK
10th International Conference on Intellectual Capital and Knowledge Management 24‐25 October 2013 Washington, DC, USA 12th European Conference on e‐ Learning 30‐31 October 2013 Sophia Antipolis, France 9th European Conference on Management, Leadership & Governance 14‐15 November 2013 Klagenfurt, Austria
International Conference on Cloud Security Management 17‐18 October 2013 Seattle, USA
7th European Conference on Game Based Learning 3‐4 October 2013 Porto, Portugal
8th European Conference on Innovation & Entrepreneurship 19‐20 September 2013 Brussels, Belgium
Copyright The Authors, 2013. All Rights Reserved. No reproduction, copy or transmission may be made without written permission from the individual authors. Papers have been double-blind peer reviewed before final submission to the conference. Initially, paper abstracts were read and selected by the conference panel for submission as possible papers for the conference. Many thanks to the reviewers who helped ensure the quality of the full papers. These Conference Proceedings have been submitted to Thomson ISI for indexing. Please note that the process of indexing can take up to a year to complete. Further copies of this book and previous year’s proceedings can be purchased from http://academic-bookshop.com E-Book ISBN: 978-1-909507-40-1 E-Book ISSN: 2048-8971 Book version ISBN: 978-1-909507-38-8 Book Version ISSN: 2048-8963 CD Version ISBN: 978-1-909507-41-8 CD Version ISSN: 2048-898X The Electronic version of the Proceedings is available to download at ISSUU.com. You will need to sign up to become an ISSUU user (no cost involved) and follow the link to http://issuu.com Published by Academic Conferences and Publishing International Limited Reading UK 44-118-972-4148 www.academic-publishing.org
Contents Volume Two Institutional Wiki: Evolving Public and Private Knowledge in MPMG
Lilian Noronha Nassif and Daniel Silva Carnevalli
482
Validation of the Scale of Knowledge Management Assessment in the Technical and Vocational Training Organization of Tehran
Fattah Nazem, Hossein Chenari and Omalbanin Sadeghi
490
Organisational Knowledge and Human Capital: A Conceptualisation for the Non-Profit Sector
Olimpia Neagu
496
Theorising a new Concept: ‘Micro Intellectual Capital’ (MIC) Using Knowledge From Inside the Classroom
Gary Oliver
506
Analysis of Awareness and Priorities, Focused on Intellectual Capital Among Slovak Companies
Ján Papula, Jana Volná, Anna Pilková, Jaroslav Huľvej
517
Towards Born-Global Innovation: the Role of Knowledge Management and Social Software
Jan M. Pawlowski
527
The Importance of Language Knowledge in International Companies
Corina Pelau, Irina Purcarea and Stelian Stancu
535
Linking External and Internal R&D and experience based Knowledge Flows for Innovation via Organisational Design Elements
Monika Petraite
543
The Role of Rational, Emotional and Spiritual Knowledge in Customer Relationship Management
Carmen Petrisoaia and Nicolae Al. Pop
552
Decision-Making Processes Based on Emotions in Universities as Learning Organizations
Magdalena Platis
560
Inter-Organizational Knowledge Transfer for Supply Chains in Crisis
Stavros Ponis and Epaminondas Koronis
569
Institutional Planning of Knowledge Generation
Evgeny Popov, Maxim Vlasov, Anna Yu.Veretennikova
577
Knowledge Audit: Findings From a Case Study in the Energy Sector
Gillian Ragsdell, Steve Probets, Ghosia Ahmed and Ian Murray
584
Shared Knowledge: Eliminating the “Ba”
Thomas Schalow
594
Correlation Between Individual Knowledge and Organizational Learning Process
Christian-Andreas Schumann and Claudia Tittmann
600
Heuristic for Unscheduled Public Transport Navigation System
José Sendra Salcedo and Osvaldo Cairó Battistuti
607
On Some Knowledge Issues in Sciences and Society
Dan Serbanescu
616
Using the SECI Model to Analyze Knowledge Creation in Students’ Software Teams
Mzwandile Shongwe
626
Do it Like the European Union (EU) Does: The Applicability of EU Knowledge Cost Management to Start ups
Evangelia Siachou and Dimitris Apostolidis
634
Use and Acceptance of Learning Platforms Within Universities
Boyka Simeonova, Pavel Bogolyubov and Evgeny Blagov
642
i
The Relationship between Knowledge Management and Employees' Empowerment in Justice Administration of Tehran Province
Faezeh Sohrabi, Alireza Chenari, Fattah Nazem, Mohamad Farahzadi and Masoumeh Bahmanabadi
652
Software Agent Societies for Process Management in Knowledge-Based Organization
Anna Sołtysik-Piorunkiewicz and Mariusz Żytniewski
661
Innovation and Sustainability: Two-Sided Knowledge Management by an Ice-Cream Producer
Inga Stankevice and Birute Slaustaite
670
Business Innovative Environment as a Prerequisite for a Long-run Competitive Advantage
Marta Christina Suciu and Cristina Andreea Florea
678
The Creative Society, Urban Revitalisation in the Creative Economy and Society: The Romanian Case
Marta-Christina Suciu and Mina FaneaIvanovici
686
Strategic Innovation – Access Path Towards a New Paradigm of an Academic Career Management
Marta-Christina Suciu, Irina Dumitrescu and Andrei Dumitrescu
692
Success Factors in Knowledge Sharing Behaviour Among Student Bloggers
Nor Intan Saniah Sulaiman, Mazlan Mohd Sappri, Mohd Syazwan Abdullah and Nazean Jomhari
702
Innovation, Knowledge and Incompetence: The Case of the Eurozone Macroeconomic Policies
Eduardo Tomé
712
Competence Management in Industrial Engineering Departments in the Czech Republic
David Tuček and Jaroslav Dlabač
722
Economic Evaluation of the Level of Knowledge Services in Selected OECD Countries
Zuzana Tučková and David Tuček
732
A Three-Dimensional Model of Identifying Barriers to Knowledge Management
Anna Ujwary-Gil
741
From KM Evaluation to Developing Evaluative Capability for Learning
Christine van Winkelen and Jane McKenzie
750
Profiling the Intellectual Capital of Italian Manufacturing SMEs: An Empirical Analysis
Chiara Verbano and Maria Crema
758
The Obligatory Passage Point: Abstracting the Meaning in Tacit Knowledge
John Walton
769
New Knowledge Creation by Collaborating GoalOriented Experts: Methodology and Models
Igor Zatsman and Pavel Buntman
776
Knowledge-Intensive Business Services (KIBS) and Their Role in the Knowledge-Based Economy
Malgorzata Zieba
785
Late Submission
793
Characteristics of Decision Problems In Innovation Process Planning
Magdalena Jurczyk – Bunkowska
795
Can Knowledge be Reliably Measured?
Rumniak Paweł
805
Insights into Knowledge Sharing in the Dubai Police Force
Dr Ibrahim Seba, Professor Jennifer Rowley and Dr Rachel Delbridge
814
PHD papers
823
Knowledge Management and Creative Thinking Framework Integrated in Training of Future Students
Andra Badea, Gabriela Prostean, Adrian Adam and Olivia Giuca
825
The Importance of Play in Overcoming Fears of Entrepreneurial Failure
Ramona Cantaragiu and Shahrazad Hadad
833
ii
The Role of Emotional Intelligence Efficiency in Multinational Financial Institutions
Elizabeth Lorena Croitor (Tcaciuc), Cristian Valentin Hapenciuc, Livia Elena Blanariu (Vranciu) and Daniela Mihaela Sandu (Neamtu)
840
Knowledge Sharing and Channel Choice: Effects of the new way of Working
Arjan de Kok, Bart Bellefroid and Remko Helms
849
Job Evaluation for Knowledge-Based Organizations
Paweł Fiedor
860 ,
Towards a Decision Approach for the Characterization of Potential
Sahar Ghrab, Ines Saad, Faiez Gargouri and Gilles Kassel
868
Knowledge Management Influence on Innovation: Theoretical Analysis of Organizational Factors
Ingrida Girniene
877
Developing Knowledge Management Capabilities in Social Enterprises: UK Experience
Maria Granados, Vlatka Hlupic, Elayne Coakes and Souad Mohamed
886
Research Regarding the Informational System (Information and Knowledge) Required for an Environmental Manager
Ionut Viorel Herghiligiu , Luminita Mihaela Lupu and Bogdan Budeanu
896
The Impact of Emotional Knowledge on key Aspects of the Economy
Andrei-Alexandru Morosan, Gabriela Arionesei, Paul-Panfil Ivan and CristianValentin Hapenciuc
905
Factors for Knowledge Sharing Behaviour to Develop Trust in Professional Organisations Environment
Salah Rana, Malcolm Crowe and Abel Usoro
914
Knowledge Sharing as a Problem of the Individual Nature of Knowledge
Vaclav Reznicek, Zdenek Smutny, Jaroslav Kalina and Alexander Galba
920
DataTalks: A Unified Knowledge Pool in SaaS and Mashup Systems
Sasha Mile Rudan, Dino Karabeg and Alf Martin Johansen
926
Data Mart With Lean Six Sigma Concept for Performance Level Assessment in Knowledge Management Framework
Jevgeni Sahno, Eduard Shevtshenko and Tatjana Karaulova
932
What is Your Organization’s IQ? – A Practical Tool to Gauge Enterprise Intelligence
Evren Satıcı and Özalp Vayvay
942
Intra-Organizational Cooperation and Knowledge Sharing: A Comparison of Slovak LIS University Departments
Peter Steranka
950
The Role of Individual Factor in Knowledge Sharing Behavior Among Profit Oriented Webloggers
Ruzleeta Zakaria, Nor Intan Saniah Sulaiman, Haslinda Ibrahim, Mohd Syazwan Abdullah, and Nerda Zura Zabidi
961
1
Work In Progress Paper
969
A Knowledge Sharing System Based On Structured And Unstructured Knowledge
iii
Leandro Ramos da Silva and Nizam Omar
971
Institutional Wiki: Evolving Public and Private Knowledge in MPMG Lilian Noronha Nassif and Daniel Silva Carnevalli Public Ministry of Minas Gerais, Belo Horizonte, Brazil liliannassif@mpmg.mp.br dcarnevalli@mpmg.mp.br Abstract: The innovation process must facilitate transformation of tacit knowledge into explicit knowledge inside organizations. This behaviour should make enterprises more competitive. Although wiki technology can be used for collaborative work and knowledge codification, several doubts exist how to implement it without disclosing private knowledge of an organization. This paper presents a case study that uses a wiki technology with well‐defined user profiles that can organize knowledge codification. Additionally, the implemented wiki guarantees secure access control to private information of the organization. The institutional wiki was initially deployed in the information technology area. For each sector of the institution, the model defines two distinct spaces: private and public. The designated space for private information is used for documentation of the sector, while the public information space is used for dissemination within the company. The wiki implementation brought several benefits, such as standardization of internal work and development of high performance teams. The case study enabled capturing private, technical, tacit knowledge and disseminating public information. Growth metrics measured the wiki success. The results demonstrate increasing rates of wiki page creation and revision that are related to the organizational knowledge evolution cycle. Keywords: knowledge creation; tacit knowledge capture and dissemination; Wiki; knowledge sharing; organizational knowledge
1. Introduction Knowledge management in organizations must constantly deal with information and keep the enterprise motivated to learn and innovate. This innovation process also holds a contemporary vision of people management where work features are focused on continuous learning, vision sharing, model multiplicity, and flexibility. The management of organizational knowledge combined with modern people management can be integrated and benefited from using Web 2.0 tools, such as the wiki. A wiki is a democratic tool where the user community is responsible for its contents generated in an open communication model and open knowledge creation. Some wiki benefits include teamwork, knowledge management, collaboration, and interaction. The institutional wiki differs from the traditional wiki because it is secure, closed, with restricted access, protected from vandalism, and appropriate for small groups. Recently, the use of wikis within companies has been adopted for various purposes and implemented in many ways. The Economist Intelligence Unit (2007) conducted a survey about Web 2.0 in enterprises. In the survey, CitiGroup© declared to use wikis internally for large project knowledge management involving specific terminology and standard processes. One obstacle to information sharing within companies is determining secure data access levels. Different sectors of the same company may need to safeguard procedural information specific to their areas. At the same time, they need to publish data relevant to other areas and disseminate generic information under their management. Another challenge is structuring the record of tacit knowledge as a retention mechanism of people who may quit the company. The objective of this paper is to resolve these challenges by presenting a case study of a structured, secure institutional wiki which can evolve private knowledge inside each enterprise sector and disseminate public information on the intranet. The paper is organized as follows: section 2 presents organizational knowledge concepts. Section 3 describes theories about wiki benefits used as a knowledge management tool. Section 4 presents related works. Section 5 shows our case model study implemented at the Public Ministry of Minas Gerais and the related results. Finally, section 6 concludes the paper.
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2. Concepts The definition of organizational knowledge has received different interpretations. Tsoukas & Vladimirou (2001) considered this concept the capability of organizational members to differentiate distinct processes for executing their work. In particular contexts, organizational knowledge depends on historically collective understandings. One of the dominant views of organizational knowledge was defined by Levitt & March (1996). They state that it exists in the form of routines resulting from an accumulation of past experiences that guide future behaviors. In this work, we are particularly interested in organizational knowledge creation and management, which allows developing new products and services, and improving important tasks. To achieve this aim, knowledge must be codified. Boisot (1995) classified different concepts of knowledge, shown in Table 1. The codified knowledge can be stored or recorded in writing without incurring significant information loss. The uncoded knowledge cannot be captured in writing or stored without losing essential parts of the experience it relates. Diffused knowledge is shared. Undiffused knowledge remains shared because it is difficult to express, or the person decided to keep it undisclosed. Public knowledge is codified and diffused. Common sense knowledge is acquired gradually throughout life via personal experiences and encounters with family, friends, and community members . Personal knowledge is born from experience inaccessible to others. Private knowledge is developed by a person or group who codifies it to give meaning to certain situations. Table 1: Knowledge typology Knowledge Undiffused Diffused Codified Private knowledge Public Knowledge Uncodified Personal Knowledge Common sense knowledge
Our approach considers these concepts from the organization point of view. The private knowledge in Table 1 was interpreted in Table 2 as undiffused private knowledge and diffused private knowledge. The codified undiffused private knowledge is specific to a group inside the organization and cannot be shared with all enterprise members. The codified diffused private knowledge must disseminate information to all enterprise members, thereby improving interconnections among areas. Table 2: Codified organization knowledge typology Knowledge Codified
Diffused
Undiffused Undiffused private knowledge
Diffused private knowledge
Private knowledge of a group in the organization (Organization
Organization private knowledge
Organization public knowledge
group private knowledge)
Table 3 shows what kind of information technology tool can help codified organizational knowledge. The private knowledge of a group in the organization and the organization private knowledge represent types of knowledge limited only to organization members and can be easily codified using wiki technology. The organization public knowledge is information that the enterprise wants to share with society and can be codified using web portals freely accessible on the internet. Table 3: Information technology tools to codify knowledge in the organization Knowledge Codified
Organizational knowledge type Private knowledge of a group in the organization Organization private knowledge Organization public knowledge
Information technology tool Wiki
Accessibility Intranet
Web Portal
Internet
Wiki is a collaborative technology, a dynamic set of web pages created by various authors. A wiki is a simplification of creating HyperText Markup Language(HTML) pages combined with a system that records every change, so that any page can be reverted to its earlier stages.
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Lilian Noronha Nassif and Daniel Silva Carnevalli Wiki technology was invented in 1995 to facilitate online collaboration and best practice programming projects, and in 2000 evolved into a tool to facilitate all kinds of collaboration. Wiki is a Hawaiian word meaning "quick", "fast". Currently, there are over 240 Wikis as described by CosmoCode (2012). Nonaka and Takeuch (1995) theorized that creating knowledge is the result of a cycle composed of the following four processes:
Externalization –tacit knowledge becomes explicit through using techniques such as metaphors and models
Combination – parts of incompatible explicit knowledge are combined and new knowledge is created
Internalization –explicit knowledge becomes tacit through the operational repletion of the new knowledge absorbed
Socialization ‐ tacit knowledge becomes tacit by sharing experiences with others
The case study presented here helped interpret this knowledge cycle associated with wiki metrics, as discussed in section 5.3.
3. Theories Wiki use in organizations can produce several benefits. Grace (2009) relates many advantages of this tool which help in knowledge development: 1) Ease of use, since many organizations had fallen victim to expensive and unusable knowledge management systems; 2) Central repository of information, since it accumulates inputs from users with different expertise; 3) Tracking and revision data , since any contribution can be revised quickly or reverted to previous versions; 4) Collaboration among organizations, since they can expand their business to different regions or develop collaborative projects with other regional organizations; 5) Solving information overload caused by email, since it involves exchanging numerous drafts as attachments creating problems of last version identification and unnecessary data storage of previous versions; and 6) Building a trusting culture, since being part of a group to achieve goals appeals to the social nature of humans. Wiki can also supply different needs inside organizations. Wagner (2004) divides these needs into two perspectives: user and creator. From the knowledge user perspective, knowledge tools should incorporate fast question answering, provide optimized search engines, and assure knowledge quality. From the knowledge creator perspective, wiki tools must disseminate knowledge quickly, combine knowledge from multiple experts, and provide fast database correction mechanisms.
4. Related work Different kinds of wiki implementations have been conducted inside organizations. McKelvie et al. (2007) presented MapaWiki as a central tool for collaboration within the company Mapa. This case highlighted wiki power by demonstrating an interactive collaborative environment that allowed knowledge capture, storage, and sharing. MapaWiki improved efficiency and allowed adoption of best practices. The choice to use a wiki was based on its simplicity and flexibility to store and retrieve information. One of the challenges in MapaWiki implementation was the uncertainty how to classify the articles. However, the experiment demonstrated wiki applicability even without a technological staff to support it. The work conducted by Holtzblatt et al. (2010) explored factors that negatively impacted the use of wikis inside enterprises. The authors related the reluctance in organizations to share specific information due to the perceived extra cost, information nature, and desire to only share finished content. The paper related that wiki success depends on the implementation of incentive structures, clear guidelines, and dynamic access control. Other interesting work was developed in NBC as registered by Biboo et al. (2007). This case showed a slow acceptance of wiki inside the organization. The wiki was initially implemented in the information technology (IT) area in 2005, but only after 2008 it became important for other areas in the enterprise. Our work has similarities with some aspects of these cases. It was initially implemented in an IT area, and it has been important in adopting best practices. However, the structure of our solution can overcome the difficulties founded in these related works, since it is based on a secure environment, and the categories are well‐defined.
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5. Case study – WikiMPMG This section presents a case study called WikiMPMG. The description includes technology customization, interface structure, and the roles adopted to organize knowledge.
5.1 Description The wiki used in our case study is called Dokuwiki and was developed by Gohr (2003). It is open source wiki software and uses simple syntax. It categorizes and organizes content into namespaces (directories) instead of a database. Content editing can modify small parts of a page. Dokuwiki generates an automatic table of contents according to the header page. Page layout customization can use more than one hundred template options. Our case study is called WikiMPMG, where MPMG means the Public Ministry of Minas Gerais. The WikiMPMG objectives include:
Being a collaborative environment of information sharing (with public access on the MPMG intranet)
Being a documentation repository for each private sector in the enterprise (with restricted access)
WikiMPMG was customized with security and control characteristics, such as:
Centralized user authentication, by using Lightweight Directory Access Protocol (LDAP): A unique login and password are used in the entire enterprise to access all systems, including WikiMPMG.
Encrypted Traffic, by using HyperText Transfer Protocol Secure (HTTPS): All data traffic between the user machine and the wiki machine is encrypted.
Control access to pages for predefined groups, by using Local Access Control List (ACL): Access to each WikiMPMG page can be defined by the wiki administrator.
5.1.1 WikiMPMG interface structure The WikiMPMG is based on its organogram. Each enterprise sector can create a space in the WikiMPMG. For the purpose of simplification, all kinds of subareas in the MPMG organogram are considered sectors. Only employees of a sector have access to the private space of that sector. All employees have permission to read pages in the public space of any sector, but they cannot write, exclude, or alter pages on the public space of another sector.
Figure 1: WikiMPMG initial page WikiMPMG content can be developed by all MPMG employees. Page creation requires no technical knowledge. A word search only returns results from pages where the user has reading access. Recent alterations in the wiki space are displayed with author and date. This facilitates identifying who is contributing to expand the knowledge. Past changes can be visualized and even recovered from a change history.
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Lilian Noronha Nassif and Daniel Silva Carnevalli The WikiMPMG has a sidebar with dynamic content based on the first header of each page. Figure 1 presents the WikiMPMG initial sidebar view to the wiki administrator. However, the initial sidebar view of regular users shows their sector private information and public information from all sectors. In Figure 1, the sidebar is formed by hierarchical indexes with the name of each MPMG sector inside the Information Technology Superintendence (STI), called Directory of Systems and Information (DISI), Directory of Networks and Databases (DRBD), and Directory of Support and Maintenance (DSMT). 5.1.2 Roles in the MPWiki One important aspect that must be considered when different volunteers create knowledge together is defining editing rules. Wikipedia is a wiki that has been working since 2001, and 7.8 million people access it daily. This dynamic environment uses rules to keep the content reliable. Based on the Wikipedia rules cited by Cordeiro (2007), we created WikiMPMG user profiles (Table 4), which are appropriate for any organizational wiki. Table 4: User profiles in WikiMPMG Profile
Description
Quantity
Reader
A user who visits the WikiMPMG to get information about other sectors. A user who writes in the private space of his sector.
All WikiMPMG users All sector employees N ( 1 per sector)
Editor Participant
Supereditor
Administrator
Sponsor Manager
The head of a sector who decides to host and develop content on WikiMPMG. He gives directions to the Supereditor about what information should be published and suggests the data structure of his sector. He gives the Administrator the list of people allowed to edit content on his WikiMPMG sector. A user indicated by the Participant for having facility with syntax formatting of content and ability to organize information. He is the focal point for information publishing. He makes textual corrections on pages published by editors of his sector. A person who facilitates content growth in the WikiMPMG and disseminates best editing practices. He contacts Supereditors to improve their structures. He installs new features in the wiki and trains editors. A person who initiates a new project using wiki tools. The WikiMPMG judge: he decides case conflicts, authorizes wiki developments, defines wiki usage policies, and articulates new WikiMPMG sector entries. He is the point of contact with sponsors.
Collaboration Power Low Medium High
N ( 1 per sector)
Medium
1
High
N (1 per Wiki Project) 1
Very High Very High
Different types of wikis can be created inside an enterprise. Table 5 presents some of the possibilities to develop wiki projects. According to this classification, the wiki of our case study is a WikiStructure. Table 5: Wiki possibilities for MPMG Wiki types WikiStructure WikiPeople WikiMedia WikiNews Wikipedia
Description Each sector of MPMG can share organizational private data inside the organization and sector private data inside the sector Space for publishing individual data Media files database where everyone can contribute Repository of free news that everyone can edit Encyclopedia
5.1.3 Private and public knowledge of a sector in WikiMPMG The WikiMPMG was initially developed in the IT area. Considering that enterprises normally have an IT sector, adapting the following example is possible for any organizational wiki.
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Lilian Noronha Nassif and Daniel Silva Carnevalli In the WikiMPMG, the IT sector considered the following information as private:
Network map (for example, network addresses and the network equipment list )
Procedures (for example, software installation and backup restoration)
Web servers certificate list
Routines (for example, the list of database jobs that run frequently)
Datacenter subsystems documentation (for example, how to use fire alert system, access video cameras, and calculate energy consumption)
In the WikiMPMG, the IT sector considered the following information as public:
What are information policy restrictions
What are information technology attributions
How to ask for information technology services
How to use services like email, instant messaging, and wifi networks
The IT sector knowledge codification brought the organization several benefits. Users easily found information about how to use or request services. At the same time, the private knowledge codification allowed the IT team to define service deployment standards. This codified tacit knowledge reduced the problem solving time. Technicians shared skills among themselves, contributing to establishing a high performance team.
5.2 Results WikiMPMG started in February, 2012 by using the Technology Information Superintendence (STI) department as a pilot project. The STI WikiMPMG started with 63 users, 65 daily page editions, and 117 different media. The pilot project lasted one year. During this period, few users were added, the number of daily page editions decreased to 12, and the number of media files increased to 342, as shown in Table 6. Table 6: WikiMPMG general data Daily page editions User count Media Count
February 2012 65 63 117
May 2012 19 65 212
March 2013 12 72 342
Dokuwiki manages page revisions. When a page is edited in DokuWiki, it creates a revision file with the old document. Old versions can be viewed by clicking the Old Revision button. The change logs are stored in files. These change logs allow tracking the full life cycle of a page such as creation, deletion, and reversion. These controls helped determine how much the wiki evolved in terms of new page creations and existing page revisions. Figure 3 shows that the number of new pages increased 222.35% (from 416 to 925 pages), and the number of revisions in existing pages increased 590.74% (from 1155 to 6823 revisions).
Figure 3: WikiMPMG page creation and revision
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Lilian Noronha Nassif and Daniel Silva Carnevalli Table 7: WikiMPMG size Page size (bytes) Change log size (bytes) Revision size (bytes)
February 2012 1,931,125 874,008 1,443,187
May 2012 3,947,235 1,934,560 6,405,792
March 2013 4,289,235 2,472,454 13,208,096
The information volume is another reference to analyze knowledge growth. Table 7 presents the total WikiMPMG bytes used to store pages, revisions, and log changes. The page storage space increased 222%, very similar to the page number growth rate, presented in Figure 3. This pattern is kept because all collaborators were oriented to follow the hints of good style to create wikis pages described in Dokuwiki (2012). Because of these hints, the wiki page size remained the same. For example, in February 2012, the average page size was 4642 bytes, and in March 2013, the average page size was 4637 bytes. Nevertheless, the page revision storage space increased 915% in one year, because wiki accumulates several old versions that can be retrieved anytime. According to Figure 3, the page revision rate was higher than the page creation rate. The existing knowledge was constantly revisited to include more details and evolve the same subject. The performance time of MPMG technical users improved since several IT infrastructure problems were solved in a shorter time. In this aspect, wiki worked as a solution repository to provide information quickly.
5.3 Discussion The WikiMPMG implementation confirms several aspects and benefits discussed in the theory section. The tool provided fast question answering by implementing an engine to locate tags inside the wiki. Independent of how the knowledge was organized, it was quickly identified. This characteristic also helped different sectors find others that were involved with the same issue. The case study confirmed the wiki benefits such as its ability to be the most important technical information repository in STI. Other benefits include its ease of operation as well as its ease of navigation by eliminating pdfs and email files. During the wiki pilot project, 9 technicians joined the group and quickly learned team procedures and understood the technical environment. This quick learning demonstrated the wiki ability to accelerate accumulated knowledge comprehension for new team members. The WikiMPMG statistical results provided an understanding of the knowledge life cycle. The high page revision rate revealed in Figure 3 expresses that the combination of different explicit knowledge can create new knowledge. The wiki page creation and revision correspond to the externalization and combination phases in the knowledge spiral, described in Figure 4.
Figure 4: Knowledge spiral and the correspondence with wiki page creation and revision
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6. Concluding comments The private knowledge of an organization should be codified to improve the development of new services and products. Nevertheless, this codified knowledge must be protected from unauthorized access. Associating new technologies to allow collaborative knowledge codification has recently evolved with wikis. However, how to implement it inside enterprises remains a significant challenge. This paper presented a case study where wiki technology is completely analyzed as a tool to help evolve private and public organizational knowledge. The organizational private knowledge should be diffused in the intranet, while the private knowledge of a group in the organization must be restricted to a sector. Developing an institutional wiki requires defining user profiles by organizing the knowledge codified by all employees. The case study presented some limitations. The pilot project was applied to the IT area because of its close connection to technology. The comprehension of the knowledge relevance could be more significant by analyzing institution sectors related to this theme. Recently, a new sector named Center of Studies and Functional Enhancement (CEAF) has started developing data in WikiMPMG. Its approval may popularize this technology inside the institution. Institutional wikis contain several open problems that require further research. One is defining more success metrics such as user adaptability to the wiki environment by considering the roles presented in this paper. Another challenge is defining a standard structure to capture knowledge inside each institutional sector, including processes, projects, procedures, and environment descriptions. WikiMPMG proved that codified knowledge can be organized, protected, and constantly evolved. The results obtained show knowledge creation and evolution inside a participative organization. The MPMG is prepared for new products and services innovation since it developed a motivation mechanism that can transform tacit information into codified knowledge.
Acknowledgements The authors thank Maria Claudia Samarane for her important contribution in the case study definition. The authors also thank the CEAF, in the name of João Paulo de Carvalho Gavidia and Alessandra de Souza Santos for their innovative vision of using wiki technology for functional enhancement.
References Bibbo, Danielle, Eric Sprehe, James Michelich, Young Eun Lee (2010). Employing Wiki as a Collaborative Information Repository. Proceedings of Thirty First International Conference on Information Systems, St. Louis. Boisot, M.H. (1995). ”Information Space: a Framework for Learning in Organizations, Institutions and Culture”, Londres, Routledge, 1995, p. 146. Choo, C.W. (1998). The Knowing Organization: How Organizations Use Information to Construct Meaning, Create Knowledge, and Make Decisions. New York: Oxford University Press. Cordeiro, Tiago (2007). “Dá para confiar na Wikipedia? ”. Superinteressante, São Paulo: Abril, 01 nov. 2007. Available at http://super.abril.com.br/superarquivo/2007/conteudo_545649.shtml. CosmoCode (2012).”WikiMatrix – Compare them all”. Available at www.wikimatrix.org . Visited in 24/feb/2012. Dokuwiki (2012). “Hints on good style”. Disponível em http://www.dokuwiki.org/tips:good_style. Visited in 24/feb/2012. Garza, Roberto (2012). “Wiki Use for Collaboration”. Available at http://www.slideshare.net/garzaroberto/wiki‐ powerpoint‐11505819. Visited in 23/feb/2012. Gohr, A. (2003). “Dokuwiki – It’s better when it’s simple”. Available at www.dokuwiki.org. Visited in 11/nov/2011. Grace, Tay Pei Lyn (2009).”Wikis as a knowledge management tool”. Journal of knowledge Management, vol 13. N.4, pp. 64‐74. Holtzblatt, L. J., Damianos, L. E., and Weiss, D. (2010). “Factors impeding Wiki use in the enterprise: a case study”. In Proceedings of CHI '10, ACM, 4661‐4676. Levitt, B., March, J.G. (1996) “Organizational learning”. In M.D. Cohen and L.S. Sproull (Eds) Organizational Learning. Thousand Oaks, CA: Sage. McKelvie, G. Dotsika, F. and Patrick, K. (2007). “Interactive business development, capturing business knowledge and practice: a case study”. The Learning Organization, Vol. 14 No. 5, pp.407‐22. Nonaka, I., & Takeuchi, H. (1995). The knowledge creating company: How Japanese companies create the dynamics of innovation. New York: Oxford University Press. The Economist Intelligence Unit (2007). “Web 2.0 goes corporate”. Serious business. Tsoukas, H. and Vladimirou, E. (2001), What is Organizational Knowledge?. Journal of Management Studies, 38: 973–993. Wagner, Christian (2004).”Wiki: A Technology for Conversational Knowledge Management and Group Collaboration”. Communications of the Association for Information Systems: Vol. 13, Article 19.
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Validation of the Scale of Knowledge Management Assessment in the Technical and Vocational Training Organization of Tehran Fattah Nazem, Hossein Chenari and Omalbanin Sadeghi Department of Education, Roudehen Branch, Islamic Azad University, Roudehen, Iran nazem@riau.ac.ir hossein_chenari@yahoo.com osadeghi82@yahoo.com
Abstract: The purpose of the present study is to validate a scale for measuring knowledge management in the Technical and Vocational Training Organization of Tehran. The population of the study included all the staff employed in Technical and Vocational Training Organization of Tehran. The research sample comprised 226 staff who randomly selected from the whole staff. The research instrument was the Sallis and Jones’s (2002) knowledge management questionnaire which consisted of 42 items with ten underlying constructs of vision and mission, strategy, organizational culture, intellectual capital, learning organization, leadership and management, teamwork and learning communities, sharing knowledge, knowledge creation and digital sophistication. The obtained Cronbach Alpha appeared to be 0.83. The results of factor analysis and principal components analysis, using a varimax rotation showed that the six underlying factors in knowledge management cover the factors of intellectual capital, digital sophistication, knowledge creation, learning organization, team work and learning communities, and vision and mission and the factor of intellectual capital had the highest contribution to the formation of the knowledge management in the Technical and Vocational Training Organization. Keywords: Knowledge management, factor analysis, technical and vocational training organization
1. Introduction and purpose of the study In the fast-paced world, the organizations are bound to invent and follow effective and essential tools, among them, knowledge management is a process which can help the organizations select, classify, share, transfer, and advance the important data and knowledge. Knowledge management is a specialty to name activities such as problem solving, active learning, and decision making (Tsang ho 2008). Knowledge management can make movements to a variety of organizational performance through enabling the organization to move toward more intelligent performance (Wiig 1999). Today’s business world is characterized by the growing complexity of global firms, information explosion and speed of decision making, and value and mobility of key employees (Mohrman et al. 2002). In economies, dominated by services, where people and information are primary drivers of business, these three trends demand that knowledge and its effective management be treated with particular attention. Organizational knowledge, therefore, has been promoted to the status of valuable strategic asset (Zack 1999). Moreover, several researchers have shown that knowledge management should be rooted in the firm’s strategy and that the level of linkage between the two determined the success of KM initiatives. Appropriate strategic alignment can in turn support the organization’s mission and strengthen its competitive position (Davenport & Prusak 1998; Zack 1999). Hansen et al. (1999) have shown how a company’s choice of knowledge management strategy is not arbitrary as it must be driven by the company’s competitive strategy, and they warn that knowledge management should not be isolated in a functional department. They have drawn attention to the importance of the corporate organization and the significance of corporate culture in supporting the chosen KM strategy (Hansen et al. 1999). Sallis & Jones (2002) offered a useful knowledge management self-assessment checklist with scoring elements such as:
Vision and mission: It refers to having vision as a knowledge-based organization and sharing it with the stakeholders and the mission as the knowledge creator and translating it into practical strategies.
Strategy: It refers to developing modeled scenarios and applying them in the management.
Organizational culture: It refers to the different dimensions of culture including the creating, centralizing, sharing, and recognizing organizational culture as a key competence.
Intellectual capital: It includes recognizing the value of intellectual assets and codifying its tacit knowledge.
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Learning organization: Under learning organization, organization should create continuous learning, define skills to create new knowledge, recognize EQ and its influences encourage creative thinking, and promote action learning both for individuals and teams.
Leadership and management: In leadership and management, organizations are required to have seniormanagement support, have knowledge leaders and managers with appropriate leadership styles, and develop strategies for promoting middle-managers.
Teamwork and learning communities: Under teamwork and learning communities, organization should encourage learning communities and knowledge teams, establish trust, and recognize the need for intellectual autonomy.
Sharing knowledge: It signifies that organizations ought to collect, record major organization events, and share new information, and understand competitors’ knowledge management system.
Knowledge creation: It requires the organizations to recognize new knowledge, those known as experts, and turn it into service.
Digital sophistication for the organization: In terms of digital sophistication, organizations are to develop technologies among its employees by clear technological architecture, enhancing its knowledge, and devising virtual collaborative systems and/or communities (pp.125-129).
Using the six factors in his research, Shirzad (2012) found leadership and management the most important factors in formation of knowledge management. However, Qara'ii (2013), in his study of knowledge management factors, considered vision and mission, strategy, organizational culture, intellectual capital, learning organization, leadership and management. In a research done by Brandt Jones (2009), considering knowledge creation as an important dimension in the study of knowledge management, it was concluded that there was a significant relationship between organizational culture and knowledge management. The study was based on the idea of Argote et al. (2003) which considered knowledge creation as the generation of new knowledge. On the other hand, Cho (2011) in his proposed model of factors has used factors including learning organization, sharing knowledge, knowledge creation, and organizational culture as the explanatory factors of the knowledge management. Moreover, according to the research carried out by Sajjadian (2013), dimensions such as knowledge creation, digital sophistication, and intellectual capital are the constituent dimensions of knowledge management, the factors which have been considered in this research too. In a research done by Kumar Singh (2008) the results showed that directive as well as supportive styles of leadership to be significantly and negatively associated with the art of knowledge management practices. It also depicts that consulting and delegating styles of leadership are positively and significantly related with managing knowledge in a software organization. Finally, only the delegating mode of leadership behaviors was found to be significant in predicting creation as well as management of knowledge for competitive advantage in software firms in India. In addition, Feizi and Qeitari (2011), in their study, state that knowledge management has gained importance as the major cause of growth and development of countries in recent years, because of dissemination of information, development of new technology, professional and technical growth, increased job competition, conversion of knowledge to capital, and importance of human development. They found a significant relationship between knowledge management and learning organization. In his research, Mayfield (2008) found out that knowledge management is a business problem within the domain of information systems and management. The risks associated with losing criticalknowledge can be measured using metrics on employee’s retention, recruitment, productivity, training and benchmarking. Certain enablers must be in place in order to engage people, encourage cooperation, create a knowledge-sharing culture, and, ultimately change behavior. As the purpose of the study is to validate the scale of knowledge management assessment in the Technical and Vocational Training Organization of Tehran, the following research questions are posed.
2. Research questions Regarding the questionnaire of knowledge management, what are the factors which construct knowledge management in Technical and Vocational Training Organization?
Which of these factors has more contribution in forming knowledge management?
What are the items included in each factor which constructs a part of the questionnaire of knowledge management?
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3. Method of the study The researchers have used factor analysis, and principal components analysis, using a varimax rotation in order to identify the underlying constructs of knowledge management. The population of the study included all the staff employed in Technical and Vocational Training Organization z 2σ 2 n= d2 formula was used. Regarding the of Tehran. In order to estimate the least volume of sample, minimum research sample required for the staff’s group, 226 individuals were randomly selected, using simple random sampling method, and the same number of questionnaires of knowledge management was distributed among them. The research instruments was as follows: Sallis and Jones’s (2002) knowledge management. The questionnaire consisted of 42 items with ten underlying constructs of vision and mission, strategy, organizational culture, intellectual capital, learning organization, leadership and management, teamwork and learning communities, sharing knowledge, knowledge creation and digital sophistication. The obtained Cronbach Alpha appeared to be 0.83.
4. Findings of the study To answer the research questions, factor analysis procedure was applied. The first step in factor analysis process which is also its first assumption is checking missing data. In this step, all the subjects were involved and none of them was eliminated from statistical analysis. Hence, in this research no item has been eliminated except eleven subjects. And the given situation shows that there is no need to omit some of the items and it is possible to follow the process of Factor Analysis while having all the items. The second factor analysis assumption denotes enough sample size. In this research, Kaiser- Meyer- Olkin (KMO) equals 0.83. Consequently, the sample size is sufficient. The third factor analysis assumption is normality of multi-variation distribution known as sphericity. As the Approximate Chi Square equaled 4335.779 with the 861 degrees of freedom, it can be stated that the amount of the Approximate Chi Square is statistically significant and the given statistics is significant at least at the 0.999 level of confidence ( α = 0.001). According to component matrix of items, we can determine both the specific factor of each item and its position in the related factor based on loading factor. After studying the table of component matrix precisely, the researchers used Rotation Method so that loading factor of each item can be determined stressing on recognition of each item in one of the 6 factors. Because, the researchers have followed Exploratory Factor Analysis and used Principal Component Methods from Extraction of Factors, Varimax Method was applied (table 1). According to Varimax, the researchers were able to determine both the factor to which the item belongs after rotation and the position of each item in related factor with reference to loading factor. The related table shows in which factor each item has been located after the rotation. For instance, Items 20, 36, 33, 31, 30, 29, and 28 have been located in the first factor (intellectual capital). To fulfill the purposes of the study, determining the indexes of intellectual capital, and its components, the underlying items, and the index with the highest contribution, eventually, 6 factors have been extracted from rotation of factor analysis. In fact, Knowledge Management consists of 6 factors respectively as follows: learning organization knowledge creation, digital sophistication, intellectual capital, vision and mission, teamwork and learning communities. The table also indicates that intellectual capital has the highest level of contribution to the formation of Knowledge Management Assessment in the Technical and Vocational Training Organization. The reason is that, as the first column, that is, intellectual capital factor shows, 7 items with more than 0.5 have been located in this column. The reason behind the selection of factors in "factor analysis" is that in each factor two or more questions have been included and the amount of load of the given factor is higher than 0.50. In this study, the questionnaire of knowledge management consisted of ten factors including vision and mission, strategy, organizational culture, intellectual capital, learning organization, leadership and management, teamwork and learning communities, sharing knowledge, knowledge creation as well as digital sophistication. After the final administration of factor analysis, and tabulation of the obtained data in the form of Rotated Component Matrix (Table 1), all factors were selected except leadership and management, sharing knowledge, strategy and organizational culture. The criterion for selected factors was that each factor had to have two items whose load factors were higher than 0.50. The omitted factors had less than two items with load factor of 0.50.
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Fattah Nazem, Hossein Chenari and Omalbanin Sadeghi Table 1: Rotated Component Matrix X X01 X02 X03 X04 X05 X06 X07 X08 X09 X10 X11 X12 X13 X14 X15 X16 X17 X18 X19 X20 X21 X22 X23 X24 X25 X26 X27 X28 X29 X30 X31 X32 X33 X34 X35 X36 X37 X38 X39 X40 X41 X42
intellectual capital
digital sophistication
knowledge creation
learning organization
teamwork and learning communities
vision and mission 0.513
0.550 0.649
0.594 0.637 0.616 0.539 0.554 0.576 0.578
0.632 0.532 0.505 0.593 0.630 0.723 0.504 0.603 0.615 0.733 0.660 0.588 0.677 0.611 0.535
0.577 0.708 0.694 0.639 0.596
Hence, emphasizing at the six-fold factors of knowledge management, items related to each factor have been summarized in table 2 respectively.
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Fattah Nazem, Hossein Chenari and Omalbanin Sadeghi Table 2: Results of Factor Analysis of knowledge management Construct Factors
Index
Items
First Factor
intellectual capital
20,36,33,31,30,29,28
Second Factor
digital sophistication
26,25,22,19,18
Third Factor
knowledge creation
15,14,13,4,3
Four Factor
learning organization teamwork and learning communities vision and mission
37,38,39,41
Five Factor Six Factor
10,9,8 24,21,2,32
5. Discussion and conclusions In the present study, a questionnaire for assessing knowledge management (Sallis and Jones 2002) was used, which has ten dimensions: vision and mission, strategy, organizational culture, intellectual capital, learning organization, leadership and management, teamwork and learning communities, sharing knowledge, knowledge creation and digital sophistication. The results of the present study of the factor analysis method showed that knowledge management in Technical and Vocational Organization of Iran composes of six factors including Learning organization, knowledge creation, digital sophistication, intellectual capital, vision and mission, teamwork and learning communities. The findings of this study is in line with studies done by Shirzad (2012), Sajadian (2013), Qaraie (2013), Brandt Jones, (2009), Cho ( 2011), Feizi and Qeitari (2011), Mayfield (2008), and Kumar Singh (2008). Leadership and management are the most important factors of knowledge management in the work of Shirzad (2012); and in Brandt Jones’ (2009) study, knowledge creation is an important dimension in knowledge management. The results of the study were justified by Sajjadian (2013) who considered knowledge creation, digital sophistication, and intellectual capital as the knowledge management dimensions; Kumar Singh (2008) who showed that directive and supportive styles of leadership have a negative relationship, but consulting and delegating styles of leadership positively relate with knowledge management. He also considered delegating mode of leadership behaviors as the significant factor in predicting creation and management of knowledge. In their study, Feizi and Qeitari (2011) declared that knowledge management has gained importance as a major cause of growth and development of countries. They found that there is a significant relationship between knowledge management and learning organization. Accordingly, In order to strengthen knowledge management in the Technical and Vocational Organization of Iran, these statements are suggested:
Intellectual capital should be strengthened.
Employees should be provided with internet accessibility for research purposes.
Organizational knowledge should be shared.
Technical and Vocational Organizations should turn into learning organizations.
With regard to the most impact of the first factor, intellectual capital, on shaping knowledge management in the technical and vocational organizations it is recommended that:
Individual’s tacit knowledge turns out to the explicit knowledge and shares among the employees of the organization.
Employees’ intellectual capital should be valued.
Intellectual capital of the organization should be recognized and taken into consideration in order to be positively exploited.
In conclusion, the newly-proposed results in this research can be effectively employed to enhance the knowledge management in similar organizations.
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Acknowledgements The authors want to extend a heart-felt gratitude to the members of Technical and Vocational Training Organization for their commitment and efficient research assistance. They are truly appreciated as their partnership was vital to carry out this research.
References Argote, L., McEvily, B. and Reagans, R. (2003) ‘‘Managing Knowledge in Organizations: an Integrative Framework and Emerging Themes’’, Management Science, Vol. 49 No. 4, pp 571-82. Brandt Jones, M. (2009) Organizational Culture and Knowledge Management: an Empirical Investigation of U.S. Manufacturing Firms, [Ph. D. Dissertation], Nova Southeastern University, Florida, United States. Cho,T. (2011) Knowledge Management Capabilities and Organizational Performance: an Investigation into the Effects of Knowledge Infrastructure and Processes on Organizational Performance, [Ph. D. Dissertation], University of Illinois at Urbana-Champaign, Chicago. Davenport, T. H. & Prusak, L. (1998) Working Knowledge: How Organizations Manage What They Know, Harvard Business School Press, Boston. Feizi, T., & Qeitary, L. (2011) The Relationship of Knowledge Management and its Components with the Dimensions of the Learning Organization in the Science [M.SC, Dissertation ] Azad University of Roudehen, Iran. Hansen, M. T., Nohria, N. and Tierney, T. (1999) ‘‘What’s Your Strategy for Managing Knowledge?’’, Harvard Business Review, March/April, pp 106-15. Kumar Singh, S. (2008) “Role of Leadership in Knowledge Management: a Study”. Journal of knowledge management, Vol. 12, No. 4, pp 3-15. Mayfield, R. D. ( 2008) Organizational Culture and Knowledge Management in the Electric Power Generation Industry , [Ph. D. Dissertation] , University of Phoenix, Arizona. Mohrman, S. A., Finegold, D.and Klein, J. A. (2002) “Designing the Knowledge Enterprise: Beyond Programs and Tools”, Organizational Dynamics, Vol. 31, No. 2,, pp134-150. Qaraie, N. (2013) Examination of the Relationship among Knowledge Management, Intellectual Capital, and Efficiency in the Shahid Beheshti University [M.S. Dissertation] Azad University of Roudehen, Iran. Sallis, E. & Jones, G. (2002) Knowledge Management in Education. Kogan Page, London. Sajjadian, M. (2013) The Relationship among Social Capital, Knowledge Management, and Organizational Intelligence in Tehran’s Offices of Education [M.S. Dissertation] Azad University of Roudehen. Iran. Shirzad, M. (2012) Examination of the Relationship among Philosophic Mentality of Managers, Quality of Employees’ Working Life, and Knowledge Management in the Health Administration in Eastern Tehran [M.S, Dissertation ] Azad University of Roudehen, Iran. Wiig, K. M. (1999) “Comprehensive Knowledge Management”, [online], http://www.ecz.edo/decenter/ok. Zack, M. (1999) “Developing a Knowledge Strategy”. California Management Review, Vol. 41, No. 3, pp125-146.
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Organisational Knowledge and Human Capital: A Conceptualisation for the Non‐Profit Sector Olimpia Neagu Vasile Goldiș Western University, Arad, Romania olimpian2005@yahoo.com Abstract. The non‐profit sector encompasses organisations aiming to create social value for society, not to create profit or benefits for their members. Many studies have investigated learning and human capital in knowledge‐based organisations in the business sector, however, the non‐profit sector has been neglected. NGOs have to continuously improve their performance; they are knowledge‐intensive organisations. The demand for timely and high quality services, tailored to the beneficiaries' needs, requires NGOs to adopt new internal processes and managerial paradigms. All resources of NGOs should be managed with efficiency and effectiveness, the most important being their knowledge and human capital. This paper tries to conceptualise the interaction between organisational knowledge and human capital within NGOs in achieving excellence, by describing the mechanism through which innovation, human capital and knowledge, generate social value. The paper aims to find answers to the following research questions: what are the typology and the specific features of knowledge within a NGO, what is the role of human capital in knowledge management, what factors influence the adoption of knowledge management, and how does innovation ‐ linked to knowledge management ‐ generate value for society? The findings are based on two study cases of Romanian NGOs. Keywords: knowledge management, human capital, innovation, non‐profit organisation
1. Introduction Non‐governmental organisations (NGOs) consist of people unified around a group of common values, ideas and desires, committed to contribute to the achievement of the organisational goals. NGOs create social value for the community by using organisational resources (knowledge, human and financial resources), concerned with the efficiency and effectiveness of their management. Many studies have investigated learning and human capital in knowledge‐based organisations in the business sector, however, the non‐profit sector has been neglected. NGOs are knowledge‐intensive organisations. Their knowledge capital is heterogeneous, widespread, rarely formalised and unstable (due to volunteer workers) (Lettieri et al., 2004). NGOs must continuously improve their performance and offer added value for their beneficiaries. The demand for timely and high quality services, tailored to the beneficiaries' needs, requires NGOs to adopt new internal processes and managerial paradigms, according to the changes in society and its components. Knowledge, as a resource, emerged as a result of the progression to a knowledge‐based society, and requires a new conceptual vision regarding the relationship between various forms of organisational capital, their combination, and the result: the creation of social value. In this context the following research questions were formalised:
What are the key typologies of knowledge within a NGO?
What role does human capital play in knowledge management?
What factors affect the approach the NGO adopts toward knowledge management?
How does innovation generate social value in NGOs?
The present paper intends to find answers to these questions by analysing two Romanian NGOs, and to conceptualise the interaction between organisational knowledge and human capital within NGOs, by describing a mechanism through which innovation, human capital and knowledge could generate social value. The paper is organised as follows. After a short review of knowledge management in NGOs, the situation of NGOs in Romania is presented, then, findings related to the two case studies are described. The final section is dedicated to conclusions and further directions of research.
2. Knowledge management in NGOs: the state of the art Organisational knowledge
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Olimpia Neagu Generally, organisational knowledge includes the extent to which the knowledge is spread within the organisation. Hatch (2010) defines it as: "when group knowledge from several subunits or groups is combined and used to create new knowledge, the resulting tacit and explicit knowledge can be called organizational knowledge." Others present a broader perspective: "individual knowledge, shared knowledge, and objectified knowledge are different aspects or views of organizational knowledge" (Ekinge & Lennartsson, 2000). Types of knowledge in NGOs The design or implementation of knowledge management systems within NGOs has barely been studied and little effort has been made in this area. We can mention Italian authors such as: Lettieri, Borga and Savoldelli (2004) who investigated the role that knowledge management plays in achieving excellence in non‐profit organisations. They identified six types of knowledge within non‐profit organisations: accounting/administrative, managerial/organisational, teaching/training, fundraising, public relations, marketing, operational and miscellaneous. On the other hand, the current popular conception of organisational knowledge makes a distinction between tacit and explicit knowledge. This derives from the philosopher Gilbert Ryle (1949) who made a distinction between ‘knowing what’ (explicit) and ‘knowing how’ (tacit). In the traditional perception of the role of knowledge, tacit knowledge is often emphasised as being key for success and the creation of new values (Bergeron, 2003). A reason for this view is that explicit knowledge (e.g. market research results, plans, patents etc.) is useful only in combination with an individual's tacit knowledge. Some assessments say this constitutes for up to 80% of all knowledge in a company (O'Dell & Grayson, 1998). Knowledge can also vary in its degree of articulation; simple knowledge can be explicit, implicit or tacit. Most knowledge, however, is not simple but complex, and contains a combination of explicit, implicit and tacit components (Newman & Conrad, 1999). Explicit knowledge includes information that has been documented or can be shared with someone. This is knowledge that has been articulated in such a way that it can be directly and completely transferred from one person to another. Implicit knowledge is knowledge whose meaning is not explicitly captured, but can be inferred; in effect, the codification process is incomplete. Implicit must rely on previously retained knowledge. Tacit knowledge refers to personal knowledge in one’s head ‐ knowing how to do something based on experience. It includes judgment, insights, experience, know‐how, as well as personal beliefs and values. As Polany (1966) stated “knowing more than we can say”. There are six types of tacit knowledge (Knight & Howes, 2003): know‐how, know‐who, know‐why, know‐when, know‐where, know‐that that are relevant for the activities of NGOs. Knowledge management in NGOs In the case of NGOs, knowledge management is the explicit and systematic management of intellectual capital and organisational knowledge, as well as the associated processes of creating, gathering, organising, retrieving, leveraging, and using intellectual capital for the purposes of improving organisations and the people in them. As noted by Groff and Jones (2003), turning tacit knowledge into explicit knowledge is one of the key functions of a knowledge management strategy. Within a NGO, this function is central and critical for the effectiveness of knowledge management. The knowledge management elements within a NGO are:
‐Collaboration and the ability to connect individuals or groups. NGO members should be encouraged to gather data through various resources, media, socialisation with peers and colleagues, and share this information across the organisation. For example: workshops in which teams and individual members are encouraged to share ideas, as well as strategic reviews or planning forums, internal benchmarking reports, symposiums that bring together internal groups, and external experts that share ideas and learn from one another.
Nature of expertise and access to experts. NGOs have to encourage team mixing within various projects to facilitate the transfer of knowledge, or share new approaches and perceptions across boundaries, by having people or teams possessing knowledge, work with other groups, branches or member organisations.
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Communities of practice enable an organisation's members to access specific groups to post issues, solve problems, or discuss key topics. A community of practice generally means a group of people who share a common interest in an area of competence and are willing to share the experiences of their practice. Many organisations encourage people to gather data that might benefit the organisation (Newman & Conrad, 1999). Examples of communities of practice for NGOs: web or chat boards, professional groups of social workers, human rights workers, and networks of specialists in environment protection and child protection etc.
Knowledge networking connects groups of people with systems and applications. For knowledge management to work within a NGO, data and information must be captured in a system or central repository, through a system accessible for staff, volunteers and members, as information must be provided to the right people, in the right format, and at the right time.
Access of all members to a knowledge database (of reports, facts, experiences, legislative data base). Organisations need to determine what data to capture, how to capture it, and the format for providing information to members to analyse and use.
Organisations encourage sharing information by using collaboration, mentoring and socialisation to inform people. This can be done through internal networking and forums, thematic e‐groups, e‐mailing lists and internal publications.
Human capital and the knowledge management in NGOs Human capital is formed by the aptitudes, competences, experiences and skills of the internal members of an organisation (Bontis, 1999; Bontis et al., 2002). Human resources are crucial in creating human capital because organisations cannot otherwise create knowledge (Argyris & Schön, 1978). As tacit knowledge is the content of the human capital of individuals working in an organisation, we can conclude that human capital plays a key role in the frame of an organisation's knowledge. In NGOs, human capital can be enhanced through learning in two forms: training as formal learning and informal learning. Comprehensive training could improve the knowledge, skills and competencies of staff and volunteers, to become more efficient and effective in delivering high quality services to beneficiaries. In NGOs, knowledge management functions have close a relationship with the collaboration features typically emerging in informal learning contexts, as in the attempt to maintain a reciprocal engagement in the achievement of a common goal, the members aim at acquiring significant learning. Nowadays, in the context of NGOs, informal learning and knowledge management can derive a significant boost from the attitudes and practices of social networking. NGOs are based on: informal learning, a natural practice in the daily behaviour of people (staff, volunteers, board members); and spontaneous relations, (interactions and conversation contribute to the creation and transmission of knowledge and support informal learning practices). Several NGOs work as a social network, unified by the personal needs and common goals of its members, interaction policies, protocols and rules, and favour the growth of a sense of belonging to a community (an organisation). Due to the fact that NGOs use e‐means for communication, these social networks are based, in a great part, on informal e‐learning. In this view, the organisational environment has to be a relational one, to support the creation of, and maintain, the following processes: generate and support motivation (the positive interactions among people, fun and pleasure that individuals have in their networking activities); organisational culture (the sense of belonging, membership, mutual understanding and social grounding); social climate (to increase self‐esteem and foster motivation for a wider visibility, to value the individual in the organisation and allow each member of the organisation to be valued by others, the agreement of respect and trust, the self‐perception of usefulness, the significance of one’s contribution to group activities, and to be considered a useful contributor to the organisation's goals).
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Olimpia Neagu Innovation in NGOs The Oslo Manual for measuring innovation (OECD, 2005) defines four types of innovation: product innovation, process innovation, marketing innovation and organisational innovation. Innovation is built on a foundation of creativity, resulting in the creation of new knowledge and learning within the organisation. The learning gained can be a valuable asset for the organisation. The scope of innovation exists primarily within the realm of the individual and the collective knowledge of the organisation. For a definition of innovation within NGOs, we have to take into consideration the main factors or situations which could generate or enable innovation, such as:
Unmet beneficiary needs and concerns;
Problem solving (lack of resources, limited resources, organisational conflicts, the existing mode of operation needs to be changed, a new approach for an old problem);
Creativity entails a level of originality and novelty that is essential for innovation;
Philosophy of services provided (social business model or the way the services are offered to the beneficiaries);
Vision of managers and leaders;
Organisational culture and climate.
Innovation in the context of NGOs can be defined as the process of generating new ideas, practices and methods aiming to offer an improved social value to the beneficiaries. The main driver of innovation for NGOs is the external environment; specifically, the target groups and beneficiaries. The shifts in the political, economic, cultural and technological components of the external environment, within the target groups needs and concerns, force NGOs to innovate their services, processes, methods or technologies.
3. Romanian NGOs In Romania, non‐governmental organisations are juridical persons consisting of individuals (physical persons) and juridical persons, which aim to carry out general or special interest activities with a non‐profit purpose. In 2010, of 62,680 non‐profit organisations registered in the National Register at the Ministry of Justice, only 21,319 were active in terms of financial activities (submission of yearly financial reports to the Ministry of Finance). In recent years, the main characteristics of the active non‐profit sector in Romania are:
Significant financial constraints, heightened by the financial crisis, through the non‐philanthropic behaviour of the population and firms;
The focus on the short‐term, in terms of resource allocation, due to limited financial resources and unstable human resources (fluctuation of volunteers);
The presence of voluntary resources, with a heterogeneous experience and knowledge, and non continuous presence;
The need to achieve a consensus on strategy due to the associative nature of organisations;
A general concern related to employed staff and volunteers' training. Many Romanian NGOs are accredited as training providers for NGO personnel;
A general concern to be more flexible and effective in the relationship with their beneficiaries and stakeholders, in order to cover theirs needs and problems;
The capacity to build coalitions with beneficiaries, stakeholders and the community, around specific social needs/problems (lobby and advocacy that aims to solve problems or bring an improvement);
The Internet is generally used as means of promotion and communication (websites, e‐mail, e‐forums, e‐ newsletters);
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A general concern related to public image. NGOs use various forms to be visible in the public space by building/entering into various coalitions and networks;
An ambition to be a real partner of government and public authorities (central and local).
4. Findings from organisational profiles The two Romanian non‐profit organisations that were examined are: the National Association of Citizens Advice Bureaux (NACAB) and The League for Defence of Human Rights (LDHR). Both have a national structure, the main difference between them being the type of members' affiliation. The NACAB has independent organisations as members, whereas the LDHR is structured as an association with branches at local level, with limited independence (see the Appendix). The set of four key questions listed in the first section provides a useful framework for the investigation of knowledge and human capital interactions, in envisaging a mechanism for social value generated through human capital, innovation and knowledge.
4.1 Key typologies of knowledge within a non‐profit organisation Taking into consideration the profiles of the two organisations, the following types of knowledge were identified and are presented in Table 1 and Table 2. Table 1: Types of knowledge and their description Knowledge category Administrative Managerial Learning Service philosophy Partnership Marketing Other
Short description knowledge required to manage a NGO from legal‐economic perspective knowledge related to manage human resources and organisational processes knowledge related to learning process in the organisation: informal and formal learning (to train and update the skills of employees and volunteers) knowledge regarding the philosophy of services offered to the beneficiaries, tailored to their needs and problems knowledge related to finding and maintaining valuable and strategic partners knowledge related to fund raising, public relations, public image, public communication knowledge acquired by external sources (i.e. volunteers, beneficiaries)
Table 2: Types of knowledge and their content Type know‐how
know‐who
know‐why
know‐when know‐where know‐that
Content ‐proceedings for admittance in the organisation, written as formal documents; ‐proceedings for internal administrative processes (accountant and financial, internal rules of organisation and functioning); ‐handbook of CAB; ‐handbook of the local branch; ‐training support for CAB's adviser. ‐profile of staff and volunteers in member organisations/branches; ‐database with stakeholders (local public authorities and institutions, government agencies); ‐database with mass media. ‐ the organisation's values, principles, mission and vision are stated in all official documents (annual reports, press release, websites), are explained and are discussed in general assembly, forums, workshops. ‐event calendar inserted in the organisation's website; ‐news, as a section in the organisation's website. ‐search engine for navigation, knowledge map, logical content categorisation, intuitive categorisation. ‐virtual communities: internal network, intranet subpages, internal publications.
4.2 Factors that affect the adoption of knowledge management The main factors affecting the adoption of knowledge management in NGOs are:
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NACAB x x x x
LDHR x x x
x x x x
x x x x
‐ x ‐
‐ x ‐
x
x (partial )
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Position of NGO in the life cycle: organisations in the consolidated phase are more likely to adopt knowledge management;
Set of values and organisational culture: strong, shared common values and a strong culture are favourable for knowledge diffusion;
Juridical status and context of activities: in an organisation where the members are independent affiliates, knowledge flows more naturally than in one where activities are imposed by the centre;
Hierarchical networks structure and level of autonomy: a flexible structure and a greater degree of member’s autonomy facilitate the adoption of knowledge management;
Background of governing board members and operational staff average age: a social science background and a younger age are an advantage for the adoption of a knowledge management approach.
4.3 The role of human capital in knowledge management within NGOs We agree with researchers who state that organisational commitment, knowledge‐centred culture and training are considered as critical factors for successful knowledge management practices in social economy organisations (Cardoso et al., 2012). Therefore, we consider organisational culture as a process enabling the improvement of organisational human capital. The components of human capital consist, not only of the knowledge of individuals, their skills and competencies, their innate talents or health status, but also their social skills. As consequence, an organisation’s human capital can be decisively influenced by organisational culture. Communication, interactions, opinion and information sharing, through internal social events or internal networking, that aim to strengthen the organisational culture, play a role in enhancing an organisation’s human capital. The organisational human capital, as tacit knowledge or knowledge embedded in people, can be enhanced through two forms of learning: informal and formal (training). NGOs are concerned with the processes of organisational learning. They currently have internal initiatives or opportunities for interconnecting members and team working, to make the organisation’s past and present experiences available for all. They also have an internal system of training, ensuring specific skills for service delivery. Figure 1 reflects the role of human capital in the processes of knowledge management within NGOs. Organisational learning and organisational culture influence the accumulation and development of human capital, as well as the knowledge processes (knowledge creation, retention, sharing and utilisation) within NGOs. An organisation that learns from its past and present experiences, with a consolidated culture, has great internal cohesion and members who are dedicated to its mission, is likely to develop and accumulate human capital of a higher quality. Through its specific forms (formal and informal), organisational learning facilitates creation, diffusion and utilisation of knowledge within NGOs. In turn, all knowledge processes are of a learning nature. Therefore, they influence the process of organisational learning.
Knowledge management Processes: knowledge creation, retention, sharing, utilisation Explicit knowledge Implicit knowledge Human capital
Organisational culture
Learning
Figure 1: Position of human capital in the knowledge management Our conceptual considerations on human capital and knowledge, in the creation of social value, are based on the conceptual framework developed by Perez and Ordonez de Pablos (2003), for the classification of different forms of human capital that may exist in an organisation. In the view of these authors, there are four forms of
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Olimpia Neagu human capital in an organisation, as uniqueness and strategic value in the creation of social value: idiosyncratic, ancillary, core and compulsory human capital (Figure 2).
uniqueness
Idiosyncratic knowledge (1)
Core knowledge (3)
Ancillary knowledge (2)
Compulsory knowledge (4)
strategic value
Source: adapted from Perez and Ordonez de Pablos (2003) Figure 2: Forms of human capital within NGOs Quadrant 1 idiosyncratic knowledge represents human capital with strong uniqueness and low strategic value for social value creation. This is a specific NGO resource represented by volunteers. NGOs are currently working with volunteers, not because it represents a free resource, but because they are committed to share their expertise and competencies with other members, or are engaged to contribute to the organisational goals and mission. In order to increase the value of this form of human capital, it has to be linked to other forms of human capital, as well as with relational and organisational capital. Quadrant 2 represents the ancillary human capital, meaning staff knowledge that is neither useful for creating social value for beneficiaries, nor is it particularly specific to the NGO. It comprises non‐skilled or low skilled staff: receptionists, temporary workers for mailing, cleaning, maintenance, and other administrative tasks. Quadrant 3 comprises core knowledge, meaning personnel possessing NGO specific knowledge and competencies: project manager, network coordinators, fund‐raisers, and public policy experts. NGOs are interested to over‐invest in this type of human capital. NGOs are currently focused on the internal development of skills, and organising comprehensive training to develop unique or NGO specific skills (i.e. CAB advisers and managers). Quadrant 4 reflects compulsory knowledge, that is not specific to an NGO or another, but it is critical for achieving the organisation’s objectives. In this category are: leaders, managers, senior managers, and board members. Due to the transferability of this form of capital, the practice and human capital theory suggests that organisations are not likely to invest in this kind of human capital.
4.4 Innovation in Romanian NGOs There are several ways to enable the process of innovation within NGOs. The multiple forms of innovation (service, process, marketing, organisational) are likely to be used due to the concerns of NGOs for internal cohesion, improved communication and a strengthened organisational culture, and learning. Tables 3 and 4 describe situations leading to innovation, and the types of knowledge in relation to innovation within NGOs. The main impact of innovation within NGOs is reflected on knowledge processes (creation of new knowledge and facilitation of sharing/transfer, retention and utilisation). However, specifically, innovation is viewed as resulting in positive changes within NGOs, because it must add value for beneficiaries. Table 3: Situations leading to innovation and means of enabling innovation in NGOs Identified situations/problems/ opportunities identified unmet beneficiaries needs
Means of innovation
Possible impact
a new service an improved service
an added value offered
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Means of innovation
Possible impact
creative fund raising activities donors searching new procedures and policies for volunteers recruitment new approach and methodology of training activities new approach in PR
diversification of financial resources diversification of human resources professionalised human resources a clear public image, positioning in the public perception
Table 4: Knowledge and innovation in NGO Types ok knowledge tacit knowledge Human capital ‐ enhanced by informal and formal learning (training) explicit knowledge
Enhanced by innovation in: ‐means of informal learning ‐means and methods of strengthening the organisational culture ‐methods of training ‐human resources procedures and policies (recruitment, selection, motivation) ‐organisational procedures ‐organisational processes ‐philosophy of services ‐standardised documents
4.5 Mechanism through which human capital, innovation and knowledge generate social value Figure 3 illustrates the mechanism of creating social value through human capital, innovation and knowledge. The main channels through which innovation is created are: services (as philosophy or as mode of delivery); procedures (for admitting new members, human resources related procedures and financial procedures); processes (training, organisational processes); and methods (to facilitate social interactions, to communicate within or outside the organisation). These channels are activated by people (staff, managers, volunteers, board members), and their competencies (their human capital). The innovation may have an ending if these groups are able to activate and keep open these channels. Social value can be created through the knowledge management processes, with organisational resources (human, financial), according to the vision of the leaders and organisational strategy. Social value
Resources
Innovation
Individual commitment
Beneficiary
Knowledge management processes Knowledge Human capital
Vision/Strategy
Organisational learning Organisational culture
Figure 3: Mechanism of social value creation within NGOs
5. Conclusions and further directions of research This paper offers a view of interconnections between human capital, knowledge and innovation within NGOs. The main conclusions are:
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Organisational culture and informal learning are critical for the adoption of the knowledge management approach within NGOs;
The lack of financial resources and the instability of human resources are the main constraints of Romanian NGOs, forcing them to be innovative in all aspects (services, processes, organisational and marketing);
The knowledge management processes are facilitated by high quality human resources, competitive and very well trained, with specific and relevant competencies (a high quality human capital);
The knowledge capital of Romanian NGOs is not explored and studied.
As further directions of research, there are at least four areas of further studies that are identified within this study: knowledge strategies: determinants, objectives, alternatives; knowledge development process in NGOs: factors enabling it, stages; knowledge and learning tools; how the knowledge is effectively managed.
Appendix 1: Organisational profiles 1. National Association of Citizens Advice Bureaux (NACAB) The National Association of Citizens Advice Bureaux (NACAB) is a non‐governmental, non‐profit organisation, founded in 2003, to support and direct the activities of the Citizens Advice Bureaux (CABs). The NACAB consists of 49 independent NGOs that established CABs and CAB branches in more than 60 localities in Romania, both in urban and rural environments. The NACAB mission is to consolidate Romanian society by offering information/advice services, and by promoting the interests of all citizens in decision‐making processes. The values of the NACAB are freedom of expression, gender equality, civic involvement, and respect to the citizen. The NACAB coordinates the activities of the CABs, promotes the network of the CABs at the national level, represents the network of the CABs in its relationship with public authorities, and aims at elaborating documents with potential of impact in what concerns public policy making and social services sector, based on information furnished by the CABs. do you mean "to highlight documents and their potential impact in the area of public policy making and social services, based on information supplied by the CABs." In order to achieve its mission, the NACAB needs to further strengthen its organisational capabilities in order to become a stable landmark in mitigating the relationship between citizens (and their consequent problems and needs) and public institutions (and their responsibility to involve citizens in the decision‐making process). Usually, the NACAB facilitate informal learning and strengthen culture by the following means: annual general assemblies, e‐forums, workshops, internal publication, networking, and website resources. Strategic objectives for 2010‐2013: continual adaptation of services to the citizens' needs, a unified external image, raising participation in the organisation life of all internal structures, improvement of the fundraising capacity, and raising the involvement of the NACAB in influencing public policies. Currently, the NACAB carry out different projects financed from external sources (e.g. Strengthening the capacity of civil society to promote initiatives of social inclusion; Exercising democracy: evidence‐based and participatory local policy making, Closer to Europe through volunteering, Flowchart etc.). The NACAB is internationally affiliated to Citizens Advice International and European Citizens Advice Services. The NACAB has procedures for admitting members (organisations comprising a CAB in their structure) in the organisation, financial and accounting procedures, and internal rules of working and organisation. Sources: interviews, website. 2. The League for Defence of Human Rights (LDHR) The League for Defence of Human Rights (LDHR) is a non‐governmental, non‐profit organisation, founded in 1990, structured as an association, with branches at local level having a limited independence. The vision of the LDHR is a "world where we respect each other". LDHR values are: altruism, professionalism, responsibility, and respect for the citizen. In fulfilling its mission, LDHR members are guided by the principles of volunteering, independency, non‐discrimination, openness and responsibility towards the citizen. The organisation's current activities: information and advice services in the field of human rights, and their intensive promotion. The LDHR has procedures for admitting members (individuals) into the organisation, financial and accounting procedures, and internal rules of working and organisation. The means to facilitate organisational learning and
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Olimpia Neagu culture are general assemblies, networking, and training courses. The strategic objectives for 2010‐2013 are improvement of fundraising capacity and strengthening the organisation's public image at national level. Currently, some active local branches are carrying out various projects in the human rights field (human trafficking, minorities rights, immigrant minorities, education for democracy and human rights, European citizenship), financed from external sources (European Union) and the national budget. The LDHR is internationally affiliated with the International Federation of Human Rights in Paris. Sources: interviews.
References Argyris, C. and Schön, D. (1978), Organizational Learning: A Theory of Action Perspective, Addison‐Wesley Publishers, Reading, MA. Bergeron, B. (2003), Essentials of Knowledge Management, John Wiley & Sons. Bontis, N. (1999), “Managing organisational knowledge by diagnosing intellectual capital: framing and advancing the state of the field”, International Journal of Technology Management, Vol. 18 Nos 5‐8, pp. 433‐62. Bontis, N., Crossan, M.M. and Hulland, J. (2002), “Managing an organisational learning system by aligning stocks and flows”, Journal of Management Studies, Vol. 39 No. 4, pp. 437‐69. Cardoso,L., Meireles, A., Peralta,C.F. (2012), "Knowledge management and its critical factors in social economy organizations", Journal of Knowledge Management, Vol. 16 Iss.2, pp.267‐284. Ekinge, R., Lennartsson, B. (2000), "Organizational Knowledge as a Basis for the Management of Development Projects", Accepted to Discovering Connections: A Renaissance Through Systems Learning Conference, Dearborn, Michigan. Groff, T.R. and Jones, T.P. (2003), Introduction to Knowledge Management, Burlington, MA: Butterworth‐Heinemann. Hatch, J., (2010), Defining Organizational Knowledge: Turning individual knowledge into organizational intellectual capital, (http://knol.google.com/k/defining‐organizational‐knowledge) accessed 24 March 2013. Knight, T. & Howes, T. (2003), Knowledge Management ‐ A Blueprint for Delivery, Oxford: Butterworth‐Heinemann. Lettieri, E., Borga, F. and Salvodelli, A. (2004), “Knowledge management in non‐profit management”, Journal of Knowledge Management, Vol.8 No.6, pp.16‐30. Newman, B. and Conrad, K.W. (1999), “A Framework for Characterizing the Knowledge Management Methods, Practices and Technology”, The Knowledge Management Papers. (http://www.km‐forum.org/KM‐Characterization‐ Framework.pdf) accessed 25 March 2013. O'Dell, C. & Grayson, C. J. (1998), If Only We Knew What We Know: The Transfer of Internal Knowledge and Best Practice, Free Press, 1998. OECD (2005), Oslo Manual for Collecting and interpreting Innovation Data, Statistical Office of European Communities, Luxembourg. Perez, J.R. and Ordonez de Pablos P. (2003), ”Knowledge management and organizational competitiveness: a framework for human capital analysis”, Journal of Knowledge Management, Vol.7, No.3, pp.82‐91. Polanyi, M. (1966), The Tacit Dimension, Garden City, NY, Doubleday. Ryle, G. (1949), The Concept of Mind, London: Hutcheson.
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Theorising a new Concept: ‘Micro Intellectual Capital’ (MIC) Using Knowledge From Inside the Classroom Gary Oliver The University of Sydney, Australia gary.oliver@sydney.edu.au Abstract: A new concept is theorised, that of micro intellectual capital (MIC) which is equally applicable to organisations and the classroom. The elements of the theory are diverse knowledge, background and experience, a credible source of knowledge, a common issue, an opportunity for learning, motivation to establish and maintain communication, together with its context. These elements were derived from a broad consideration of the literature on intellectual capital (IC). By moving from the traditional top‐down view of IC to a bottom‐up view the detail of processes and events which generate MIC are apparent. Since their contribution to MIC varies, any snapshot of knowledge flows and stocks is likely to be misleading. It is illustrated from the classroom in a case study that explores the generation of MIC both individually and through membership of a team. In the classroom, MIC manifests in terms of more confident, flexible communication/persuasion, as well as through developing generic stills which are unlikely to become obsolescent as occurs with discipline knowledge. Situating MIC in this way changes its perception from an external judgement to an awareness gained through feedback, reflection and communication. Although developed outside the business environment but with reference to it, there is scope to compare and apply the insights from the MIC to business performance. It has the advantage of not requiring the balanced scorecard but is expressed with financial and non‐financial information. Keywords: bottom‐up, collaboration, intellectual capital (IC), knowledge flows, micro intellectual capital, team‐based learning
1. Introduction: Moving from aggregate IC to micro IC Universities are a major contributor to the intellectual capital (IC) in both their region and their nation (Sanchez and Elena, 2006). IC is the collection of intangibles which “allows an organisation to transfer a collection of material, financial and human resources into a system capable of creating value for the stakeholders” (European Commission, 2006, p. 4). A top down approach advocates inclusion of IC in accounting systems and reports (e.g., Corcoles et al., 2011). The objective of this paper is to propose a bottom‐up account of IC termed micro IC (MIC). It is illustrated using the needs, expectations and experience of students although it could equally be applied to organisations (businesses). The nexus between teaching and business is found in the observation from Ichijo and Nonaka (2007, p. 3) that “The success of a company in the twenty‐first century will be determined by the extent to which its leaders can develop intellectual capital through knowledge creation and knowledge sharing on a global basis.” Development of the theory proceeds in four steps. First IC is related to knowledge sharing and generally accepted segments (categories) of IC are identified. Second, rival theories of IC and value are examined. Third, the MIC theory is proposed. Fourth a case study is used to trace the generation of MIC. It is concluded that MIC is linked with activities that promote both feedback and reflection.
2. Generally accepted segments (categories) of intellectual capital There is a consensus that there are three basic and closely interrelated segments of IC (e.g., Brooking, 1996; Edvinsson and Malone, 1997; Moon & Kym, 2006; Stewart, 1997) which are based on effectiveness (Kannan & Aulbur, 2004) and knowledge as a stock (Dierickx & Cool, 1989) that is captured explicitly in a central repository that may be human (Starbuck, 1982) or technological. While these categorisations were formulated with organisations as their context they can be equally applied to student knowledge and learning as the three generic categories highlight different facets of IC as it is situated (Lave & Wenger, 1991) in university teaching and learning (Table 1). Table 1 presents the traditional segmentation of IC and then extends it to universities. It can be seen from Table 1 that the traditional three segments of IC can be readily extended to student learning. The difference lies in the student as a single being unlike the organisation, and their potential for passive acquisition of knowledge. The latter point is a matter that will be dealt with as part of the MIC discussion.
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Gary Oliver Table 1: Consensus view of IC as a stock for organisations extended to universities Stock of IC
Human capital
Employee capabilities knowledge, competences, experience, and know‐how Employee satisfaction Employee sustainability Organisation culture
Structural capital
Organisational processes including technology
Innovation and intellectual property
Customer relationships Relational capital (Social capital)
Supplier relationships Community (or community groups) relationships
Orientation Traditional Extended Organisations using organisational University classroom learning and performance teaching Accumulated value of investments in Knowledge possessed by students employees’ education, training, demonstrated in class, in the competence, and future (Becker, 1964; intermediate assessment tasks and in Kaplan & Norton, 1996; Nerdrum & the final summative assessment task Erikson, 2001; Pigou, 1928; Smith, 1776)
Organisational vision, mission and strategy implementation (Bollen et al., 2005; Knight, 1999; Mintzberg, 1978; Saint‐Onge 1996; Roos, et al., 1997). Processes, systems, structures and other intangibles including culture that are owned by the firm but do not appear on its balance sheet (Martín‐ de‐Castro, Navas‐López, López‐Sáez, Alama‐Salazar, 2006; Saint‐Onge, 1996; Stewart, 1997) Intellectual property created and available (Brooking, 1996; Edvinsson & Malone, 1997; Stewart, 1997; Sullivan, 1998) Knowledge embedded in the relationship with the customer (Knight, 1999; Leana & Van Buren, 1999; Skyrme, 1998; Sveiby, 1997) Knowledge embedded in the relationship with the supplier (Knight, 1999; Skyrme, 1998; Sveiby, 1997) Knowledge in groups and networks of knowledge resources embedded within and derived from a network of relationships (Coleman, 1988; Knight, 1999)
Awareness of students of the university and subject aims
Knowledge available to students from using the systems and processes of the university and of the class
This includes the learning character of the university, and its departments or disciplines Knowledge embedded in the student‐ teacher relationship This is the knowledge that arises from being part of a team and from being in the specific class (or stream) Knowledge gained by students about the university and higher education Knowledge provided by students in other classes and courses made available thorough interactions.
MIC as sharing knowledge as flows to build stocks Stocks and flows can be used to show the duality of knowledge (Bontis, 1999) so separating them is an artificial distinction. The position in this paper is that both are complementary but flows represent the critical catalyst phase of knowledge sharing since knowledge is stratified (Foss, 2006) across individuals. Making use of such knowledge available begins with sharing it as shown in Figure 1 with a knowledge market (a dyad or economic supply and demand forces). Traditionally knowledge is treated as an object which can be converted or codified (Bassi & van Buren, 2000, Dierickx & Cool, 1989, Johnson, 1999; Nonaka & Takeuchi, 1995; Nonaka et al., 2008), which can be transferred from the human being (Dixon, 2000) for external storage. Knowledge flows are worthy of examination in their own right. There is a body of knowledge studies (e.g., Augier et al., 2001, Dixon 2000, King & Ko 2001, Markus 2001, O’Leary 2001, Schultze & Boland 2000, Swap et al., 2001) which theorise knowledge flows in their own right. This view is used in Table 2. A flow approach to knowledge has the advantage that flows can be specified (Cricelli & Grimaldi, 2008) and their contribution to MIC can be assessed without necessarily managing knowledge as a process.
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Gary Oliver Knowledge possessor(s)
Knowledge flow
Knowledge sharing
Knowledge market
Knowledge flow
Knowledge recipient(s)
Figure 1: Knowledge stocks and flows in knowledge sharing The wealth creation argument from Stewart (1997) that a higher‐valued asset that results in new product characteristics and/or process improvements is the result of stocks and flows is central to MIC. In this case, the higher‐valued asset is the student who becomes an employee with better potential to perform when they commence their new job (or better perform their existing job). This capacity is a combination of practice in disciplined analysis and synthesis provided by studies and adapting to new situations. Rival theories in the field of intellectual capital creation
The rival theories of IC can be divided into those viewing knowledge as a flow and those viewing knowledge as a stock. The flow‐related theories are dominated by social capital creation (e.g., Nahapiet, & Ghoshal, 1998), improved leadership (e.g., Johnson, 1999) and “knowledge, experience, expertise and associated soft assets, rather than hard physical and financial capital” (Klein, 1999: 1), and practice (Østerlund & Carlile, 2005). These theories are summarised in Table 2. However, their knowledge flows also imply a change in the stock of knowledge. Table 2: Existing theories on knowledge flows with implications for MIC Author Augier et al., (2001)
Theories of knowledge as a flow Knowledge flows differ for structured and unstructured problems
Bourdieu, (1977)
Individuals are not necessarily aware of the value of their knowledge and it is made available as part of social practices Distinguish between near (routine) and far (novel) transfer of knowledge Views knowledge flows in a value chain
Dixon, (2000) King & Ko, (2001) Markus, (2001)
Nissen & Levitt, (2002)
Nonaka et al., (2008) O’Leary, (2001) Schultze & Boland, (2000)
Knowledge re‐use occurs with individual knowledge workers, small groups, novices (of expert) or secondary knowledge miners Analyses flows into direction, hierarchy, explicitness and time underpinned by the Nonaka and Takeuchi (1995) SECI conversion model Restatement and consolidation of the earlier SECI and Ba knowledge creation models Frequency of knowledge reuse involves knowledge flows Gatekeeping practices can inhibited knowledge flows
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Implications for MIC Developed from considering structured and unstructured problems, that is , from using significantly different approaches Depends upon the relational interdependencies of individuals and institutions but this may have a short‐ term focus Uses near knowledge techniques to facilitate far knowledge Does not depend on sequencing processes in the primary and support activities of the value chain May arise from transferring or repurposing knowledge
Independent as to whether knowledge is transmitted or constructed
May be the result of a sequence of processes
Facilitated by re‐use Affected by gatekeeping or the availability of multiple sources of information
Gary Oliver Author Swap et al., (2001)
Theories of knowledge as a flow Storytelling contains knowledge and lessons
Implications for MIC Developed through stories and storytelling
IC flow theories suffer from three deficiencies with regard to MIC. First when organisational performance is removed as a criterion their IC status is lost. Second, they lose scalability when particular knowledge flows are selected from particular individuals so IC is difficult to locate (e.g., Schultze & Boland, 2000). Third, theories that rely on a formal sequence (e.g., Nissen & Levitt, 2002) with reckoning of consequences at each process are apt to deny creation of IC and regard the processes as contributing no value (e.g., Nonaka et al., 2008). These theories imply that there is always choice in interactions or relations and that creation of IC is predictable, both of which MIC does not assume. The existing theories on stocks of knowledge (Table 3) deal with learning and/or valuation and have similar problems. Table 3: Existing theories on knowledge stocks with implications for MIC Author Bandura, (1977) Bontis, et al., (2002)
Theories of knowledge as a stock Stock is built through observation A stock of learning occurs at the individual and group levels
Dierrickx & Cool, (1989)
Stock is produced with a lag delaying the benefit to organisation Stock should be valued
Lev, (2001)
Implications for MIC Built though observation as well as through the creation of new knowledge Change the understanding and/or the actions of the individual although groups and organisations can facilitate knowledge flows Select time paths of flows that properly feed these stocks
Create intangible assets that have a value which can be measured
The stocks view of knowledge suffer from two deficiencies with regard to MIC. First, they require the ensuing stock to manifest itself unambiguously. As noted above, the learning views of knowledge require this to be eventually demonstrated through improved organisational performance Dierrickx & Cool, 1989) which is not always possible. Second, it accepts the fallback position that learning is a sufficient indicator (Bontis, et al, 2002). Clearly, these are fragile stocks despite any apparent competence, or capability of individuals or groups. Overall, these theories of knowledge flows and stocks are deficient in four major respects. First, they aggregate IC without identifying the individual and group contributors overlooking the Pareto principle that few are responsible for the many. Second, they are biased toward knowledge as a stock even taking a snapshot of a knowledge flow. Third they overlook the contextual factors that can stimulate flows and sensemaking. Finally they are deficient in their perspective by preferring performance rather than learning. In fairness to them, an aggregate view of IC is little concerned with from whom it originates and what the circumstances are which produced it because human capital is only one of three contributors and business fads such as process reengineering have straddled the organisation‐individual divide. MIC overcomes these shortcomings. Micro intellectual capital (MIC) theory
The micro IC theory proposed is the first of its kind. It has five interacting elements as shown in Figure 2 which are active within the specific environment. Each element is described identifying its source and its relevance to the MIC theory. There must be a diverse knowledge base (Penrose, 1959). MIC relies on advances in economic and multi‐ national firm studies to support a heterogeneous view of knowledge. There is evidence over a long period that the creation of new knowledge is facilitated by diversity (e.g., Becker, 1964/1993; Pawlowsky, 2001). There must be motivation for establishing and maintaining communication (Massey, 2001). This may have to do with social norms and values including group psychology of the in‐group and out‐groups (Evered & Louis, 1981). However, the overall contributor to communication appears to be associated with opportunities to learn (e.g., Schön, 1983), perhaps through mentoring (Swap et al., 2001) which is mainly informal and the satisfaction it can provide.
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Gary Oliver Working on a common problem or issue (Schein, 1970) facilitates knowledge flows. The common problem may be one which is given to the participants (Nonaka & Takeuchi, 1995) or one in which the participants recognise the need for investigation (Polanyi, 1966). However, a common problem or opportunity may not be sufficient; there must be an opportunity for learning. It is questionable whether copying observed behaviour (Bandura, 1977) in situ is learning but if it is then Argyris and Schon, (1974, 1978) would treat it a single loop learning where there is no reflection and reorientation of goals. Thus any learning opportunity should have the formal elements of making time available (Aronson et al., 1999) and active engagement with support (Vygotsky, 1934/1968, 1978). The outcome should be double loop learning in which goals and means are reconsidered after reflection (Argyris & Schon, 1974, 1978). Fifthly, the providers and recipients of knowledge must be credible sources of knowledge (Lave & Wenger, 1991). This can require the presence of experts who have extensive experience (e.g., Ericsson, 2006) or knowledgeable individuals who can provide hitherto unknown knowledge (Davenport & Prusak, 2000). Context provides the MIC theory with specificity (Augier, et al., 2001). There are two aspects of context. The first is environmental factors that affect perception of a stimulus. The second is the constraints and opportunities provided by context. The circumstances affecting MIC may therefore be facilitative or disruptive.
Figure 2: Close coupling of the five elements of the micro‐intellectual capital flow model within a specific environment The MIC theory then leads to a different view of the value of IC. Table 4 is factored into the sources of value traditionally identified which are compared with those for MIC. Table 4: Comparison of IC as aggregate value and MIC IC segment
IC value Employee capabilities
Human capital
Employee satisfaction Employee sustainability (turnover)
Structural capital
Organisation culture
Issue with MIC Capabilities are nascent and unlikely to be seen as a potential and judgement of them may be deferred or subject to overconfidence (Kahneman, 2011) Satisfaction and dissatisfaction in humans are in dynamic tension (Nietzsche, 1969) MIC is not oriented toward the perspective of the organisation Opportunities for ethical and helping behaviours are presented to individuals and teams and reactions are shaped by the organisation culture and individual values (Chatman & Barsade, 1992)
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Avenue for developing MIC Structured approaches for problem solving, communication and teamwork, and, repertoire of problems solved Personal values, skills and judgements about value or other people Externally imposed evaluation criteria and performance against the criteria Adaptability and resilience of personal, peer and organisational values including the access to resources
Gary Oliver IC segment
IC value Organisational processes including technology Innovation and intellectual property
Relational capital (Social capital)
Customer relationships Supplier relationships Community (or community groups) relationships
Issue with MIC Include procedure descriptions, on‐ the‐job training, informal learning and learning through doing (Arrow, 1962) Appropriate local ideas and techniques for solving problems (Davenport & Prusak, 2000) Establish formal and informal relationships which do not always use a codified strategy (Hansen, et al.,. 1999)
Avenue for developing MIC Formality and legitimacy of routines including innovation
Extent to which novel solutions are recognised and protected Continuity of relationships when difficulties are encountered encourages learning
The MIC thus balances learning with feedback, reflection and communication recognising that knowledge develops gradually (Schön, 1983). Learning features learning by doing. Feedback can identify erroneous knowledge, advise on improving it and helping understanding (Nadler, 1977). Reflection with reference to experiences considers commonalities, differences, and interrelations (Dewey, 1933). Although these are the crux of MIC they are not its sources since the knowledge (knowing) is associated with particular qualities and conditions at an instant, and the successful execution of an activity is only partly dependent upon the possession of appropriate knowledge.
3. Application of MIC to an empirical case A case study (Yin, 2003) is used to evaluate the theory. It uses the teaching and learning approach adopted in a postgraduate managerial accounting unit of study (Managerial accounting and decision making) which is the entry level managerial accounting course for both the Master of Professional Accounting and the Master of Commerce degree. Enrolments are approximately 300 per semester with approximately a 70% international student intake. The unit comprises a 13 week semester with 10 separate topics and a capstone integrated case study allowing for an opening introduction week and closing review week. Students are placed into teams in week one which they retain throughout the semester. Students are required to prepare for class (Michaelsen, 2004) and this preparation is tested with 10 multiple choice questions of basic knowledge first individually and then re‐taken in their team. Successful completion of the readiness assurance entitles teams to undertake the business practical. It consists of a scenario which requires analysis and selection of a managerial decision from a pool of six equally viable options. Teams have to justify their choice and defend it against challenges from other teams. The comments that follow are informed by triangulation (Jick, 1979). There are six classes taken by three different instructors which allow their observations to be compared. Data is collected by different methods (observation, self‐report, objective assessment and final exam calculations and essays). The team only functions in class and the class activities are structured as shown in Figure 3 which shows the simultaneous change in knowledge, the passage of time and the use of higher order skills or thinking (Bloom’s taxonomy, in Anderson and Krathwohl, 2001) in teaching and learning in individual and team (peer) contexts. (A BPMN or flow diagram is unsuitable as it cannot show the MIC creation opportunities as they are not processes as is expected with IC). Its interconnected nature indicates that none of these elements is dominant and their close coupling creates knowledge flows and stocks, building MIC. A diverse knowledge base is created by deliberately allocating students to teams on the basis of their education, communication and culture to achieve a mix of high, medium and low within the one team. MIC is generated through the new intellectual experiences, more complex social interactions and sharing their knowledge that arise from the presence of the diverse membership which mitigates against groupthink (Janus, 1972). There must be motivation for establishing and maintaining communication. Apart from the task orientation and the different potential contributions team members are capable of making there is the carrot of the assessment mark. If students are considered nodes they participate in the knowledge flows shown in Table 6 below. The MIC generated here takes the form of knowledge flows and knowledge stocks. However, knowledge flows are a combination of explicit and tacit knowledge (Polanyi, 1966) so the student has to make
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Gary Oliver sense of knowledge either for themselves in reflection (Boud et al., 1985) or by initiating discussion with other members (Brookfield & Preskill, 1995).
Figure 3: Overview of the IC building process in the classroom Table 6: MIC from knowledge flows at the major knowledge nodes Knowledge nodes Knowledge flows
Within team
Between all teams in the class
Knowledge flows in class
Description of concepts or analytical frameworks; commentary; clarification of understanding
Justification for the recommend‐ ation; Challenges to the recommend‐ ation; Defence by the recommend‐ing team
Knowledge flows outside class
The team may meet and discuss any concerns before class
‐
Conclusion on the value of MIC
Human and structural MIC with the major knowledge flow
Structural MIC although this is of considerable importance,
Team to Instructor
Instructor to team
Instructor to class
Comment‐ary on their position; questions about their interpretat‐ ion of the scenario
Advice and assistance in analysis and guidance on the robustness of their position
Consultat‐ion seeks to clarify any difficulties with preparation
Provide explanations and examples to assist the team apply their knowledge Human and structural MIC depending
Misunderst‐ andings on data or theory; Illustrations of current business events that relate to the scenario; Feedback on perform‐ance Discussion answers posted on Blackboard; Broadcast e‐ mail
Relational MIC. This is the most under‐utilised
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Relation‐ship MIC; while the class state this as
Instructor to individual student Any individual discussion is usually advised to the team member’s team
E‐mail
Human and structural MIC when students
Gary Oliver Knowledge nodes Knowledge flows resulting from the flow
Within team in terms of understanding the material as well as applying it to the scenario for the topic
Between all teams in the class the knowledge know is essentially one of comparing the strength and clarity of their own presentation with those from competing teams
Team to Instructor
Instructor to team
knowledge flow with the Instructor having to prompt each team for its concerns.
upon the instructor observing the progress and performance of each individual team.
Instructor to individual student significant in discover their surveys, that many the fact that questions they make no they pose in notes an e‐mail suggests they cannot be regard it as answered general by a short comment. reply they They pay usually take much closer advantage attention to of team feedback to interaction themselves as (sometimes a team. with the instructor) Instructor to class
Knowledge flows refresh, supplement and discard elements of the knowledge stocks as shown in Table 8. The MIC generated comprises both discipline and generic skills. Table 8: A skills view of knowledge stocks and flows of workplace know‐how Type of workplace know‐how
Discipline skills (in postgrad‐ uate Managerial Accounting for Decision Making)
Generic skills (key, core, essential skills, or competen‐
Knowledge flow
Knowledge stock
Inflow(s)
Outflow(s)
Units
Features of the analytical frameworks
Individual and Team knowledge revealed readiness test
Improvements in understanding analytical frameworks
Techniques for information organisation, filtering, analysis and synthesis
Evidence of definition and scoping of the problem in context and formulation of the solution Information reduction, summarisation and prioritisation
Matching analytical frameworks with problem/task characteristics
Examples of successful and unsuccessful dissemination
Combinations of theory, evidence and logical argument
Learning how to lead, cooperate and resolve conflicts
Team behaviours and etiquette including respect and encouragement
Understanding of the problem and reasonableness of the solution Team cohesiveness and productivity
Improved ways to representing information
Suitability of supporting worksheets and supplementary analyses
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Refreshed, supplemented or discarded Details of analytical frameworks suitable for resolving management issues Conceptual, logic and analysis skills in problem solving and making recommendations concerning major issues Relevant information collection and organising for communication and persuasion Communicating, negotiating and persuasion of ideas, and information People‐related skills
Units Mental models of the analytical framework and exemplar uses based on scenarios worked on in class Repertoire of problems solved
Structured approaches used for communication and reporting Experiences of negotiation and persuasion
Interpersonal, teamwork, customer‐service skills
Gary Oliver Type of workplace know‐how cies, necessary skills, transferable skills or employability skills)
Knowledge flow
Knowledge stock
Inflow(s)
Outflow(s)
Units
Assistance interpreting ethical dilemmas
Legal, moral and ethical judgements
Appreciation of the difficulties in being ethical
Means of adapting and coping
Individual differences on politeness, perseverance, goal‐setting, positive self‐ worth, commitment to learning and social functioning
Confidence and capabilities
Refreshed, supplemented or discarded Applied understanding of ethical principles and standards Personal skills
Units Responses to ethical dilemmas and their resolution Adaptability to change in a diverse environment; personal presentation; ability to deal with limited resources
Working on a common problem or issue facilitates knowledge flows. A major feature of the task is that it cannot be completed individually and does not have a single correct answer. The possibility of multiple answers promotes discussion particularly though challenges and clarifications by the team members. The MIC generated is both direct, that is in applying knowledge, as well as indirect, comprising teamwork. The specification of class preparation combined with the individual testing of students before they commence to apply their knowledge ensures the providers and recipients of knowledge are credible sources of knowledge. The MIC created is thus credible to the students who have co‐created their knowledge. Anticipating the application of knowledge creates an opportunity for learning which permeates the classroom. The fact that classes are scheduled each week reinforces this and allows students to observe the learning of their peers. The MIC generated is visible as performance in the weekly assessment as well as its lag effects when information is recalled from the unit of study. In this case context provides the environmental factors that reinforce student perception that their learning is team‐based because ‘it is the way things are done around here’. Also important are the effects of continuous improvement which ensures the student experience is not that of test‐pilot. In these circumstances the MIC is facilitative.
4. Conclusion The case study shows that MIC is built during the activities of feedback, reflection and communication. Making and defending decisions where a shared interpretation and joint analysis of a business practice leads to topic knowledge being reshaped, applied and adapted to the context of the business practical application of knowledge. It avoids the need for a balanced scorecard approach expressed with financial and non‐financial information. A limitation is the specificity of the present case although the MIC may differ in other teaching and learning environments. Nevertheless, MIC is shown to be identifiable from bottom up and is consistent with the value segments of IC. The comments of the anonymous reviewer concerning separating the theory from the case study are gratefully acknowledged.
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Analysis of Awareness and Priorities, Focused on Intellectual Capital Among Slovak Companies Ján Papula, Jana Volná, Anna Pilková and Jaroslav Huľvej Faculty of Management, Comenius University in Bratislava, Slovak Republic jan.papula@fm.uniba.sk jana.volna@fm.uniba.sk anna.pilkova@fm.uniba.sk jaroslav.hulvej@fm.uniba.sk
Abstract: The paper provides qualitative research based evidence on the usage of the concept of intellectual capital as the system providing indicators adding value to knowledge management initiatives within companies in Slovak republic. Data in this paper have been presented and analyzed through descriptive statistics using histograms, bar charts, statistics summaries such as average, median and standard deviation, as well as using the correlation analysis, which has allowed us to analyze the relationships among selected data. The results show that the area perceived by Slovak managers as the most important in terms of achieving company’s goals and objectives is the relational capital due to its component relationship with customers, which is followed by human capital. The structural capital with its elements optimization of processes and ICT and innovations is perceived as least important for top managers in Slovakia. This low perceived importance of organizational innovations management is critical in todays world where innovative activities, particularly generated through SMEs, are generally considered as the driving force of economic development, are developing options for future competitiveness in the form of new knowledge, and are increasing the efficiency of the economy and its ability to act. Keywords: Intellectual capital, knowledge management, knowledge-based resources
1. Introduction In a “knowledge-based” economy, the source of companies’ economic value no longer depends on the production of material goods but on the creation and manipulation of intellectual capital (Guthrie et al. 2004). To obtain the competitive advantage, it is crucial for organizations to utilize knowledge efficiently and to enhance their innovation potential (Will, 2008). To achieve long-term success, it is more advantageous for the organization to find the source of its competitiveness within the company, because the only stable certainty in a constantly changing environment is the internal capacity stemming from a desire to be successful in the future (Papula & Papulová, 2012). According to the resource-based view on the organization, the sustainable competitive advantage is being achieved by continuous development of existing and by creating of new company’s’ resources and capabilities in response to quickly changing market conditions. The main sources of a company, which have been developed in terms of today’s economy, are intangible resources, also referred to as the intellectual capital of the company. Thus, managing the intellectual capital becomes more and more important for future-oriented organizations (Will, 2008). As a result, the concept of intellectual capital, which incorporates skills and knowledge at all levels of an organization, has become the most important economic resource and is replacing financial and physical capitals as the most important source in the new economy.
2. Defining intellectual capital and its management in an organization During recent years, intellectual capital has been a subject of great interest of many researches in many scientific areas including management. Intellectual capital, defined as any knowledge convertible into value (Edvinsson 1997), brings the right schema for presenting qualities and potentials for company stakeholders. There are several views at the breakdown structure of intellectual capital model presented in literature, usually consisting of three main components: human capital, structural capital (also labeled as internal or organizational) capital and relational capital (also labeled as external capital) including customer relationship component (Edvinsson 1997, Sveiby 1997, Stewart 1998, MERITUM 2002, Bontis 2002, Mouritsen et al 2002, Pablos 2003). As human and structural capital both contain knowledge oriented towards something inside the organization, the relational capital contains knowledge items oriented outside the company (Papula and Volná 2012). To define every component of intellectual capital independently, human capital can be described as combinations of knowledge, skills, innovativeness and ability of the company’s individual employees (Edvinsson and Malone 1997). The human capital cannot be owned, it can only be rented (Edvinsson 1997). The structural capital consists of internal structure, which includes patents, concepts, models, computer, and administrative systems. Popular is defining the structural capital as knowledge that does not go home at night (Stewart 1998), or what left behind when the staff went home (Edvinsson 1997). The third category of
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Ján Papula, Jana Volná, Anna Pilková et al intellectual capital resources comprises external structure, customer capital and market assets, which are basically about relations with customers (Bukh et al. 2001). Figure 1 shows the components of intellectual capital as they were drafted within ARCS model for Intellectual Capital Repoting (Koch, Leitner and Bornemann 2000).
Figure 1: Components of intellectual capital in ARCS model for Intellectual Capital Reporting (Koch, Leitner and Bornemann 2000) The performance of an organization is given by the accumulation of the results of all processes and activities within that organization. Therefore, managers of organizations have to seek to discover and understand the factors that contribute to performance (Papulová 2008). When deciding on the most appropriate constituency for performance measurement, it is important to respect that strategic management is concerned with managing a business for a long term, and accordingly to select an appropriate perspectives and indicators (Holienka 2013). Several models, which measure the components of intellectual capital of an organization by using qualitative and quantitative indicators, have been developed after the introduction of the Balanced Scorecard in 1996 (Kaplan und Norton 1996). Until that time, comprehensive management information systems had already been developed, but financial indicators still dominated (Will 2008). However, only the performance measurement frameworks and control systems based on combination of financial and nonfinancial measures could ensure the development and realization of knowledge-based strategies (Tayles, Pike and Sofian 2007). The resource-based theory also states that a company’s competitive advantage is derived from the company’s ability to assemble and exploit an appropriate combination of resources (Cheng, Lin, Hsaio, Lin 2010). Among first such models, Skandia Navigator (Edvinsson and Malone 1997) and the Intangible Asset Monitor (Sveiby, Linard and Dvorsky 2002) were developed, both aiming at identifying and evaluating intellectual capital in order to outweigh deficits of mainly financially oriented management (Will, 2008). In 2000, Danish Agency for Trade and Industry supported by the Ministry of Trade and Industry published “A Guideline for Intellectual Capital Statements – a Key to Knowledge Management”, in which the processes of knowledge management were divided into identification of knowledge resources (elements of intellectual capital), defining the management challenges and actions that build knowledge resources and indicators to measure these resources or actions (Danish Agency for Trade and Industry 2000). Another examples of practical frameworks for intellectual capital management using indicators for measuring components of intellectual capital are the ARCS model for Intellectual Capital Reporting (Koch, Leitner and Bornemann 2000) and the “Intellectual Capital Statements – Made in Germany” model by the German Federal Ministry of Economics and Labour (Alwert, Bornemann and Kivikas 2004). All these authors have developed models, which measure the components of intellectual capital by using qualitative and quantitative indicators and communicate the results in an intellectual capital statement. The intellectual capital statement is an instrument to assess, develop and report the intellectual capital of an organization and to monitor critical success factors systematically (Mertins, Wang and Will 2009). The intellectual capital statement supports the company’s knowledge management, i.e. the part of management work that obtains, shares, develops and anchors knowledge resources (Danish Agency for Trade and Industry 2000). It provides a status of the company’s efforts to develop its knowledge resources through knowledge management in text, figures and illustrations (Danish Agency for Trade and Industry 2000).
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Ján Papula, Jana Volná, Anna Pilková et al The intention of “InCaS: Intellectual Capital Statement – Made in Europe”, another intellectual capital statement guideline developed under a project running under the European Union's Sixth Framework, was to harmonize the different national intellectual capital statements approaches and to develop and test this methodology among European small and medium enterprises (Will 2008). According to the ICS Structural Model (InCaS, 2008), the organization is embedded in the business environment. Regularly, a vision of the founders and owners serves as general guiding principle for major decisions and strategic positioning. Depending on the business strategy, managerial decisions lead to operational indicators, or measures. These measures serve to improve business processes and the utilization of intellectual capital in those processes. The concept of intellectual capital provides a comprehensive insight into different areas that are essential for the organization and its performance management. To manage these areas, it is necessary to identify both initiatives and indicators. It shows the system linking to various elements and highlights the importance and value added of a systematic approach to intangible resources. This means that the temporary emphasis on an elements and their development through knowledge management initiatives can stimulate the development of the whole system. Management of intellectual capital is then focused on a comprehensive assessment of the organization's approach to identify the components of intellectual capital, its rigorous measurement via so called intellectual capital indicators, continuous monitoring of developments and changes of its individual components, benchmarking the key factors affecting the business segment and planning to ensure the necessary resources and their capacity for building sustainable competitive advantage (Figure 2). The starting point is the vision, strategy and strategic objectives of the organization with a view to the possibilities and risks encountered in the business environment. Company resources and actions are then evaluated in terms of achieving these strategic objectives of the company. The organization has to implement management of intellectual capital, which was composed this way, into its corporate culture, processes and company strategy, because this is the only way to successfully put into effect needed measures at both the operative and management levels (Papula, Weberová 2007).
Figure 2: Knowledge-based resources, knowledge management initiatives and indicators as three different areas in evaluating intellectual capital and its management in an organization 3.
Research methodology
3.1 Research goal The aim of this paper is to evaluate the usage of the concept of intellectual capital as the system providing indicators adding value to knowledge management initiatives within companies in Slovak republic. For the research, we have used the breakdown structure of intellectual capital, which consists of three dimensions: the human capital, the structural capital and the relational capital including relations with customers.
3.2 Sample and data collection The research has been conducted within Slovak companies using structured face-to-face interviews with company owners, company directors, financial directors or financial controllers - depending on the size of interviewed company. The sample consists of 37 companies based in Slovak republic, especially small and
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Ján Papula, Jana Volná, Anna Pilková et al medium enterprises (SMEs). Through the interviews, three different topics have been investigated. Firstly, the perception of managers on the importance of individual areas of intellectual capital in terms of achieving the strategic objectives of the company has been asked. Respondents have answered the question: “In your company, in terms of achieving the objectives of the company, what significance do you attribute to:
1.1 The quality of human resources;
1.2 Optimization of processes and ICT;
1.3 Innovations;
1.4 Brand and company image;
1.5 Relationship with external subjects;
1.6 Relationship with customers.”
Interviewees have had the possibility to assign one from following answers: 1.
The specific area has for the company almost no importance;
2.
The specific area has for the company weak importance;
3.
The specific area has for the company medium importance;
4.
The specific area has for the company strong importance.
Secondly, we have investigated the actions executed in the company that contribute to building the knowledge-based resources. Respondents have answered following question: “Please, give your opinion on the following statements:
2.1 In our company we choose, develop and educate our employees, because everyone contributes their part to achieve corporate goals.
2.2 In our company, we strive to optimize and improve the quality of the processes carried out (both main and supporting).
2.3 In our company there is a mechanism to capture ideas and recommendations for improvement or innovation, (which come from the employees).
2.4 In our company we value and enhance the value of our brands (company or products).
2.5 In our company, we are aware of the need for strong relationships and partnerships (with universities, professional associations, business partners) and we provide activities to build them.”
For each statement in the second part of interview, the interviewees have had the possibility to assign one from following answers: 1.
The statement does not apply to our company;
2.
The statement sometimes applies to our company;
3.
The statement usually applies to our company;
4.
The statement always applies to our company.
Thirdly, we have analyzed the extent of the usage of different measurable indicators from the three mentioned dimensions of intellectual capital. Interviewees have answered the question: “Which indicators (predictive or proactive) are calculated and evaluated in your company?” The extent of involvement in evaluating of intellectual capital indicators by interviewed companies has been measured by giving the interviewees four different possibilities to answer: 1.
We do not know the specific indicator,
2.
We do not measure / evaluate the specific indicator;
3.
We evaluate the specific indicator but not on regular basis;
4.
We evaluate on regular basis, minimally once a year.
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Ján Papula, Jana Volná, Anna Pilková et al Figure 3 shows the structure of the interview, including preselected measurable indicators offered to interviewees for evaluation.
Figure 3: Structure and questions of the interview 4.
Results
As a result, we have generated an electronic database of investigated information. Quantification of qualitative data has been executed by assigning the numerical value to the answers, while the questionnaire used as a basis for the structured interview has contained scale for answers. To allocate numerical values for given answers in the first and second part of the interview, we have used following methodology:
Value 1 for answers „with almost no importance“ in the first part of interview and „the statement does not apply to our company “ in the second part of the interview;
Value 2 for answers „weak importance“ in the first part of interview and „the statement sometimes applies to our company” in the second part of the interview;
Value 3 for answers „medium importance“ in the first part of interview and „the statement usually applies to our company “ in the second part of the interview;
Value 4 for answers „strong importance“ in the first part of interview and „the statement always applies to our company“ in the second part of interview.
Individual indicators have been analyzed from the point of view of their regular evaluation in the interviewed company. To allocate numerical values at the company level for every questioned intellectual capital area in the third part of interview, following actions have been provided:
Value 3 has been allocated to a company, in which at least half of predefined indicators for the specific intellectual area are evaluated on a regular basis;
Value 2 has been allocated to a company, in which at least one but less than half of predefined indicators for the specific intellectual area are regularly evaluated;
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Value 1 has been allocated to a company, in which no of predefined indicators for the specific intellectual capital area is evaluated on the regular basis.
Data in this paper has been presented and analyzed through descriptive statistics using statistics summaries such as average, median and standard deviation, as well as charts and formulas and by using the correlation analysis. Data has been processed in Microsoft Excel and Statgraphic Plus software programs. Table 1 shows basic descriptive statistics summaries for the first two parts of interview. Table 1: Main descriptive statistics summaries for questions 1.1 – 2.5 1.1 The quality of human resources 1.2 Optimization of processes and ICT 1.3 Innovations 1.4 Brand and company image 1.5 Relationship with external subjects 1.6 Relationship with customers 2.1 Initiatives in the area of the quality of human resources 2.2 Initiatives in the area of optimization of processes and ICT 2.3 Initiatives in the area of innovations 2.4 Initiatives in the area of brand and company image 2.5 Initiatives in the relationship area
Average 3.8 3.2 3.1 3.5 3.4 3.9 Average 3.4 3.5 2.9 3.2 3.4
Median 4 3 3 4 3 4 Median 3 4 3 3 4
St. Dev. 0.4 0.9 0.9 0.8 0.6 0.3 St. Dev. 0.7 0.6 1.0 0.9 0.7
As can be seen in table 1, generally the knowledge-based resources (the intellectual capital of an organization) are considered as an important company resource by top managers in Slovakia, with average values and medians between 3 (medium significance) and 4 (high significance) on the 1 – 4 scale. Slovak top managers have been most consistent in answers regarding the evaluation of the importance of two components of intellectual capital, namely the relationship with customers and the quality of human resources, which they have identified at the same time as the components with highest importance. On the other hand, the components of structural capital, innovations and optimization of processes and ICT have been considered as areas with lowest importance. By closer look at the company's aspirations, i.e. to the knowledge resources stated as very important for achieving the business goals, the most frequent are following resources: The quality of human resources, relationship with customers and brand and company image. The lowest aspirations are in the fields of innovations and relationship with external subjects (figure 4).
Figure 4: The percentage of all investigated companies, which stated particular area of intellectual capital as the area of strong importance for the fulfillment of strategic objectives When analyzing the second part of the interview, we can sum up that companies in Slovakia are generally active in knowledge management activities, since average values and medians of individual intellectual capital areas reflecting the knowledge management activities all move around the value 3 and 4. Surprisingly, companies are most active in the area of optimization of processes and ICT and at the same time least active in
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Jรกn Papula, Jana Volnรก, Anna Pilkovรก et al the area of innovations, what is consistent with the first part of interview evaluating the perceived importance of individual intellectual capital areas. High activity of interviewed companies in the area of processes and ICT is also verified by analyzing the third part of interview, where indicators from this area belong among indicators that are regularly followed by the majority of interviewed companies. Figure 5 shows the percentage value to every predefined indicator in the interview reflecting the percentage of all interviewed companies evaluating the specific indicator on the regular basis. Interesting paradox can be noticed, that companies in Slovakia are most active in knowledge management initiatives from those intellectual capital area, which they consider at the same time as least important (the structural capital).
Figure 5: Structure of indicators by the frequency of usage By comparison of companies in terms of their aspirations in fields of knowledge-based resources and regularly used various indicators, the following finding had occurred (figure 6). We used the weighted averages by numerical values for given answers in the first and third part of the interview.
Figure 6: Overall focus on knowledge-based resources compared to their measurement Finally, to measure the strength of the linear relationship between all variables analyzed in our research, we have used Pearson product moment correlations between each pair of variables (figure 7). It is obvious, that
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Ján Papula, Jana Volná, Anna Pilková et al there exists statistically significant linear relationship (positive correlation) between variables 1.1 and 2.1 - the perceived importance of human resources and knowledge initiatives in human capital area (at the 95% confidence level with p<0.05), between 1.2 and 2.2 - the perceived importance and actions in the area of optimization of processes and ICT (at the 95% confidence level with p<0.05), between 1.3 and 2.3 – the perceived importance and knowledge management initiatives in the area of innovations (at the 90% confidence level with p<0.10), between 1.4 and 2.4 – the perceived importance and actions in the area of company brand image (at the 95% confidence level with p<0.05). On the other hand, variables 1.5 and 2.5 as well as 1.6 and 2.5 (area of building relationships with customers and external subjects) tend to be unrelated, as the correlation coefficient is near to zero. Following the Figure 6, it is not surprising that there is no evidence of significant correlation between the variables 1.x and 3.x.
Figure 7: Pearson product moment correlations between each pair of variables
5. Discussion and conclusion Knowledge management, which aims to building any of the components of intellectual capital in the company through knowledge-base building activities as well as the intellectual capital of a company itself, are nowadays considered as the driving motor of sustainable competitive advantage of an organization. Companies interested in building and maintaining a sustainable competitive advantage have to focus in larger extent on their intellectual capital as well as its management through knowledge management activities. By analyzing the responses of managers of Slovak companies, it was confirmed that the high priority is in the usage and management of relationships with customers. This also points to a possible distortion of relational capital overall results, arising from significant disproportion in the internal structure (Figure 4). The relationship with partners was identified as a very important resource in a much smaller number of companies, than the importance of relationship with customers. To verify the conclusions, as well as to check the correctness of the responses included in the main evaluation, we have put some further questions into other parts of the structured interviews. In them, instead of building strategic partnerships, managers from 35 of the 37 companies considered the activity of finding savings in the selling-buying process as important. While companies are looking for a way out of the current global economic crisis, up to 62% of companies didn’t characterize the innovation as highly important source for the fulfillment of the strategic objectives. This corresponds with the conclusion, that Slovakia lags behind other European countries and ranks among the countries with the weakest innovative performance (European Commision, 2012). Among the 27 EU countries, Slovakia is up to 22nd place and belongs with its innovation performance to the so-called moderate innovators. On the other hand, according to Innovation Strategy of the Slovak Ministry of Economy for the years 2007 to 2013, building of knowledge society through human capital and innovative activities, particularly generated through SMEs, are generally the driving force of economic development, are developing options for future competitiveness in the form of new knowledge, and are increasing the efficiency of the economy and its ability to act. Building-up the knowledge economy and innovation development of the economies of individual European countries are also one of the primary initiatives currently in force in the European Union Strategy for
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Ján Papula, Jana Volná, Anna Pilková et al Growth called Europe 2020. At the same time, in follow-up questions of structured interview, only 48% of managers of Slovak companies considered the activity of investing in research and development as important. Another paradox occurs on a deeper analysis of the levels of human capital. On the one hand we see high aspirations in this area, on the other hand, as the most frequently used indicators are: Earning per worker and value added per worker, while indicators: Return on investment in education and training activities are the least followed by interviewed companies. This clearly indicates the fact that human resources are not seen as the most important source of competitiveness, but as an area where managers are looking for cost savings. This also corresponds with additional structured interview questions, when managers from 32 of the 37 companies considered the activity of reducing costs and optimizing the HR as important. Overall we can opine, that management of intellectual capital depends on the ability to measure and to manage the performance indicators. At the same time, in analyzed Slovak companies, we have identified a low maturity level in the linkage between measurement of performance indicators and perceived importance of knowledge-based resources used for ensuring their strategic objectives. Slovak companies have discovered the resource-based approach of value creation to ensure competitiveness in the market, but the initiatives are still much cost-cutting oriented, than sustainable oriented. A limitation of this research is the relatively small sample size. Further investigation has to be provided within the topic.
Acknowledgements This paper has been funded by project Vega 1/0920/11.
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Towards Born-Global Innovation: the Role of Knowledge Management and Social Software Jan M. Pawlowski University of Jyväskylä, Finland jan.m.pawlowski@jyu.fi Abstract: Innovation is a key to success of organizations and societies – different forms of innovation processes are suitable for different contexts such as frugal innovation for less developed countries. As a starting point, the paper proposes a new form of innovation: born-global innovation. Born-global innovation describes a partly open innovation process which aims at parallel innovation processes in different countries and markets to increase time-to-market and outreach / scale of innovations. To successfully initiate these processes, knowledge management is a key. The paper discusses how social software can help to support knowledge management processes for born-global innovations. It is discussed how barriers to born-global innovations can be used to overcome main barriers and built successful, long-term, knowledge-intensive innovation partnerships. Keywords: Open innovation, born-global innovation, knowledge management, social software
1. Introduction How can social software enhance knowledge management in globally distributed innovation processes? How to overcome barriers towards born-global innovation processes? These are the key questions of this paper. Innovation is a key for organizations’ success. A variety of projects has tried to improve innovation processes to enhance product development, business incubations as well as regional development. However, many innovations – in particular from SMEs – stop at the idea stage or do not reach international markets. For successful innovation diffusion and adoption, it is essential whether an innovation can be applied and utilized in different markets. Frugal innovation (Zeschgy et al., 2011) is about creating highly scalable products which have reduced functionalities while reducing costs. A typical example is the production of low cost cars for the Indian mass market. However, those innovations are usually done long after an initial product has been developed for the initial market. Born-global innovations aim at reducing time-to-international-markets by creating collaborative innovation processes in developed and less developed countries with non-competing markets. The aim is to allow organizations to create innovations for their home market and develop partnerships to immediately create parallel / frugal innovations for other markets. This new innovation concept creates new potentials, in particular access to new markets, innovation diffusion as well as long-term partnerships. To implement such a process, however, knowledge-intensive, trusted partnerships need to be built. Thus, knowledge management is an essential part of innovation (Xu et al., 2010): the innovation process requires clearly structured knowledge-related activities (e.g. sharing knowledge with fellow innovators, acquiring knowledge from external experts, using knowledge to reach market maturity). However, there are no clear strategies yet how KM tools can support different types of innovation processes (cf. Gassmann et al., 2010). The paper discusses the role of knowledge management for global innovation processes. The concept of bornglobal innovations is introduced: how can innovation processes be shared between innovators with similar innovations but separated target markets. For this process, barriers are identified. Finally, the paper proposes social knowledge management solutions to overcome barriers and enable this new type of innovation.
2. Background: Open and frugal innovation A variety of innovation processes has been studied and tried out in recent years (Elmquist et al, 2009). However, challenges and barriers remain (Enkel et al, 2009). Two concepts are the starting point for our approach: open and frugal innovation. Open innovation (OI) can be defined as “the use of purposive inflows and outflows of knowledge to accelerate internal innovation, and expand the markets for external use of innovation, respectively” (Chesbrough, 2006). Open Innovation has been applied in many different domains to embed external knowledge and expertise into organizations’ processes (Chesbrough, 2004). As a variation, community-based innovation (West & Lakhani, 2008) or creation nets (Seely Brown & Hagel, 2006) are proposed to create innovations within a selected stakeholder group might be promising as a mixture of open and closed processes. However, the key to successful open innovation is to attract the best external individuals
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Jan M. Pawlowski to support an organization’s innovation process. The process can be sketched as following (Gassmann & Enkel, 2008):
Figure 1: Open Innovation Process (Gassmann & Enkel, 2008) Secondly, frugal innovation (Zeschgy et al., 2011) describes innovation by reducing non-essential parts of a product to attract new markets by lost cost and scalable products (e.g. cars for Indian mass market). Both concepts, however, are usually just applied for large companies (Mortara & Minshall, 2011). Therefore, new innovation processes need to be developed which allow innovation transfer for small and medium enterprises. However, they need strong support, in particular for knowledge management activities. The innovation process can be classified by the following factors:
Process (Huizingh, 2010): Is the process open to external experts or closed? Is the process managed and integrated into an organization’s strategy?
Outcome (Huizingh, 2010, Zeschgy et al., 2011): Is the outcome publically available or closed (just available to the initiator)? Are there different outcomes for different markets?
Target Market (Zeschgy et al., 2011): To which markets will the outcome be distributed?
Participants (Enkel et al., 2009): Partners can be clients, suppliers, competitors or public and commercial research institutions.
Participant Engagement (Phillips, 2010): Are partners invited or is there a public call for innovation (such as competitions)?
Tools and Support (Hidalgo & Albors, 2008): Which supporting mechanisms and software tools are provided? Tools for knowledge creations, sharing and usage are of particular interest.
Many challenges remain to manage open innovation successfully. Enkel et al (2009) identified main barriers to open innovation: 1) External barriers (loss of knowledge, high coordination costs, loss of control, complexity) and 2) internal barriers (difficulty in finding the right partner, imbalance between open innovation activities and daily business, insufficient time and financial resources for open innovation activities). Some of the challenges are management-related, most of them, however, knowledge-related (du Plessis, 2007). Li & Zheng (2009) determined five factors which influence innovation performance amongst them consistent aims, effective communication, mutual trust, cooperation rules and risk control. These risks especially occur when the process is distributed globally as perceptions about imitation and risks exist. Furthermore, general challenges exist which are common to globally distributed team processes such as coordination, communication or cultural challenges (Stier, 2006, Evaristo, 2003). These challenges are in most cases related to knowledge processes. Knowledge management is a critical part of innovation (Xu et al, 2010). Du Plessis (2009) summarizes the key aspects of KM to support open innovation by:
creating tools, platforms and processes for tacit knowledge creation, sharing and leverage in the organization
converting tacit knowledge to explicit knowledge
facilitating collaboration in the innovation process
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ensuring the availability and accessibility of both tacit and explicit knowledge used in the innovation process
creating a flow of knowledge used in the innovation process
assisting in identifying gaps in the knowledge base and provides processes to fill the gaps in order to aid innovation
assisting in building competencies required in the innovation process
providing a knowledge-driven culture within which innovations can be incubated
From a knowledge management perspective, the innovation process also creates a variety of challenges amongst them: licensing issues, lack of motivation to share, lack of resources, lack of training, or language misunderstandings. One of the main tasks in design-oriented knowledge management research is therefore to find interventions to overcome those challenges (Pirkkalainen & Pawlowski, 2013). Many different tools have been studied to support innovation processes (Hidalgo & Albors, 2008):
Figure 2: Support tools (Hidalgo & Albors, 2008) It is, however, unclear which tools can overcome specific barriers (Pawlowski & Pirkkalainen, 2013). It is necessary to provide recommendations which kind of innovation processes can be supported by certain processes, tools and interventions. As a summary, it can be stated that open innovation processes are promising and necessary for organizations’ success. However, a variety of challenges remain. Those can be addressed by specific knowledge management activities as well as interventions by creating processes and tools to overcome the main barriers.
3. Born-global innovation The main idea is the process of born-global innovation. Born-global innovation consists of purposefully shared innovation processes in selected communities targeting different markets with different needs. This concept takes up the trend towards born-global organizations (Knight & Cavusgil, 2004). Organizations no longer follow the traditional Uppsala model and enter new markets only at later, mature stages of the organization but are international from the very beginning (Johanson & Vahlne, 2009) and extends the idea of creation nets (Seely Brown & Hagel, 2006). Organizations are in many cases created directly in international collaborations. Therefore, the concept aims at creating a new innovation process to facilitate the creation of global start-ups based on collaborative innovation processes. Born-global innovation aims at transferring and implementing innovation at very early stages in noncompeting markets. As an example: a small company with health-monitoring devices has developed prototypes for the Finnish market. The product might be useful for the Indian mass market but there are difficulties for the SME to go global: 1) the product is too expensive, 2) the company does not have the
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Jan M. Pawlowski possibility to create an Indian subsidiary, 3) the company has no resources to develop a different device which is cheaper and fits the Indian market requirements. The solution is to create parallel global innovation processes: ideas are shared in a certain community or network at a very early stage. Based on these ideas, collaborations between experts are initiated, e.g. one SME each in Finland and India. Those actors then develop the different versions of the innovative ideas to suit their home market. The benefit of such as process is that SMEs can immediately go global without high risk – as the products are not competing, trust building and collaboration can be achieved much easier than in heavily competitive markets. The following figure sketches the innovation process in a simplified way (as not only two but potentially more innovators can be involved).
Figure 3: Born-global innovation process The concept includes the following stages:
Initiation and Idea Sharing: In this phase, potential collaborators propose their ideas and share innovations. A matching process is needed to find adequate collaborators which target different, noncompeting markets.
Collaboration agreement and trust building: Once potential collaborators are identified, a formal agreement is necessary that the innovation process is continued collaboratively. An important part of this phase is trust building to overcome the fear of imitation.
Innovation design and development: The initial ideas need to be further developed to create first prototypes, then full products. At the same time, business models and sharing procedures are refined.
Product launch and marketing: The product is launched in the target markets, in the ideal case at the same time.
Product improvement and further collaboration: The products and processes must be continuously improved – this can include the extension of the partnerships towards other markets.
By this process, new products can be developed in joint efforts. However, many challenges for such a knowledge-intensive process remain.
4. Knowledge Management for Born global innovations It is obvious that such an innovation process must be supported by clear KM activities. The KM classification of Probst & Romhardt (1997) describes different stages of the KM process. This classification is used to illustrate how KM relates and contributes to innovation processes. The relation of knowledge management activities and innovation processes can be described as following:
Knowledge acquisition: Partners with similar knowledge and expertise must be found. This includes assessment of current knowledge and agreement on knowledge sharing.
Knowledge creation: Once a partnership has been established, different products must be created, designed and developed to fit the requirements of the target market.
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Knowledge sharing and distribution: During the process, knowledge on must be shared intensively to create the different products collaboratively. Experiences from initial stages of a product must be shared to improve the product development. As the actors are distributed, the sharing process includes challenges regarding cultural and geographic dispersion.
Knowledge use: Knowledge from both partners must be used to bring products to the market.
Knowledge maintenance: Knowledge must be extended and continuously developed to continue the collaboration and next generation products.
However, a variety of barriers remain also for the knowledge management activities (cf. Riege, 2005, Pirkkalainen & Pawlowski, 2013), amongst them:
Trust: Partners need to build trust across organizational and cultural borders.
Cultural differences: Collaboration across borders requires cultural knowledge and the willingness to understand, respect and adapt.
Fear of imitation: The main barrier in open innovation is that product ideas are imitated, so the originator looses the benefits.
Language: Not all participants might speak the same languages.
Market knowledge: Both partners need to rely on the partner’s knowledge about the target market
Therefore, it is necessary to design interventions to facilitate the processes and overcome barriers at the same time.
5. Social software to enable born-global innovation Social software has much potential to support organizations in their internationalization process (Zhou et al, 2007). To overcome the initial barriers, it is necessary to identify a clear support process. The process consists of the key activities of born-global innovation processes enriched by knowledge management activities and possible support tools.
Figure 4: Innovation and KM Process This simple mapping allows us to focus which KM processes need to be included in innovation processes and which support needs are present. As a second step, the KM activities need to be supported. For this purpose, possible social software tools are mapped to 1) knowledge management activities (Pirkkalainen & Pawlowski, 2013) and 2) innovation processes. This mapping shall support innovators to select the right tools for their purpose. In many cases in practice, social software is introduced without clear objectives and goal-/process-orientation. The following table summarizes the findings.
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Jan M. Pawlowski Table 1: Social Software for KM and Innovation Processes (based on Pirkkalainen & Pawlowski, 2013) Tool category Blogging tools
Purpose Communication
Micro-blogging tools
Connection / awareness.
Social networking tools
Awareness, communication, sharing, (collaboration), (identification)
Social bookmarking tools
Identification, collaboration, sharing
Wiki
Collaboration, sharing, identification, communication.
Synchronous / Collaborative writing Instant messaging and chat tools
Collaboration
Shared information spaces /media sharing (video, audio, images, presentations) Conferencing
Identification, collaboration, communication sharing
Brainstorming tools
Collaboration
Communication
Communication
KM Activities & processes -Active & passive exchange of professional information -Acquire / capture / create, Apply/share/transfer. Incentive for (Reuse/innovate/evolve/transform), alerting -Knowledge Evolution -Idea-generation and problem- solving -Externalization, combination -Creation, codification, sharing, collaboration, organization -Retrieve knowledge for use -Enhancing information sharing (easy to identify information updates), building common ground, sustaining connectedness among colleagues, supporting informal communication -Alerting, informing users of changes -Socialization, combination -Building personal networks leading to creation of organizational memory -Scan/Map, Acquire/capture/create, store, Apply/share/transfer, alert -Social presence in Knowledge sharing, expert finding -Socialization, combination -Scan/Map, Acquire/capture/create -Collaborative building of a knowledge structure -Alerting, informing users of changes -Combination -Sharing, collaboration, organization -Active & passive exchange of professional information -Scan/Map, Package / codification / representation, Apply / share / transfer, Reuse / innovate / evolve / transform, alert -Idea-generation and problem- solving -Externalization, combination -Creation, codification, sharing, collaboration, organization -Acquire / capture / create, store
Innovation process Idea Generation Idea Sharing Product development Product marketing
-Building personal networks leading to creation of organizational memory -Knowledge sharing for quick questions and clarifications -Externalization -Creation, sharing -Scan/Map, Acquire/capture/create -Knowledge sharing -Strorage/retrieval -Combination -Codification, sharing, organization
Trust / partnership building Product development
-Human presence- and overview of activities in distributed tasks -Early stages of teambuilding -Externalization -Activities that are similar to take normally place in business meetings, decision support -Combination
Idea Sharing Product development
Trust building (through activity reporting) Product development (status messages)
Community building Partner identification Trust building
Idea sharing Product development
Idea development Product development
Idea development Product development
Idea Sharing Product development
Idea creation Product development
This mapping shows the general opportunities of social software â&#x20AC;&#x201C; however, it is necessary to identify a minimum number of tools to overcome barriers and support knowledge-related activities.
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6. Case study: implementing a support environment for born-global innovation The following case study (Yin, 2009) illustrates how social software should be selected for born-global innovation processes as a proof of concept. Based on the born-global innovation concept and the social software classification, we have implemented a prototypical support environment. This environment is currently tested and will be validated with selected innovative organizations. As a starting point, we have identified a clear support process which includes the innovation processes and selects and maps software tools to support those processes and overcome key barriers. The process thus consists of the key activities of born-global innovation processes enriched by knowledge management activities and possible support tools.
Figure 5: Social Software Support Process The process consists of the following phases and support tools:
Initiation and Idea Sharing: In this phase, potential collaborators propose their ideas and share innovations. This process should be supported by a social network where specific potential collaborators can be addressed without sharing an idea at a level which could lead to imitation.
Collaboration agreement and trust building: We use a specific communication protocol to support collaboration building once partners have agreed to join a collaboration and develop a product together. We use cultural profiles to create cultural understanding and trust between partners.
Innovation design and development: The initial ideas need to be further developed to create first prototypes, then full products. We aim at providing a social modeling tool to support (conceptual) modeling for development and understanding. Furthermore, the social (business process) modeling allows actors to collaboratively develop the production and related processes.
Product launch and marketing: We aim at supporting the product launch by using again social networking platforms, i.e. bringing the message of new products to each country’s main social networks.
Product improvement and further collaboration: The products and processes must be continuously improved – this can include the extension of the partnerships towards other markets. The same social tools as above are used for this purpose.
The above mentioned process and corresponding social software tools are designed for facilitating knowledge management activities. They support a clearly structured KM process for new collaborations and innovation creation across borders.
7. Conclusion and outlook In this paper, I have outlined the concept of born-global innovation as a promising approach to facilitate open innovation processes for innovative organizations, in particular SMEs. I have outlined the concept of bornglobal, parallel innovation and identified the knowledge management needs. Based on this, a common
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Jan M. Pawlowski innovation and knowledge management process was derived. Furthermore, a classification was presented how social software can support this common process. Finally, the approach was illustrated in a simplified case study as an illustration of the concept. The process and support tools create a basis for successful KM for a new form of innovation. By developing the concept of born-global innovation, we create new opportunities for small businesses and entrepreneurs to go global. Furthermore, the proposed solution provides continuous support for critical knowledge management activities. Even though the initial results are promising, further research is necessary. As a next step, the implemented prototype needs to be validated in larger settings, in particular focusing on how specific barriers and success factors are addressed.
References Carneiro, V. (2000). How does knowledge management influence innovation and competitiveness? Journal of Knowledge Management, 4(2), 87-98. Chesbrough, H.W. (2004). Managing open innovation. Research-Technology Management, 47(1), 23-26. Chesbrough, H. W. (2006). Open innovation: a new paradigm for understanding industrial innovation. In: Chesbrough, H. W., Vanhaverbeke, W. & West, J. (Eds.). Open innovation: researching a new paradigm. Oxford, 1-12. Du Plessis, M. D. (2007). The role of knowledge management in innovation. Journal of knowledge management, 11 (4), 2029. Elmquist, M., Fredberg, T., Ollila, S. (2009). Exploring the field of open innovation, European Journal of Innovation Management, 12 (3), 326-345. Enkel, E., Gassmann, O., & Chesbrough, H. (2009). Open R&D and open innovation: exploring the phenomenon. R&d Management, 39(4), 311-316. Evaristo, R. (2003). The management of distributed projects across cultures. Journal of Global Information Management (JGIM), 11(4), 58-70. George, G., McGahan, A. M. and Prabhu, J. (2012). Innovation for Inclusive Growth: Towards a Theoretical Framework and a Research Agenda. Journal of Management Studies, 49, 661–683. Gassmann, O., Enkel, E., & Chesbrough, H. (2010). The future of open innovation. R&d Management, 40(3), 213-221. Hidalgo, A., & Albors, J. (2008). Innovation management techniques and tools: a review from theory and practice. R&D Management, 38(2), 113-127. Johanson, J., & Vahlne, J.E. (1977). The internationalization process of the firm: A model of knowledge development and increasing foreign market commitments. Journal of International Business Studies, 8(1): 23–32. Johanson, J., Vahlne, J.E. (2009). The Uppsala internationalization process model revisited: From liability of foreignness to liability of outsidership, Journal of International Business Studies, 40, 1411–1431. Knight, G. A., & Cavusgil, S. T. (2004). Innovation, organizational capabilities, and the born-global firm. Journal of International Business Studies, 35(2), 124-141. Li, J., & Zheng, C. (2009). The context and innovation performance in external knowledge network: An empirical study in software industry of China, IE&EM 2009, IEEE, 2049-2053).. Mortara, L., & Minshall, T. (2011). How do large multinational companies implement open innovation?. Technovation, 31(10), 586-597. Phillips, J. (2010). Open Innovation Typology. International Journal of Innovation Science, 2(4), 175-183. Pirkkalainen, H., Pawlowski, J.M. (2013). Global Social Knowledge Management: From Barriers to the Selection of Social Tools, Electronic Journal of Knowledge Management (EJKM), 11(1). Probst, G. J. B., & Romhardt, K. (1997). Building Blocks of Knowledge Management-A Practical Approach. Ecole des Hautes Etudes Commerciales, Universite de Geneve. Riege, A. (2005). Three-dozen knowledge-sharing barriers managers must consider, Journal of Knowledge Management, Emerald Group Publishing Limited, 9(3), 18-35. Seely Brown, J., Hagel, J. (2006). Creation Nets: Getting the most from Open Innovation. The McKinsey Quaterly, 2, 40-51. Stier, J. (2006). Internationalisation, intercultural communication and intercultural competence, Journal of Intercultural Communication, 11, 2006. West, J. & Lakhani, K. (2008). Getting Clear About Communities in Open Innovation, Industry & Innovation, 15 (2), 223-231. Xu, J., Houssin, R., Caillaud, E., & Gardoni, M. (2010). Macro process of knowledge management for continuous innovation. Journal of Knowledge Management, 14(4), 573-591. th Yin, R.K. (2009). Case Study Research: Design and Methods, 4 edition, Sage Publications. Zeschky, M., Widenmayer, B., & Gassmann, O. (2011). Frugal Innovation in Emerging Markets. Research-Technology Management, 54(4), 38-45. Zhou, L., Wu, W. P., & Luo, X. (2007). Internationalization and the performance of born-global SMEs: The mediating role of social networks. Journal of International Business Studies, 38(4), 673-690.
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The Importance of Language Knowledge in International Companies Corina Pelau, Irina Purcarea and Stelian Stancu Academy of Economic Studies Bucharest, Romania corinapelau@yahoo.com irina.purcarea@gmail.com stelian_stancu@yahoo.com Abstract: The process of internationalization has determined many companies to spread their value chain activities around the world based on the competences of the employees in each region and on lower costs in some countries. The internationalization requires a complex coordination of the activities from the management of any company. An advantage for these complex activities, are employees who are able to communicate in a foreign language. For this reason more and more employees are required to know a foreign language. The aim of this paper is to determine the advantages of employees who have language skills regarding easier and faster chances to find a new job and also regarding the better condition at work. The paper focuses on the results of a research about the necessity, success and maturity of German speaking employees in international companies with offices in Romania. Especially in companies offering outsourcing activities it is important to find employees who have both the competences for the field in which they work and also the knowledge of the language. This paper presents the results of a research about the perception of German speaking employees in companies with offices in Romania about their chances and advantages at work. There are analyzed several general aspects about the perceived advantages of knowing a foreign language and the characteristics of the corporate culture in companies where there is a need of speaking a foreign language. Most of the results confirm the necessity of knowing a foreign language and the success of knowing a foreign language, by having higher salaries, better recompensations or even easier possibility of finding a job. There are also analyzed several aspects about the frequency and forms of communication in German language. This aspect influences the strategies of universities, because it is important to know the impact of knowing a foreign language in order to prepare the future students for it and to offer study programs in foreign languages. Keywords: language knowledge, international competences, corporate culture
1. Introduction The developments on the international markets and the increasing process of globalization change the demand and the profile of the desired potential employees. Because of the international process, nowadays it is not enough just to have good knowledge in the field of study or specialization, but also to be able to speak and communicate in a foreign language. This article presents the results of a research about the advantages of the employees who know the German language and the implications it has on the corporate culture. There are presented the perceived advantages of the employees as well as some aspects regarding the advantages the employees have. There are analyzed aspects such as the differences in the financial rewards, the easiness of finding a job, several aspects regarding the recognition of people who speak a foreign language and others. There are also presented the implications of the use of the German language on the corporate culture. There are analyzed several aspects of the corporate culture and compared with characteristics of the German corporate culture. Based on this analysis both companies and universities can develop their personnel or study programs strategy.
2. The importance of language knowledge for future employees The increasing globalization of the economy demands new knowledge and skills for its workforce. Employees in multinational corporations must have different new competences such as being able to speak and write in foreign languages, having knowledge of several world issues, and also to be able to use a broad range of new technology in unprecedented ways. Companies need to place significant emphasis on existing language skills within the company and use these strategically (European Communities, 2008). This means looking over recruitment policies, training strategies and principles for mobility in order to encourage staff to use and develop the skills they have already acquired and provide language training in ways that are both motivating and compatible with the demands of the workplace. Better performance is delivered by more engaged and motivated employees. This motivation and implication is critical for business success. The “right” engagement and motivation of the employees can be a source of competitive advantage for a company and can lead to excellent business results. Besides this it is difficult to be copied by others.
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Corina Pelau, Irina Purcarea and Stelian Stancu Communication represents one of the key drivers to employee engagement. Corporate communications is one of the most important connection points between the majority of employees and executive leadership. Internationalisation in the business world implies the need of a high level of language knowledge for communication both within and outside the company. Language Management (LM) represents ‘the extent to which the company is able to satisfy its language needs through cautious deployment of a variety of language management tools including for example language training and expatriation’. As it can be observed in figure 1, language management is determined by three major factors: language responsiveness, language preparedness and language awareness.
A research conducted in 2000 (Didiot‐Cook et al., 2000) showed that the expected level of English does vary according to the recruiting position and all companies provide additional training sometimes to improve the level of English proficiency on the job. Another important finding of the research pointed out that the language requirements differ in terms of the different economic sectors. In marketing, in the financial world and in IT services, an advanced level is a minimum requirement whereas in Accounting a pre‐intermediate level would be enough. In the food sector, companies use the local language and only want advanced English in Anglo‐ Saxon countries. However, in the context of international meetings and negotiations all participants from the company’s staff must be proficient enough to take part and contribute in the meetings.
(Source: Gundersen, S.L., 2009, Language Management in Multinational companies) Figure 1: The language management model A recent study on a Finnish company pointed to the common corporate language as a facilitator for career development, as well as facilitator for knowledge sharing and communication in general (Nousiainen,A., 2011). Another interesting finding was that multilingual employees perceive language skills as a strong influencing factor for their career decisions so that they had chosen positions that allowed them to use their language skills or so that they had gotten additional responsibilities at work thanks to their language skills which had given them new opportunities and made it easy to build networks. One finding related to knowledge of the local language, where interviewees stated that even though it was important from a social perspective to know the local language, it was not seen as a barrier for career mobility. The literature also points to the differences which exist between the expected language skills and the actual performance of the employees (Kaur and Clarke, 2009). Communication works best if there are several languages at disposition and the people involved can choose when interacting with employees or clients. The largest number of languages is spoken in subsidiaries located in Nordic countries, followed by Continental European and Asian countries, with the lowest number of languages spoken in Anglophone countries. Andersen and Rasmussen (2002) referred to Germany as the single most important market for Danish firms. In this context, linguistic competence in German is present at many levels in the company. Linguistic competence is required at employment and the companies work hard to develop this competence. The literature also emphasizes the importance of supporting an active policy of language management in international companies, by referring to the impact of language skills on the decision‐making process (Thomas,C.A., 2007). Strategic decision‐making effectiveness can be increased not
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Corina Pelau, Irina Purcarea and Stelian Stancu only through the gathering and transfer of knowledge in all the relevant languages but also in what concerns knowledge overlapping in the different languages. In an increasing global marketplace, foreign language capability and cultural awareness are essential for the competitive advantage of a company. For example, for export markets in Asia and the Middle East, foreign language capability, can be addressed in the short term through recruitment, upskilling employees, use of professional translation and interpreter services, and the hiring of native speaking Channel Partners / Agents in the target market. Recent EU research showed that companies can achieve higher export sales growth by means of having a workforce foreign language development strategy in place which supports their export business plan (Forfas, 2012).
3. Research regarding the perception of German speaking employees at work In this chapter there are presented the results of a research about the influence of the German language knowledge on the performance of the individuals and on the corporate culture of these companies. The objective of the research was to determine if the employees who also have language knowledge besides the specialty knowledge, have better professional chances, if they have better conditions at work and if there is an influence on the corporate culture. The target group of the research are German speaking employees in companies or offices in Bucharest, Romania, where the German language is used at work. The research was conducted in the period December 2012 – January 2013 and there were questioned 310 employees. In this article there are presented the results regarding the advantages of knowing the German language for their professional career. The answers for the questions were designed with the help of Likert scale, having values from 1 (no influence/ don’t agree) and 5 (big influence/ agree). The first analyzed aspect is the evaluation of several statements about knowing the German language. As it can be observed in figure 2 most of the employees say that for them it is easier to find a job if they know the German language or that they have advantages at finding a job. Most of the respondents evaluate that there is an influence (4.24 out of 5) on finding easier a job and also that there are advantages at getting a job (4.18 out of 5). Besides this, German speaking employees evaluate the influence at 3.92 for the fact that they have higher incomes. So 48% of the German speaking employees state that they have language bonus between 1‐ 25%, while more than 18% say that they have a bonus between 26‐50%. Only 29% say that they don’t have any bonus. Another advantage of knowing the German language is the international mobility. Employees who know the German language value the influence of knowing the German language at 3.52. 4.24
Easier to find a job
4.18
Advantages when getting a job
3.92
Higher income Mobility on an international level
3.52
Recognition from the customers
3.47
Recognition from the management
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Language certificate
3.44
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3.22
Recognition from the colleagues
3.05
Structured thinking
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3.03
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2.89
Personal life
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2
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(Source: Results of own research) Figure 2: Perceived advantages of knowing the German language
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Corina Pelau, Irina Purcarea and Stelian Stancu Knowing the German language has also an influence on the recognition from customers, management and other colleagues. It is interesting that the German speaking employees evaluate the recognition of the customers (3.47) and that of the management (3.46) as higher than the recognition from the colleagues (3.05). While for the recognition from the customers and management there was rather an influence, in the case of the colleagues its is with a value of 3.05 rather neutral. Another aspect on which knowing the German language has an influence are the promotion chances at job. Probably because of the fact that most of the employees get good jobs from the beginning, they don’t value so high the promotion chances. Competences and skills such as structured thinking or better organization are valued as being neutrally influenced. Personal life is an aspect which is not influenced by knowing the German language. The advantages of knowing the German language for the professional life is confirmed by the results of the following figures. One of these advantages is the short time of finding such a job. As it can be observed in figure 3, 49% of the questioned persons have found their job in time frame of 2‐4 weeks, while 22% of the German speaking employees have found their job within 5‐8 weeks. In both cases this is a very short time. Moreover 13% of the employees have found their job in less than 2 weeks. Only 4% of the employees have searched more than 6 months for a job. 4
8
13
22
49
less than 2 weeks
2-4 weeks
5-8 weeks
less than 6 months
more than 6 months
(Source: Results of own research) Figure 3: Time to find a job for German speaking persons in Romania
Another advantage of the German speaking employees are the financial bonuses. According to their opinion, employees who know the German language, have bonuses for their competences. 49% of these employees affirm that they have a bonus of 1‐25%, while 18% say that they have a bonus of 26‐50%. 4% of the questioned persons affirm that they have an even higher bonus. Only 29% of the respondents affirm that they don’t have a bonus. Despite of this, it was not checked the value of their income. 4 18
29
49
no bonus
1-25%
26-50%
more than 50%
(Source: Results of own research) Figure 4: Bonus for knowing the German language
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Corina Pelau, Irina Purcarea and Stelian Stancu The results of the research confirm the fact that employees who know the German language not only can they find easier a job, but they also have higher incomes or higher bonuses at work. The explanation for this situation is the high number of outsourcing companies who externalize their activities in Romania. Many activities such as customer service or support, accounting or process and operations management where externalized to Romania, as they can find there qualified personnel at lower costs. Because of the efficiency of the processes more and more activities are outsourced from Romania which increases the demand for well qualified German speaking employees. Consequently the conditions for work such as higher incomes or easier time to find a job have higher advantages in Romania. These results were also confirmed in different discussions with employees or graduates who know the German language. They confirm the easiness of finding a job and the financial advantages. Another aspect is their high fluctuation of personnel at work, as many employees change jobs in the hope of even better conditions.
4. Attitude towards the corporate culture in German speaking companies in Romania Regarding the influences of the characteristics of the corporate culture in German speaking companies, these can be observed in figure 5. It is important to mention that the presented characteristics are both general characteristics and also characteristics determined by the German culture. According to the opinion of the employees one of the most important aspects for the culture of German speaking companies in Romania is the practical experience. On a scale of 1 (don’t agree) to 5 (agree) the employees of these companies value the practical experience at 3.98. Employees also agree that diplomas are important for these companies (3.58). Practical experience can be considered as a general aspect, while the importance of diplomas can depend on the culture. There are cultures were having a diploma is more appreciated than in others. Pracatical experience is important
3.98
There are cultural differences
3.82
Diplomas are important
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German corporate culture has a great influence
3.54
Employees are stimulated to learn/ improve the German language
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There are mentality differences between German speaking companies and others
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Suppliers from German speaking countries are preferred
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Similar corporate culture as in Germany
2.88 0
0.5
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(Source: Results of own research) Figure 5: Influence of the German knowledge on the corporate culture
Most of the German speaking employees rather agree that there are cultural differences between the offices in Romania and its headquarter. The statement about the cultural differences is evaluated with 3.82 on a scale of 1 to 5. With an evaluation of 3.54, the employees agree that the German corporate culture has a great influence on the culture of the office in Romania. Besides this most of the employees make a delimitation of the German corporate culture among other cultures. Most of the employees agree that there are mentality differences between the German speaking companies and others. Regarding the promotion of the German language and culture most of the employees have a neutral to rather agree opinion on the fact that they are stimulated or supported to learn the German language (3.53). This can be explained by the fact that there are needed German speaking employees. Besides this learning the German language improves the communication with the stakeholders of the company. The statement regarding the preference towards suppliers from German speaking countries has a rather positive to neutral value (3.24). On one hand the choice regarding the
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Corina Pelau, Irina Purcarea and Stelian Stancu suppliers can be based on long term relations. On the other hand in a global world, each company tries to get the best option worldwide, despite its national values. Different aspects about the communication in German speaking offices in Romania are analyzed in the following figures. In figure 6 there is presented the frequency of speaking German at work. 78% of the respondents affirm that they speak German every day. It must be mentioned that the target group for this research were chosen based on their language knowledge. For this reason this amount is probably higher. 8% of the respondents affirm that they speak German every week, while 4% of the respondents affirm that they use the German language every month. Only 10% say that they speak German more seldom. 10
4 8
78
every day
every week
every month
more seldom
(Source: Results of own research) Figure 6: Frequency of speaking German at the job
The stakeholders to whom the employees speak most frequent German are the customers. 82% of the questioned persons say that they speak German mainly with their customers. This can be explained by the fact that many German companies have externalized or outsourced their activities in Romania. 44% of the respondents affirm that they speak German to their colleagues, while 32% of them say that they speak German with the management. This implies the fact that there are colleagues or managers from abroad. These results point out again the importance of speaking a foreign language. Most of the questioned people speak German with more than one group of stakeholders. Without knowing a foreign language it would be difficult to have a good communication in the company.
90
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80 70 60
44
50 40
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%
30 20 10 0 Customers
Colleagues
Management
(Source: Results of own research) Figure 7: Stakeholders with whom they speak German
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Corina Pelau, Irina Purcarea and Stelian Stancu Regarding the type of communication, most of the respondents (76%) have affirmed that it is rather formal. Only 24% of the respondents say that they have an informal and friendly type of communication. This is probably influenced by the German corporate culture, which is rather formal.
24
76
Formal
Informal and friendly
(Source: Results of own research) Figure 8: Communication in German speaking offices in Romania
5. Conclusion The results of the research show that knowing the German language brings a lot of advantages for the employees who know it. As we mentioned before we live in a world with increasing globalization and the internationalization of processes in business have changed the requirements to employees worldwide. Based on these developments, for the employees nowadays it is not enough just to be well qualified in the professional field, but also to have language knowledge and to be able to use it in the professional field. As the results of the research show, at a personal level, knowing a foreign language has great advantages for the future job chances, the flexibility of the employees and also on the financial advantages of the people. For instance knowing the German language in Romania allows a person to find a job or to have better financial rewards. Besides the advantages at a personal level, there is also an impact on the corporate culture of the companies. As the employees evaluate it, in German speaking companies, there can be identified some characteristics of German corporate culture. For instance the communication with customers, colleagues and managers takes place in German language, the communication style is more formal and other. Besides this the language skills of the potential and actual employees gives companies a higher flexibility. Certain activities such as customer support or accounting don’t have to take place anymore in the headquarters countries, but can be externalized and relocated in other parts of the world depending costs or other criteria. This development has implications both for the strategy of the companies and also for universities and even schools. Knowing that there is demand for well qualified employees who know the German language, there should be developed study programs which should develop both the competences in the field of the specialization and also the language competences. Using the German language at work has also implications on the corporate culture. Probably this development is caused by the collaboration with German stakeholders.
References Andersen, H., Rasmussen, E.S., 2002, The role of Language Skills in Corporate Communication, retrieved at http://www.sam.sdu.dk/~era/Helle%20Andersen%20og%20Erik%20S.Rasmussen.pdf AON Hewitt, 2012, 2012 Trends in Global Employee Engagement, http://www.aon.com/attachments/human‐capital‐ consulting/2012_TrendsInGlobalEngagement_ Final_v11.pdf , accessed on: 23.03.2013 Didiot‐Cook,H. et al., 2000, Language needs in business, a survey of European multinational companies, retrieved at http://www.hec.edu/var/fre/storage/original/application/7ca31409fee 3f05c77b4a3d9286927d2.pdf , accessed on: 23.03.2013 European Commission, 2008, Companies work better with languages – Recommendations from the Business Forum for Multilingualism established by the European Commission, 2008, European Communities, retrieved at http://ec.europa.eu/languages/pdf/davignon_en.pdf, accessed on: 23.03.2013
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Corina Pelau, Irina Purcarea and Stelian Stancu Didiot‐Cook,H. et al., 2000, Language needs in business, a survey of European multinational companies, retrieved at http://www.hec.edu/var/fre/storage/original/application/ 7ca31409fee3f05c77b4a3d9286927d2.pdf , accessed on: 23.03.2013 Forfas, 2012, Key skills for enterprise to trade internationally, retrieved at http://www.skillsireland.ie/media/EGFSB22062012‐Key_Skills_for_Enterprise_to _Trade_Internationally.pdf , accessed on: 23.03.2013 Gaal, Z.; Szabo, L.; Csepregi, A. (2011): Indiviudual characteristics influencing competences found important for knowledge sharing by middle managers, in Bratianu, C.; Bratucu, G.; Lixandroiu, D.; Pop, N. Al.; Vaduva, S. (2011): Proceedings of th the 6 International Conference on Business Excellence, pg. 221‐224. Gaal, Z.; Szabo, L.; Csepregi, A. (2012): Organizational characteristics and social competences – are there differences within social competences connected with co‐operational skills based on the characteristics of the organization? in Bratianu, C.; Bratucu, G.; Lixandroiu, D.; Pop, N. Al.; (2012): Business Excellence – Challenges during the Economic Crisis ‐ Proceedings of the 6th International Conference on Business Excellence, pg. 220‐224. Gundersen, S.L., 2009, Language Management in Multinational companies, SNF Report No.21/09, retrieved at http://brage.bibsys.no/nhh/bitstream/URN:NBN:no‐bibsys_brage_23893/1/R21_09.pdf , accessed on: 23.03.2013 Nousiainen, A., 2011, The relationship between language and careers in multinational corporations: A case study of UPM – Kymmene Oyj, retrieved at http://epub.lib.aalto.fi/en/ethesis/pdf/12559/hse_ethesis_12559.pdf , accessed on: 23.03.2013 Pop, N.Al.; Tantau, A.; Pelau, C.; Bena, I. (2011): Human Capital and Relational Capital Dynamics within a University, in: Proceedings of the 3rd European Conference on Intellectual Capital, Nicosia, Cyprus, pg. 343‐349. Sarjit Kaur, Candice Marie Clarke, 2009, Analysing the english language needs of Human Resource staff in multinational companies, English for Specific Purposes, Issue 3 (24), Volume 8, retrieved at http://www.esp‐ world.info/Articles_24/Analysing%20EL%20needs%20of%20HR%20staff%20_ Sarjit%20&%20Candice_.pdf , accessed on: 23.03.2013 Skills for success in multinational business, http://hrd.apec.org/index.php/Skills_for_Success_in_Multinational_Business Thomas,C.A., 2007, Language Policy in Multilingual Organizations, retrieved at http://www.gse.upenn.edu/sites/gse.upenn.edu.wpel/files/archives/v22/v22n1_Thomas.pdf , accessed on: 23.03.2013
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Linking External and Internal R&D and experience based Knowledge Flows for Innovation via Organisational Design Elements Monika Petraite Kaunas University of Technology, Donelaicio 20-320, Kaunas, Lithuania monika.petraite@ktu.lt
Abstract: The gaps in linking external and internal R&D and experience based knowledge (i.e. integration of external knowledge in internal innovation processes), as well as in linking R&D and market knowledge occur as a failure of the organizational innovation management process. From the theoretical point of view we can say, that organizational design determinants should be adjusted to the enabling and absorption of various knowledge flows and linkages for innovation, however, the problem is of much deeper scope, as we want to know, which organizational design factors ensure the enablement of knowledge flows and how? How organizational design determinants are changing with the type (R&D, experience based, market driven) knowledge flows for innovation? What are successful organizational design combinations that ensure the linkages of various knowledge types for innovation? Based on this problem formulation, the paper aims to explain the impact of organizational design determinants on the knowledge flows for innovation (positive and negative effect), with the specific focus on two types of knowledge classified by its origin, i.e. R&D, experience based, and provide empirically tested interrelationships between the certain configurations of organizational design factors and combinations of knowledge flows and linkages for innovation in business organizations. Keywords: External and internal knowledge flows, R&D based knowledge, experience and market based knowledge, organizational design, innovation
1. Introduction The link between knowledge management issues and innovation is not a new, still not resolved question in innovation management literature and practice. The varieties of knowledge typologies and accordingly changing nature of knowledge involved in innovation process are remaining at the focus of both, knowledge management and innovation researchers. The scope of literature on the issue shows that at least two types of knowledge are of crucial importance in R&D driven innovation process, i.e. new knowledge on technologies, processes, etc., created via external and internal R&D processes, and experience based knowledge, established via organizational and market driven learning processes. Firms are heavily investing in acquisition of both types of knowledge; however, it does not always lead to the successful innovation process, because of the several reasons: first, because of the failure of integration of externally and internally developed R&D and experience based knowledge into the innovation process, second, because of the failure of linking the R&D based knowledge with the experience based knowledge in innovation development. The gaps in linking external and internal R&D and experience based knowledge (i.e. integration of external knowledge in internal innovation processes), as well as in linking R&D and experience based knowledge occur as a failure of the organizational innovation management process. From the theoretical point of view we can say, that organizational design determinants should be adjusted to the enabling and absorption of various knowledge flows and linkages for innovation, however, the problem is of much deeper scope, as we want to know, which organizational design factors ensure the enablement of knowledge flows and how? What are successful organizational design combinations that ensure the linkages of various knowledge types for innovation? The analysis of those factors is not only important because of the need to describe the organizational infrastructures enabling knowledge flows at the micro level from the empirical perspective, but primary because it provides the ground and implications for action in designing productive knowledge flows for innovation. A wide body of literature has arisen that identifies the common factors shared by innovative organizations and the factors that impact on the ability to manage innovation. The few literature review based models on the organizational factors that influence the ability to manage innovation at the firm level were constructed and relationships between the factors where identified at the theory level (Jucevicius, 2008, 2010), Smith, at all, 2008). Some of the conceptual frameworks of organizational knowledge flows for innovation, that integrate soft and hard organizational dimensions and processes remain very few, and actually non of them is able to explain how and in which way certain configurations of organizational factors lead to the formation and diversity of knowledge flows for innovation.
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Monika Petraite Based on this problem formulation, the paper aims to explain the impact of organizational design determinants on the knowledge flows for innovation (positive and negative effect), with the specific focus on types of knowledge classified by its origin, i.e. R&D and experience based, and provide empirically tested interrelationships between the certain configurations of organizational design factors and combinations of knowledge flows and linkages for innovation in business organizations. The paper is structured as follows. The first section is dedicated to the concept ot R&D based innovation analysis,
2. The concept of R&D based innovation The innovations based on the science and technological knowledge are considered to be among the key drivers of productivity, competitiveness, economic growth and quality of life. The technology-based innovations, their continuous improvement in the last two decades has transformed the world economies and had a profound impact on the globalisation and development of the knowledge economy. Although in NIS studies almost every innovation is regarded as technological innovation, we should clarify the concept of technological innovation within the contemporary innovation theory. At the outset of our discussion, we have to admit that innovation, including the technological innovation, is first and foremost a category of economic development rather than that of R&D. Even if we think of the dimension of technological innovations, the market involvement remains crucial. For example, ‘innovation is a successful commercial application of new technologies, ideas or production and/or organization methods, which manifests itself in the commercialisation of new or improved products and/or processes in the market. Innovative activity is defined as the implementation of scientific, technological, or know-how based knowledge in the production processes, which creates an opportunity to produce new or improved products and improve production and business processes. Innovation embraces the whole chain of steps from the idea generation to product commercialisation, which are executed in the close interaction of the firm and its environment’. However, innovation is primarily regarded as an area of entrepreneurial intervention. According to Drucker (1993) ‘innovation represents the specific tool of entrepreneurs, the means by which they exploit change as an opportunity for a different business or different service innovation involves, changing the value and satisfaction obtained from resources by the customer’. The innovation transforms and elevates the economic resources to the levels of higher productivity; it also creates new resources, and sustains the competitiveness of business organisations or even entire nations. By referring to the existing variety of the definitions of innovation, Jakubavičius et al. (2003) point out that, according to Schumpeter (1934), innovation tends to be more of an economic rather than technological phenomenon. Technological development is not considered to be an innovation unless it generates the growth of economy and profit. In order to generate profit, innovation has to develop and maintain a unique competitive advantage in national and international market. In this way, in order to define the relation of science and technology knowledge with innovation, we should also consider the entrepreneurship, or technological entrepreneurship, as a linking tool between the R&D knowledge and the market. While exploring the definition of science and technology knowledge based innovation, we should consider the key characteristics of the science-based technological regimes (Coriat et al., 2002). In order to understand the specificities of science-based sectors, the basic point is the opposition between two main forms of industrial innovation process regarding the conditions of production and diffusion of the basic knowledge and capabilities (Coriat and Weinstein, 2001; Marsili, 2001). In the ‘scale intensive’ and ‘specialized suppliers’ sectors (typology suggested by Pavitt, 1984) or in the ‘complex systems’ and ‘product engineering’ regimes (typology suggested by Marsili, 2001) innovation is mainly the result of development (as opposed to research) or engineering activities. The innovation capacities of firms rely mainly on the specific and tacit, collective capabilities, formed by the internal processes of collective learning. It does not imply that relations with the academic research are not important, but it mainly concerns the engineering disciplines (mechanical and electrical engineering, computer science) or general scientific knowledge (mathematics); one of the key problems for firms is the capacity to have access to and to combine various technical knowledge and capabilities. In the science-based innovation regimes innovation is based on research, which is to a large extent carried out outside firms. Innovation is thus directly dependent on strong and direct links between the firms and academic research. The new ‘external’ scientific knowledge produced by the academy has to be quickly transferred by the industrial enterprises into the applied research and development. Therefore, the access to external
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Monika Petraite knowledge from universities and public research institutions is a critical factor of innovation. Innovation thus means the commercial use of a type of knowledge that often is at the edge of state-of-the-art, and which comes from other organizations, i.e. not from the business enterprises. It implies an internal capacity of firms regarding the absorption of external knowledge and discoveries produced by those organizations, as well as some internal investments in basic research (Rosenberg, 1990). Technological opportunities are particularly high and persistent. The scientific advances are opening a wide scope of opportunities for the new product development (Marsili, 2001). Product (and process) design is thus based on the commercial exploitation of a cluster of scientific results belonging to related but often distinct disciplines; however, the differentiation of the knowledge base is relatively low, as compared to the ‘complex systems’ or ‘product engineering’ sectors (Rosenberg, 1990). Both the ‘complex systems’ or ‘product engineering’ and the ‘science based’ technological innovation regimes require high levels of networking and partnerships, and highly depends on the absorptive capacity of the innovating firms. Thus the concept of technological innovation, regardless of its type, should be supplemented with the dimension of networking and partnership. As a result of this concept analysis, we define technological innovation as the interaction of R&D and entrepreneurial dimensions, executed in the networks of knowledge creating organizations. The type and direction of this interaction is defined by the institutional context or NIS.
3. Empirical data collection and analysis methodology In order to achieve the research goal the method of survey-in-written was chosen. The research instrument was designed by referring to the aspects of organizational design determinants supporting knowledge integration in innovation processes. Likert scale of five levels was chosen for measurement of variables. The method of mixed sample formation was chosen. The business organizations were identified according to their activity sector. In another step in the selected quotas certain organizations, which had to meet the requirements identified in the quotas by means of expert selection: to belong to business sector and demonstrate some of innovation activities. In these organizations the respondents were selected by expert selection method. The criteria that determined getting into the sample were: 1) a respondent works for this organization, 2) the respondent‘s work profile is that he/she has the expertise on innovative activity in the organization. In total in the research 290 of the respondents participated. The logic of empirical data analysis and application of analytical tool sequences was determined by the goal of our research – identification of impact of organizational design determinants on integration of knowledge in innovation processes by the knowledge type, i.e. R&D based, and experience based. The second determinant was the research instrument that constituted of large sets of organizational dimensions and characteristics, as retrieved form the literature analysis. And last but not least determinant was the logic of empirical data collection that was based on the sample of innovative organizations from the NACE rev. 2 business related sectors (i.e. C to N). Based on these determinants, the data analysis methodology was chosen, which followed three steps: first, data condensation via factor analysis, second, search for statistically important relationships among factor characteristics via correlation analysis. Research instrument constituted of 9 large organizational dimensions research blocs, number of characteristic measured within each block varied from 13 to 44. Likert scale was chosen for the evaluation of each factor (1 – strongly disagree, 3 – no opinion, 5 – strongly agree), which was transferred into SPSS data sets. For our analysis only knowledge integration into the innovation process related factors and characteristics were chosen. In the first step of data condensation via factor analysis, the factor wage limitation p > 0.5 was introduced. KMO of each factor block varied from 0.902 to 0.770. 33 significant factors were condensed from the factor analysis, which allowed linking of tested and internally correlating organizational characteristics in to the meaningful factors. As a result, factor analysis provided theoretical framework of organizational design dimensions with qualitative meanings that are presented in the Table 1.
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Monika Petraite Table 1: Organizational design factors supporting R&D and knowledge flows in business organizations
Profile or organizational infrastructure
Organizational communication Knowledge and learning processes for innovation
Factor title
Number of statements
KMO
Factor loadings (max-min) 0,802-0,587 0,819-0,617
Participation (of users and employees) facilitating IT platform Effective and flexible information and innovation experience management system (Explicit knowledge for innovation management system) Creativity and cooperation facilitating physical organizational infrastructure (Facilitating tacit knowledge management for innovation)
3 4 4
0,815-0,633
Innovation partnership supporting IT and physical infrastructure (Networked knowledge management infrastructure Effective system of innovation activity communication
3
0,854-0,62
R&D knowledge development for innovation
6
0,888-0,788
Individual and group based innovation competence development Learning for innovation supporting organizational systems Absorption of internal and external innovation experiences Enabling learning results for innovation Development of strategic innovation competencies internally External sourcing for innovation ideas Support for creativity for innovation Internal experience based learning for innovation Routine based and customer oriented organizational learning
5 4 5 2 3 3 3 2 2
0,763-0,56 0,766-0,566 0,75-0,51 0,756-0,718 0,774-0,511 0,85-0,565 0,687-0,487 0,819-0,758 0,717-0,606
0,774
15
0,902
0,782
0,845-0,526
The universal empirically retrieved organizational design determinant constituting knowledge integration in innovation processes; provide us with the universal design for innovation profile, which is to be tested across various types of knowledge with the next step in search for important relationships within each factor. However, important shared knowledge enabling for innovation design characteristics in business sector organizations are: ď&#x201A;§
Organizational infrastructure related determinants: participation (of users and employees) facilitating IT platform; effective and flexible information and innovation experience management system; creativity and cooperation facilitating physical organizational infrastructure; Innovation partnership supporting IT and physical infrastructure;
ď&#x201A;§
Organizational communication: effective system of innovation activity communication;
ď&#x201A;§
Knowledge and learning processes for innovation: R&D knowledge development for innovation, individual and group based innovation competence development, learning for innovation supporting organizational systems, absorption of internal and external innovation experiences, enabling learning results for innovation, development of strategic innovation competencies internally, external sourcing for innovation ideas, support to creativity for innovation, internal experience based learning for innovation, routine based and customer oriented organizational learning.
With the next step correlation coefficient (r ) between the extracted organizational design determinants were calculated. Despite the fact, that the strong correlation was not identified, the analysis was carried out on the base of agreed correlation strength within each factor, which allowed to monitor certain tendencies on the impacts of organizational design determinants for R&D and knowledge flows for innovation.
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4.
Organizational design characteristics enabling R&D and experience based knowledge integration into the innovation processes
From the knowledge theory, the R&D based knowledge can be basically defined as an explicit, empirically tested and „approved „knowledge for innovation, that is codified in explicit form and by nature is easy transferred among innovation agents. However, according to the Gibbons Mode 2, it involves high costs of learning by the recipient because of the diversity of the knowledge bases between the knowledge provider and the recipient. Another important restriction is related to the R&D based knowledge involvement costs, which are tightly linked to the general R&D knowledge development and acquisition strategies (internally developed, not IPR protected, internally developed, IPR protected, externally developed, not IPR protected, externally developed IPR protected) and the knowledge absorption costs, which companies are attempting to reduce via the organizational processes integrated in the organisational design The development of R&D knowledge for innovation is tightly linked with the general business strategies and their attitudes towards openness in R&D intensive processes. The organizational design factors that determine the integration of R&D based knowledge into the innovation process are related to the physical and „soft „infrastructures, including the participation enabling ICT platform, and innovation related experience management. The R&D based knowledge and it‘s integration into the innovation processes was tested in relationship to the innovation participation enabling IT platform, effectiveness and flexibility of innovation experience based knowledge management platform, cooperation and creativity enabling organizational physical infrastructure, and innovation partnership and cooperation enabling „soft“ infrastructure. The R&D based knowledge integration into the organizational innovation processes is positively related to the innovation participation enabling information technology platform, but the importance of certain features vary in relation to the mode of R&D based knowledge acquisition in the organization. The key variables tested were related to various tools that organisations use for the acquisition and development of R&D based knowledge, i.e. internal R&D, joint R&D with universities and R&D institutes, involvement in PhD development with universities, co-development of patents and scientific publications, participation in scientific and industrial conferences, including speeches and presentations. The integration of internally developed R&D knowledge was positively related to such innovation participation enabling IT platform characteristics as organisational IT supported platform for the group work, and organisational information systems that allow customer participation in innovation activities, and also to the availability of specific IT tools for new idea generation and systematisation. The integration of R&D knowledge jointly developed with universities and R&D institutes was positively related to organisational IT supported platform for the group work, organisational information technology systems that allow customer participation in innovation activities, and also to the availability of specific IT tools for new idea generation and systematisation. The integration of R&D knowledge obtained via the development of new PhDs together with universities was positively related to such innovation participation enabling IT platform characteristics as organisational IT supported platform for the group work, and organisational information systems that allow customer participation in innovation activities, and also to the availability of specific IT tools for new idea generation and systematisation. The integration of R&D based knowledge gained from scientific publications and patents into the innovation processes was positively linked to the organisational information systems that allow customer participation in innovation activities, and also to the availability of specific IT tools for new idea generation and systematisation, which also played a moderate role in integration of knowledge gained from other publications. The integration of R&D based knowledge gained from participation in various conferences including both – scientific and industrial in the innovation process is positively related to the availability of specific IT tools for new idea generation and systematisation, and organisational information systems that allow customer participation in innovation activities. The organizational IT tools supporting employee participation in innovation activities were weakly related to the R&D based knowledge integration into the innovation processes. The characteristic of internal and external innovation experience management system was not related to any type of R&D based knowledge integration into the innovation processes, but one. The support to new idea
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Monika Petraite generation in external and not related to work environment had positive effects on internal R&D and external industrial and scientific knowledge knowledge integration. While analysing the link between the integration of R&D based knowledge integration in innovation processes and physical infrastructures for creativity and partnership in an organisation, and testing the relationship between the convenience of organizational spaces for creative wok, collaboration, circulation among employees, and new idea generation, and the various profiles of R&D based knowledge, only the availability of physical spaces dedicated to new idea generation showed stronger positive impact, especially for knowledge gained and developed via patents and scientific publications, joint R&D with universities and R&D institutes, internal R&D and knowledge gained from the scientific and industrial conferences, and also knowledge gained via joint development of PhD‘s. Organisational spaces that support circulation and communication among various employees play role in supporting the successful integration of internal and joint R&D knowledge in innovation processes, while improvement of organisational spaces to support collaboration plays a role in integration of internal R&D based knowledge. Experience based knowledge by its nature is tacit, embedded in organisational know how, routines and cultures, in most cases related to individual contexts of operation. Despite of it’s tacit nature, capturing and integrating experience based knowledge in organisational innovation processes is very important, especially while developing market driven innovations and diversifying among various innovation contexts. The experience based knowledge also leads to the development of hardly replicable organisational core competencies in innovation, “modus operandi” of an innovative organization, which distinguishes it from other actors in the field. Still, capturing internal and external experiences, and especially, integrating them into the innovation processes is not an easy task from the organisational design perspective, as, differently from the R&D based knowledge, experience based knowledge resides in processes, organizational routines, organizational artefacts and so on. The following variables of experience based knowledge integration in innovation processes were tested in relation to organisational design variables: capturing of external and internal experiences as gathered from capturing and storing of internal experiences for knowledge reuse by other departments and employees, by learning from other organisations experiences, and sharing own experiences with other organisations, and finally, capturing experiences from the diversity of activities, not directly related to business activities of an organisation, and integrating them in the innovation processes. Internal experience based knowledge integration into innovation processes depends on organisational capability to capturing, storing mad reusing internal experience based knowledge for innovation and improvement activities. Organizational design components that support the integration of entire knowledge in innovation processes are related to the innovation participation supporting IT platforms, and namely the availability of convenient team work platform and idea generation and systematisation tolls, organisational communication system characteristics, such as easy to use and flexible information distribution system, access to desired information as related to innovation activity, support to idea generation in out of office environments, and also the work environment, that enables cooperation and partnership among employees. The innovation partnership enabling infrastructure still plays a crucial role in integration of internal experience based knowledge in to innovation processes as compared to other organizational design components, i.e. collaboration with partners, and storing and sharing information as related to partnerships. Integration of experience based knowledge as gathered from other organisations is supported by convenient team work platforms, easy to use and flexible information distribution system, access to desired information as related to innovation activity, access to information about previous innovation projects support to idea generation in out of office environments, and also the work environment, that enables cooperation and partnership among employees. The innovation partnership enabling infrastructure plays a crucial role in integration of external experience based knowledge in to innovation processes as compared to other organizational design components, i.e. collaboration with partners, and storing and sharing information as related to partnerships. Learning from various life experiences is strongly supported by physical and face to face communication supporting infrastructures.
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Monika Petraite Table 2: Organizational design characteristics enabling R&D based knowledge integration into the innovation processes Involvement Internal Joint R&D Scientific in PhD R&D with publications development universities, and patents with R&D institutes universities Innovation participation enabling IT platform Organisational IT 0,326 0,312 0,21 0,199 supported team work platform Specific IT tools for new 0,288 0,217 0,236 0,217 idea generation and systematisation Organizational IT 0,321 0,24 0,253 0,269 platform enabling customer participation in innovation and improvement activities Organizational IT tools 0,058 0,155 0,034 0,39 enabling employee participation in innovation and improvement activities Effectiveness and flexibility of innovation experiences management system Easy to use and flexible -0,016 0,058 -0,101 -0,037 information distribution and communication system Access and easy reuse of 0,07 0,113 0,018 0,03 knowledge on previous innovation projects Easy access to the 0,031 0,079 0,004 0,001 information related to innovative activities Support to new idea 0,197 0,256 0,210 0,219 generation in out of office environment
Other publications
Participation in variuos conferences, including presentations
0,173
0,173
0,101
0,326
0,224
0,318
0,05
0,133
0,003
-0,03
0,062
-0,104
0,197
0,022
0,116
0,255
Creativity and partnership enabling physical infrastructure of an organisation
Convenience of physical spaces for creative work Improvement of organisational spaces to support collaboration Organisational spaces that support circulation among various employees from different projects and departments Physical space dedicated for new idea generation
-0,018
0,061
-0,021
-0,003
0,092
0,015
0,149
0,211
0,086
0,123
0,204
0,183
0,042
0,206
0,196
0,217
0,171
0,139
0,286
0,333
0,361
0,384
0,139
0,33
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Monika Petraite Table 3: Organizational design characteristics enabling experience based knowledge integration into the innovation processes Capturing and storing of learning experiences for knowledge reuse by other departments and employees
Capabilities to learn from other organisations experiences
Innovation participation enabling IT platform Specific IT tools for new idea generation and 0,253 0,18 systematisation Organizational IT platform enabling 0,164 0,214 customer participation in innovation and improvement activities Organisational IT supported team work 0,277 0,273 platform Organizational IT tools enabling employee 0,069 0,206 participation in innovation and improvement activities Effectiveness and flexibility of innovation experiences management system Easy to use and flexible information 0,357 0,298 distribution and communication system Access and easy reuse of knowledge on 0,282 0,275 previous innovation projects Easy access to the information related to 0,374 0,345 innovative activities Support to new idea generation in out of 0,26 0,272 office environment Capturing and Capabilities storing of learning to learn from experiences for other knowledge reuse by organisations other departments experiences and employees Creativity and partnership enabling physical infrastructure of an organisation Convenience of physical spaces for creative 0,154 0,177 work Improvement of organisational spaces to 0,046 -0,034 support collaboration Organisational spaces that support 0,131 0,063 circulation among various employees Physical space dedicated for new idea 0,251 0,312 generation Organisational structures enabling partnership Organisational information systems to 0,301 0,35 support collaboration with partners Organisational IT system supports the 0,266 0,419 storage and exchange of information with partners Easy and convenient access to colleagues 0,008 0,266 within the work space
Sharing innovation experiences with other organisations for capability building purposes
Learning from various life experiences, not directly related to work
0,103
0,119
0,196
0,166
0,048
0,241
0,122
0,105
0,247
0,243
0,19
0,083
0,343
0,073
0,312
0,123
Sharing innovation experiences with other organisations for capability building purposes
Learning from various life experiences, not directly related to work
0,204
0,253
0,046
0,156
0,047
0,162
0,152
0,12
0,197
0,212
0,153
0,213
0,178
0,293
5. Discussion and conclusions As it comes from the results of analysis, the successful integration of R&D based knowledge is linked to the organisational IT infrastructures, but also vary according to the mode of R&D based knowledge acquisition. The integration of R&D based knowledge, where the firm takes active development role, i.e. internal R&D, joint R&D and involvement in new research via human resources circulation between academia and business (i.e. new PhD development), the organisational IT platforms for the team work, new idea generation and
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Monika Petraite systematisation were important, was well as the organization IT platforms for customer participation in innovation and improvement activities. I.e., the integration of original new developed R&D knowledge in the innovation process requires the linkage between the R&D based knowledge development, and the lead customer knowledge, as supported via the collective team work and idea generation platforms. When the firm acquires external R&D based knowledge without higher levels of active levels involvement, i.e. via scientific publications, patents, and scientific and industrial conferences, the importance of IT supported team work platforms is reducing, but the idea generation and systematisation tools and customer participation platforms still play a role. The organisational IT tools allowing employee participation in innovation activities do not contribute to the processes of R&D based knowledge integration into the innovation process, while customer participation enabling platforms are of critical importance for the success of R&D knowledge integration into the open innovation processes. It could be explained by the R&D driven innovation profile, which requires high levels of novelty, and seeks to link market opportunities with the new knowledge, while the contribution of employees is more important in the improvement activities and experience based knowledge application for innovation. The latter type of knowledge integration is much more sensitive to organizational spaces, team based activities allowing transfer and exchanges of tacit knowledge, and an experimentation. The analysis results in findings that a) various types of knowledge, that play important role in innovation development, are established via different organizational design elements, and thus, certain design elements might work well for certain types of knowledge, but fail for some other, b) integration of different types of knowledge (i.e. R&D, experience and market based) require a specific attention and supporting design elements in for integration of them in the innovation process.
References Chesbrough, H. (Ed.). (2006) Open Innovation: Researching a New Paradigm. Oxford: Oxford University Press. Coriat, B., & Weinstein, O. (2001) The organization sectoral view. ESSY working paper. Coriat, B., Orsi, F., & Weinstein, O. (2002) Science-Based Innovation Regimes and Institutional Arrangements: from Science Based ‘1‘ to Science Based ‘2’ Paper presented at the DRUID Summer Conference on ‘Industrial Dynamics of the New and Old Economy - who is embracing whom?’ Copenhagen/Elsinore 6-8 June 2002. Drucker, P.F. (1993) Innovation and entrepreneurship: practice and principles. New York: Harper Business. Herzog, P., Leker, J. (2007) Open vs. Closed Innovation strategies – also different innovation cultures? Paper presented at the R&D Management Conference, 4-6 July, 2007Bremen. Jakubavičius, A., Strazdas, R., ir Gečas, K. (2003) Inovacijos. Procesai, valdymo modeliai, galimybės. Vilnius: Lietuvos inovacijų centras. Karagozoglu, N., & Lindell, M. (1998) Internationalisation of Small and Medium sized Technology –based firms: An exploratory study. Journal of Business Management, 36 (1), 44-58. Laamanen, T., & Autio, E. (1996) Dominant dynamic complementarities and technology-motivated acquisitions of new, technology-based firms. International Journal of Technology Management, 12, 7/8, 769-786. Marsili, O. (2001) The Anatomy and Evolution of Industries. Cheltenham and Northampton: Edward Elgar. Pavitt, K. (1984) Sectoral patterns of technical change. Research Policy, 13, 343-373. Rosenberg, N. (1990) Why do firms do basic research (with their own money). Research Policy, 19, 300-323. Rothwell, R. (1994) Towards the Fifth-generation Innovation Process. International Marketing Review, 1(11). Schumpeter, J.A. (1934) The theory of economic development. English translation. Cambridge: Harvard University Press. Tidd, J., Bessant, J., & Pavitt, K. (2008) Managing innovation. UK: John Wiley. von Hippel, E. (1988) The Sources of Innovation. New York: Oxford University Press. West, J. & Gallagher, S. (2006) Challenges of open innovation: the paradox of firm investment in open-source software. R&D Management, 36, 3, 319-331.
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The Role of Rational, Emotional and Spiritual Knowledge in Customer Relationship Management Carmen Petrisoaia and Nicolae Al. Pop The Academy of Economic Studies, Bucharest, Romania carmen.petrisoaia@yahoo.com nicolae_al_pop@yahoo.com Abstract: This paper aims to identify the role held by rational, emotional and spiritual knowledge in customer relationship management. Through an exploratory, qualitative research in the insurance industry, we intend to obtain a model that reflects the importance of the three aforementioned types of knowledge in the process of building profitable, long term relationships with the organizational customers. Although studies have already been carried out on relationship marketing and customer relationship management in the organizational context, they are considerably fewer than the ones dedicated to the individual consumers. The differences between the two types of customers and the significant share of the organizational trade from the global GDP underline the necessity of a rigorous approach of organizational customer relationship management, from the point of view of relationship marketing strategies and also information technology. Knowing the customer is one of the fundamental components of customer relationship management, along with the relational strategy, the communication and the proposal of individualised value. In the case of CRM, the rational knowledge could be associated to the marketing information system made by data warehouses (global internal data), data marts and external data such as mega bases, panels, market studies and competition. The emotional and spiritual knowledge can be related to the attachment created between the organizational customers and the sales agents (in some cases the key account managers). Previous studies have shown that the departure of the most appreciated and well trusted sales agents determined profit loss and customer defection. Also, emotional knowledge can be connected to the social and cultural characteristics that generate a certain specificity of the buying behaviour. Hence, the three types of knowledge subscribe to customer relationship management and their recognition and adoption by the insurance companies represent a key step in order to provide long term profitability. Keywords: CRM, rational knowledge, emotional knowledge, spiritual knowledge, insurance, organizational customer
1. Introduction The aim of this paper is to obtain a CRM model applicable in the business‐to‐business setting of the Romanian insurance market. In the last few decades, the CRM concept was intensely analysed and developed in the scientific context as well as among practitioners from numerous industries (Peelen et al. 2009b). Although the literature offers various models describing the CRM approach with its multiple dimensions, only a few of them imply knowing the customer from a broader perspective. The academic papers distinguish between tacit knowledge, rooted in past experiences and represented for instance by the customized approach of each customer and explicit knowledge, articulated, codified and communicated through symbols and language (Polyani, in: Alavi and Leidner, 2001). Based on the Romanian insurance market’s coordinates, where the penetration degree was of only 1.77% in 2008, a relatively low level compared to other EU states (Petrescu, 2012), the study builds a CRM model applicable by the insurance companies in the business‐to‐business context. In order to achieve this objective, several models previously published were analysed. The elements considered suitable for the insurance industry were selected and two new components from the customer knowledge area were included: the emotional knowledge that requires interpersonal skills and the spiritual knowledge that interferes with the organizational and the national culture of the business customers. The two constructs can be associated with the tacit knowledge and they are consistent with the relationship marketing characteristics. The model that resulted was submitted for validation to 12 insurance practitioners. They found resemblances between the conceptual model and the actual CRM implemented processes, but they also brought some suggestions for improvement.
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2. Literature review 2.1 CRM Over the past decades, CRM enjoyed a special attention, in the academic perimeter (Plakoyiannaki, 2005, Henneberg, 2008), but also amongst practitioners. From the 45 CRM definitions identified by Zablah et al. (2005) in the academic literature, one can deduce the lack of a joint conceptualization concerning the CRM dimensions. Moreover, the perception differences identified in practice have an important impact over the results generated by the implemented strategies (Payne and Frow, 2008). According to a brief definition, CRM reunites the potential of the relationship marketing strategies and the information technology in order to create long‐term mutually profitable relationships with customers and other stakeholders (Peelen et al, 2009b). The existing customer’s retention and building relationships with new ones can be possible in the contemporary competitive environment through excellent service by identifying customers and building an information database about them (Thakur and Summey, 2010). Peelen et al. (2009a) consider that knowing each customer is essential for a customized offer proposition and it represents one of the CRM components along with the relational strategy, the communication and the individualized value proposition.
2.2 Knowledge The notion of knowledge has been a subject of debate since the Greek philosophers’ era, and nowadays there are three key elements that ought to be differentiated: data – raw numbers and facts; information – processed data; knowledge – authenticated and personalized information (Alavi and Leidner, 2001). In an organization, knowledge can be shared through electronic documents and personal interactions, generating different costs and benefits (Haas and Hansen, 2007). Knowledge is the currency of the interactions between the organization and its suppliers, customers and other partners and, when efficiently exploited it can become a competitive advantage (Carlile and Rebentisch, 2003). Alavi and Leidner (2001) created a taxonomy and distinguished several types of knowledge: tacit, explicit, individual, social, declarative, procedural, causal, conditional, relational and pragmatic.
2.3 Rational knowledge Knowing the customer through data is possible by: identifying his coordinates (name, address, phone number, email address), the market segment, the affiliation to a certain group and subgroup, the preferences for a particular communication channel, previous transactions and the history of communication in order to avoid redundant dialogs and establish point of reference (Peelen, 2005). The birth of information technologies starting with 1990 has enabled the organization of CRM activities (Sun, 2006), through the creation of a marketing information system (MIS) composed by data warehouses, data marts and external databases such as mega bases, panels, various market studies (Badoc and Trouillaud, 2004). The extraction of new, easy to use and useful information from the abovementioned warehouses in order to support the decision‐making process is called data mining. This activity can help to revolutionize marketing through: customer defection and attraction analysis, customer migration, customer approach, response analysis, customer loyalty, cross‐selling, segmentation and niche market, distribution and communication channel analysis (Gamble et al., 2008). On this topic, Peppard (2000) writes that insurance companies have the tendency to gather ample information about customers and prospects, without using it because of the organizational culture, traditions, job protection, thus neglecting the fact that they have a competitive advantage and a cost reduction source. The data mining techniques allow analysing segments of valuable customers and building a forecast model that indicates on one hand the future profitable customers, and on the other hand the customers more likely to become debtors (Gamble et al., 2008).
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2.4 Emotional knowledge Emotional knowledge gets formed through the analysis and the understanding of complex emotions (Mayer and Salovey quoted by Kidwell et al., 2011). The sales people that use emotions in the marketing exchanges can perceive customers’ feeling easier and know what emotions they should show towards them (Kidwell et al., 2011). Empathy has a cognitive component (perspective‐taking) and an emotional one (empathic concern). An essential element for sales people, perspective‐taking represents the ability to understand and anticipate thoughts, feelings and actions in order to comprehend and satisfy the needs of each individual customer (Widmier, 2002). In a study carried by Biggerman and Buttle (2011) they identified four values of the relationships between organizations, the first one being the personal value. The general managers who were interviewed in a qualitative research attributed value to relationships based simply on the likes and dislikes manifested towards the business partners. The personal value can determine the preservation of a relationship regardless of its problems, even though in other cases it would have been terminated. Relationship marketing, whose key component is CRM (Pop and Petrescu, 2008), underlines the three essential features of contemporary marketing: empathic thinking and action, relational creativity and partnership. (Schebesch and Pop, 2013) They contribute to attaining the objective of the relationship marketing approach: gradually winning the trust of the actors involved in the exchange process (Schebesch and Pop, 2013). Trust represents an important relationship determinant, contributing to their development and reducing perceived risk (Egan, 2004).
2.5 Spiritual knowledge In the hereto paper, spiritual knowledge is considered the knowledge of the cultural background manifested in the buyer‐seller dyad. Culture is the collective programming of the mind that distinguishes the members of a group or a category from the others (Hofstede, 2001). For that matter, culture represents one of the eight components of relationship marketing, and, when the cultural differences are overcome, they create value (Gordon, 1998). The organizational culture is a phenomenon in itself, differing from the national cultures simply because individuals were not born within the organization, they hold an influence over the others and their involvement is limited to the amount of time spent as organizational members (Hofstede et al., 2010). Griffith and Harvey (2001) developed an intercultural communication model meant to improve business‐to‐ business relationship quality through four distinct constructs: cultural understanding, communication competence, communication interaction and cultural interaction. Cultural understanding is a set of abilities and cultural knowledge usually based on past experiences that allow an appropriate interaction with other national and organizational cultures. Communicational competencies are a set of abilities and knowledge, related to communication, enabling relevant communication with partners of a cross cultural network. Communicational interaction involves hybrid communication strategies resulted from the development of a new communication culture. Cultural interaction relates to the adjustment of the national and organizational cultures that take place due to the cross cultural communication within the network (Griffith, Harvey, 2001) The organizational structure influences both culture and the decision‐making process. In the case of a single owner, organizational culture will evolve around their character and style. When there are at least two owners, the management structure changes, because decisions have to be approved unanimously (Young, 2005).
2.6 Insurance industry Unlike other European states, Romania doesn’t have a century‐old tradition in the insurance industry, and, despite the last two decades’ progresses, the gap is still present because the insurance development is correlated to the economic evolution (Ciurel, 2011). Although the insurance need is significant, the demand is still low due to several causes: economical, social, cultural, and educational (Ciurel, 2000). Because during five decades, between 1952 and 1990, a state‐owned insurance company held the monopoly on the insurance market, the Romanians got used to get their losses entirely covered by the state, and adapting to charged protection proves to be difficult (Constantinescu, 2004).
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Carmen Petrisoaia and Nicolae Al. Pop Hofstede’s diagram concerning power distance versus uncertainty avoidance (Hofstede et al, 2010) situates Romania in the high power distance and high uncertainty avoidance category together with countries such as Russia, Croatia and France. The organization's needs for insurance are similar to those of the individual consumers, but they have a wider dimension, often influenced by its sphere of activity (Ciurel, 2011). Depending on their target, insurance companies can have a dedicated department that deals with the organizational customers (Galiceanu, 2004). Several authors showed that the structure of each organization reflects on its buying behaviour (Young, 2005, Soopramanien et al., 2010). Firstly, if a buying centre exists within the organization, one individual may occupy one or several of the following roles: user, influencer, buyer, decider and gatekeeper and that will have an impact on the services’ evaluation criteria (Homburg and Rudolf, 2001). Secondly, in a small company it is common that one single individual is in charge with buying the insurance. Therefore, it is likely that he will behave like an individual consumer and it can be stated that the organizational buying behaviour in small companies is strongly influenced by the characteristics of the individual consumer (Soopramanien et al.,2010) and implicitly by the national culture. These characteristics of the organizational market enhance the importance of a complete knowledge because the Romanian insurance companies must adapt to different subsets of organizational customers and individualize as much as possible their approach.
3. Methodology In a general setting, a model is a simplified design allowing the comprehension of something that is too complex to be understood (Hofstede, 2001). An analytical model is a group of variables and their correlations conceived in order to represent partially or totally a real process or system. Models can be verbal if they imply a textual description, graphic if they are spatially represented, mathematical when the relationships between variables are expressed through equations (Malhotra et al., 2011) and conceptual as diagrams suggesting possible causal relationships (Schmidt and Hollensen, 2006). Before building a model: the decision making managerial utility should be evaluated, a purpose must be established, the degree of complexity and the model’s applicability have to be assessed (Leeflang and Wittink, 2000). The literature review allowed the identification of seven CRM conceptual models that will be further analyzed. 1 Gartner (Buttle, 2010): the first two stages of this model, vision and strategy are fundamental, being oriented towards the identification of objectives and segments as well as towards leadership and value proposition. The next two stages are focused on customer experience and organizational collaboration. They are strongly connected because understanding the customer’s needs, cooperation and feedback depend on the organizational culture, the employees’ personal skills, the communication between employees, partners and suppliers. The last four dimensions articulate around processes, information, technologies and CRM measurement, the key components for profitability and customer satisfaction. The model defines CRM as a principle of organizational efficiency. The rational intelligence is represented through the sixth dimension, Information – data and analyses. One condition for effective information exploitation is a joint vision and a solid infrastructure. The dimension named „Valued customer experience” is based on understanding the customer’s needs, collaboration and feedback. 2. IDIC (Peppers and Rogers, 2004): The model develops in cascade, following four consecutive stages: customer identification and understanding, customer differentiation ‐ behaviour and needs segmentation, effective customer interaction, customized offer and communication. Customer knowledge becomes a conducting wire active in all the four stages of the. Hence, an extended knowledge is intended, but other information is missing regarding the instruments and key variables involved in the CRM processes. 3. QCI (Woodcock et al., 2002): The model has components common to the Gartner vision, but they are not hierarchically structured, but rather represented in interaction, like a mechanism: customer experience, processes management, technical support, customer information, people and measurement. Several original
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Carmen Petrisoaia and Nicolae Al. Pop elements are used such as the external environment and the core of the model represented by the customer management activity developed around the market penetration, acquisition and effective customer retention. The model is evidence that the CRM approach relies, among others, on customer information, processed by the employees and the partners. Meeting and knowing customers is one of the values explicitly adopted by the QCI model. 4. Thakur, Summey (2010): The CRM model gives a special importance to the external environment taking into consideration the entrance barriers and the market diversification. Another major component allowing overcoming competition is the knowledge about customer behaviour including: demographic data, previous buying behaviour, preferences and motivations. Further, customer commitment is built through the capacity to convey value, understand customer’s needs, assuring his loyalty. This CRM approach aims to optimize customer lifetime value for the organization. The external environment is well represented, with the competitive advantage of a CRM approach strongly elaborated. Customer knowledge is seen from a wider perspective, without however covering all its dimensions and without involving the employees. There is no mention of the instruments through which knowledge is exerted and the channels used for transfer. The CRM efforts, such as building customer loyalty, maximising the retention degree and value are not evaluated or qualified in order to assess the model’s effectiveness. 5. Buttle (2010): The model’s objectives proposed by Buttle are customer satisfaction and ensuring long‐term profitability. The vision develops in five stages: customer portfolio analysis, customer intimacy, network development, value proposition, customer lifetime management. The implementation of the stages is determined by leadership and culture, data and information technology, people and processes. The stage called “customer intimacy” refers to customer knowledge beyond the rational part. 6. Payne (2009): The author structures the model in five processes, and four of them take place consecutively: strategy development, value creation, getting the competitive advantage, multi‐channel integration and performance evaluation. Each of the aforementioned steps depends on the information management process. The information management process allows building information about the customer, but the model only favours rational knowledge. 7. Zablah et al. (2004): This model identifies two different processes permanently linked: the information management process (through data collection, obtaining and disseminating information) and the interaction management process where all the exchanges between the customer and the company are included. Although it doesn’t explicitly contain the notion of knowledge, the model synthesizes the idea that the interactions take place on several levels, enabling a customers’ evaluation and a prioritisation.
4. Results From the seven models previously presented, the rational customer knowledge is described as a stage in a sequence, a part of a process or even a process by itself. This type of knowledge relies on the information architecture exploited by the company’s members. However, only in the models that explicitly involve people and customer experience, one can identify emotional and cultural dimensions. The model suggested (Figure 1) underlines the importance held by all the knowledge stages within the CRM approach. In order to create a CRM insurance model, one should take into consideration the industry’s characteristics described earlier. Due to the important influence of the economical, political, social and technological environment, the „external environment” component from the QCI model will be preserved. It is a key determinant of the organizational buying behaviour and the insurance company. Along with the external environment, customer experience enables emotional and cultural customer knowledge by the insurance company members. The latter are sustaining the rational knowledge process facilitated by the information architecture. In this model, company employees attain a holistic customer knowledge, representing the essential element in service delivery and a fundamental resource for the organization (Grönroos, 2007). The planning and analysis stage, identified in the QCI model appears in other models, in various representations: in Gartner through the CRM strategy, in Butte through leadership and culture, in Payne
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Carmen Petrisoaia and Nicolae Al. Pop through the strategy development process. Further, the customer proposition corresponds to the value proposition from the Gartner and Payne models. Next, the customer management activity follows the same coordinates developed by the QCI model, specifically: market penetration, retention and acquisition, and, in the end, the measurement takes place.
Figure 1: CRM model applicable in the insurance industry The model was submitted for validation to a group of 12 managers activating within the insurance Romanian companies. The chosen research method was the semi directive interview. The participants appreciated the use of the three‐ dimensional organizational customer knowledge within the CRM approach, because service customization cannot be performed exclusively by means of the marketing information system. The analysis and planning performed by the superior management in the headquarter, as well as the offer proposition involving the local units are all based on this key construct. According to some respondents, organizational customer knowledge proves to be difficult and complex due to the buying centres, formed by several members involved in the decision making process. Although the national and the organizational culture are common in a buying centre, the individual particularities shouldn’t be neglected because of their influence over relationships. An important comment was that small companies usually ask for low price offers and aren’t very responsive to cross‐selling actions. This subset of organizational customers tends to be quite reluctant when it comes to insurance policies. The statement formulated by several respondents of our study, clearly sustains Soopramanien’s et al. idea (2010) that the organizational buying behaviour in small enterprises can be similar to the household buying behaviour. Moreover, large companies seem to have a better understanding of the importance held by insurances, but sometimes decision making is delayed by the buying centre’s lack of communication or the existence of multiple representatives delegated to negotiate insurance contracts. Another observation formulated by the practitioners was that the measurement stage offers results that seldom return as data in the information structure in order to be further processed by the company’s members. Hence, this idea was taken into consideration and led to the introduction of a bidirectional liaison between measurement and the information architecture.
5. Conclusions The CRM literature review revealed the need for deeper customer knowledge. This necessity is confirmed by the specific setting of the insurance industry from Romania, with a high development potential, but delayed by economical, social and cultural aspects. In order to facilitate customized service delivery for the Romanian organizational customers, some important elements from previous models were kept: the external environment, customer experience and information architecture. Because organizational buyers have
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Carmen Petrisoaia and Nicolae Al. Pop different evaluation criteria for services, rational knowledge was completed with two new constructs meant to sustain the CRM approach: spiritual and emotional knowledge. On one hand, spiritual knowledge refers to understanding and accepting customers’ national and organizational culture, and on the other hand, emotional knowledge relies on emotions comprehension and the assessment of the role played by them within the business interactions. Insurance companies should use knowledge as a competitive advantage, in order to palliate the reluctance towards insurances observed in small companies and the lack of a coherent dialog determined by multiple representatives in large companies. The hereto study did not develop on elements already described by the academic literature such as: customer management activity with the three dimensions: penetration, retention, acquisition and results measurement. The complexity of these elements requires a more detailed approach within other studies. Furthermore, the effects of this new model over customers’ satisfaction were not assessed, so, further research is necessary to establish the nature of this link. The semi directive interviews’ conclusion was that in the organizational insurance setting it is necessary to implement a CRM model based on the extended customer knowledge beyond the information technology. Hence, the resulting conceptual model aims to help the insurance companies by offering a CRM approach for organizational customers, but it requires a qualitative research in order to be validated. Moreover, it can be submitted to specialists from other industries.
Acknowledgements This work was co‐financed from the European Social Fund through Sectoral Operational Programme Human Resources Development 2007‐2013; project number POSDRU/107/1.5/S/77213 „Ph.D. for a career in interdisciplinary economic research at the European standards”
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Decision‐Making Processes Based on Emotions in Universities as Learning Organizations Magdalena Platis Faculty of Business and Administration, University of Bucharest, Bucharest, Romania magdalena.iordache‐platis@drept.unibuc.ro Abstract: Universities have had an important role in human society, no matter their profiles – technical or humanities, social or medical studies. This is explained by the mission of each higher education institution. In general, the mission statement of any university relates to three main areas – education, research and social involvement which are reflected in priorities, objectives and concrete actions differentiating one university from another. Behind these priorities, there is one universal task common to all universities, which is not so concretely expressed: they educate people! Therefore, many processes in universities are different from similar ones in other type of organizations. Even when a more corporative approach was adopted by university managements, many specific issues were revealed for the simple reason that universities do not produce tangible goods. Rather they deal with knowledge, emotions, people, long term results, competences and learning outcomes. In universities, decision‐making processes do not always consist of having academics or professors as decision‐makers. Sometimes, the students themselves become the decision‐makers. More than professors, students, when they become decision‐makers, are focused more on emotions. In addition, emotions and priorities can be transferred from one generation of students to another. This paper reveals the importance of emotions transferred during teaching in order to understand the decision making processes undertaken by students. It starts from the following statement: ‘students’ education is based not only on transfer of theoretical and practical knowledge, but also on transfer of emotions; students’ behaviours are a consequence of their learning, including emotional knowledge; transfer of emotions affects professionally the students. The main objectives of this paper consists of the following: identifying effects of the transfer of emotions during teaching; analysis of the decision‐making processes adopted by students from the perspective of the influence factors; identifying measures that universities need to take in the area of quality management. The main outcome of the paper consists of identifying a new approach to be applied to quality management in universities through the set of measures proposed for the improvement of the decision‐making processes. Keywords: learning organization, university, emotions and decision‐making process
1. Universities as learning organizations Universities are organizations which combine production factors in order to achieve their objectives, just like any other organization. They use resources, they identify objectives, they measure their activity according to their output in relation to their objectives. Nevertheless, universities do not act like small or medium enterprises or like multinational companies since they provide services to its beneficiaries much differently. Therefore, specialists pay a lot of attention to universities when they debate management approaches. Different types of management – strategic management, human resources management, time management, conflict management, change management etc. have been included in educational activities by higher education institutions. At present, more than in other period of time, the continuous needs of adaptation have transformed universities from passive organizations to active ones. The main reasons to consider universities as active organizations are as follows:
universities continuously develop partnerships not only with other higher education institutions, but also with companies from the public and private sectors;
universities have created different networks and consortia in order to better achieve their objectives, with organizations which share the same main goals or complementary ones;
universities are permanently subject to accreditation and external evaluations, meaning that they have to follow and change specific standards, criteria and indicators from time to time;
universities exist through their students and generations of students change every year through new incoming students;
universities have introduced in their activity principles of quality management which promote the continuous improvement of their processes;
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universities have understood that the connection with the business environment is the method of ensuring sustainability, meaning that new areas of study need to be included in the curriculum or even that new programs or projects are or become more connected to the labour market.
Universities, as active organizations, behave like learning organizations. The concepts of the learning organization as well as its dimensions were explained by Peter Senge in “The Fifth Discipline” (1990). Starting from there, a university as a learning organization is an entity where people continuously develop their capacity for achieving the needed results, where people learn to work together sharing common aspirations. According to Peter Senge, the dimensions of a learning organization are: systems thinking, personal mastery, mental models, shared vision and team learning. These all characteristics can be easily observed at universities:
Systems thinking → universities consist of several entities, such as faculties, departments, research centres, and administrative offices. They all are parts of one single body which is the university. Faculties may have different missions as they may develop educational and research programs in different fields from exact sciences to humanities or social‐economic areas, but all their missions are integrated in the general institutional mission of the university. Different structures have their own strategic plans and operational plan, but they are also integrated in the institutional ones. A strong connection among these components exists, each entity having its own importance in an institutional orientation.
Personal mastery → universities are active through their employees’ work. They invest effort to better understand their tasks and to discover opportunities and challenges for a better performance. In other words, professors, researchers and administrative staff who are responsible of certain operations and activities or processes know how to learn from their own or others’ previous mistakes and act better, which will generate professional and personal development.
Mental models → university staff have the ability to build mental models on future actions. Through communication, each construction of such a model is questioned and subject to change. In comprehensive universities, where faculties or schools reveal different fields, the ability to identify more complex approaches to mental models is higher than in universities where all faculties are parts of the some field. In other words, the different backgrounds of the staff contribute to a better understanding of the reality and more chances to successfully overcome all the difficulties.
Common vision → all the members of the academic community share the university vision and therefore, the university becomes more efficient in the learning process and several strategies are the subject of debates and explanations. Once strategies are adopted and the process of implementation started, many other actions are based on those documents as part of the main strategic goal, as part of the university vision. Understanding the vision is important for the individuals who are expected to be committed to its implementation.
Team learning → universities, like any other organization are larger or smaller from criteria such as: the number of students, the number of academic staff, the number of study programs etc. Team learning is a characteristic of universities, no matter their size. When professors become part of the management team, the team work is not related to the management structure only, but it includes all the learning issues coming from implementation of a decision which was made. In other words, the management team cooperates with and learns from administrative staff – financial, human resources, structural funds, international activities, research and the meeting of social obligations etc. At the same time, administrative staff learns from academics about what teaching and research mean and try to find the best solutions to different situations.
Two main types of team learning can be identified – horizontal or vertical team learning. Horizontal team learning is realized when similar teams cooperate and share their way of doing things, such as different faculty management teams (Dean’s team at the Faculty of Physics, Dean’s team at the Faculty of Letters, Dean’s team at the Faculty of Business etc.) or different project management teams. Vertical team learning is attained when cooperation is developed among different hierarchical levels – Rector’s team, Dean’s team, Director’s team.
Therefore, universities are easily considered learning organizations – see Figure 1.
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Figure 1: Universities as learning organizations
2. Decision‐making processes in universities University management is based on a set of specific management tools interdependently used towards achieving set goals. These tools are part of different types of management, such as in Figure 2.
Figure 2: Types of university management Jensen et al. (2008) mentioned that different empirical research demonstrates the role of cognitive ability and personality factors in academic success. Other studies have started to investigate the role of the emotional intelligences in academic success. Humphrey et al. (2008) developed a research where was demonstrated how leaders’ emotions influences the behaviour and the emotions of their employees. They define the emotional labour and consider that the success can be achieved through it. Through analogy, professors can be considered leaders and students, the employees. Other studies – Boyatzis et al. (2002)‐ show the connections between the cognitive and emotional intelligence on one hand and competences that can be developed to students, on another hand. At the same time, studies ‐Stepherd Dean A. (2004) ‐ reveal the role of emotions in learning from failure. This has generated among professors a large preoccupation on how students feel, instead of how they think.
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Magdalena Platis At present, university management is a system which consists of a set of several combined management methods, tools and approaches. The main approaches of the university management are the following:
2.1 Strategic management Universities act in different national and international contexts. From the strategic management point of view, universities have to understand their own internal environment and to identify the features of the external environment, meaning potential competitors or partners, as well as clients. In this perspective, the main general management functions ‐ to plan, to organize, to coordinate, to command, to control can be identified in universities. Therefore, university management functions are:
Planning → universities have a strategic plan which reflects their main directions in the area of education, research and society involvement.
Organizing → activities need to be developed once the necessary inputs are available; in other words, universities should be prepared before deciding to start a process.
Coordinating → activities need to be clearly understood and be mutually supporting. University management has to establish priorities and to communicate to employees the decisions made.
Commanding → decision‐making processes are based on motivation and involvement.
Controlling → universities need to have feedback from different subject areas in order to know which decisions was worked best.
At the same time, university management is a management of resources, consisting of human resources, financial resources, material resources or time. All the general management functions are applied to resource management; therefore, universities plan, organize, coordinate, command and control related to resources.
2.2 Quality management In all organizations, quality management is not a declaration statement; it is a system which includes institutional structures and procedures of quality assurance. In universities, the system of quality management includes different committees and internal procedures to help the senior management know what works. The main principle of quality management is continuous improvement. At the same time, specific attention is paid to all interested parties, such as students, graduates, the academic community, and potential employers. Promoting the continuous improvement of the processes, quality management is directly connected to change management which means that the university has the capacity to improve the administration of change. At the same time, since the student is considered a client, quality management in university is connected to marketing management or to the way of managing the marketing activity developed in order to attract students.
2.3 Customer relationship management New Marketing promotes a new relationship between the provider of goods or services and the beneficiary. In the case of a university, customer relationship management consists of a new set of strategies to attract the clients for a long term. In the case of a university, this type of management becomes a student relationship management which consists of all activities the higher education institution supports in order to attract the students in the process of continuous education. More than this, activities take place for the students’ benefit, including the performance management of their learning process. At the same time, a good orientation towards students means also an efficient conflict management. This involves a relationship based on mutual respect among parts, whether they are students or part of the academic community. The decision‐making process in universities is characterized by three main components, as in all organizations: decision‐maker, performer and context. In universities, the decision making process is more complex than in a private profit – oriented organization. Main elements of the decision‐making process are reflected in table 1.
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Magdalena Platis Table 1: Features of the decision‐making process No. crt. 1 2 3 4
5
Feature
Private organization
University
Decision‐maker Clients
Individual or participative Individuals or other businesses Occasionally
Individual or participative Students and potential employers Permanent in different Board of Deans or Senate Difficult to be measured
Clients involvement in the decision‐making process Results considered in the decision making processes Clients status at the organization/ university
Measurable (units of production, values etc.) Outsiders
Insiders, members of the university community
In other words, everything universities do directly or indirectly affect the students. During the interactions between professors and students there are many transfers of knowledge (theoretical and practical) plus emotions. In universities, the transfer of emotions is not a purpose or objective in itself. However, in psychology, the process of transfer is very well known. Obviously, during teaching and learning, between professors and students, an important link is defined by the transfer of both knowledge and emotions. Most of the time, knowledge is divided into tacit and explicit. While the first is generated through experience, the latter comes through formal training and education. In a learning organization, such as a university, organizational learning is the process through which the university transmits tacit and explicit knowledge. At the same time, cognitive knowledge is connected with emotional knowledge and can transform from one to another; a process which is interestingly described by knowledge dynamics (Bratianu et all, 2011), including also the conversion between tacit and explicit knowledge (figure 3).
Figure 3: Knowledge dynamics Decision‐making processes reflect the main function of management. For a university, decision‐making processes are based on emotions and on the emotional transfer from professors to students and from students to professors. During teaching, professors transfer emotions to students. The effects of such a transfer are reflected in figure 4.
Figure 4: Effect of the transfer of emotions during teaching
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Magdalena Platis Therefore, in a university, students learn from the emotional transfer and may become better learners. At the same time, in a university, students make decisions based on emotions.
3. Applied research methodology The topic chosen to be demonstrated here is the students’ decision‐making process, especially when they decide who they would like to have as the professor‐coordinator of their final thesis before graduation. The main objective of the research: to identify reasons or arguments the students’ decisions are based on when they decide to choose the professor. Secondary objectives:
To discover the reasons for the students having a higher attendance.
To find out how the students define the concept of an attractive course.
To identify the students’ perception of the professor‐student relationship.
To understand why the students learn with pleasure if they learn through their preferred choices.
After analysing the specific literature in the area of knowledge management and educational management, the applied research consisted of two phases. First phase: to address open questions to students from four different master programs. The questions were the following:
Which are in your opinion the main reasons for the students to have a better attendance at classes?
How would you define the concept of an attractive course?
What are the main features of a good relationship between professor‐student?
What are the active criteria in order for you to learn a subject with pleasure?
The second phase: to create a questionnaire based on the first answers of the students. The answers of the students were included in the alternatives of the questions: Q1: The professor who coordinated my thesis:
was identified by me, based on subjective reasons, being a more preferred professor compared to others;
was chosen by me from a list of less busy professors;
was accepted by me, being one of the last available;
was chosen by me, being the head of the subject in the field I wanted to study;
Q2: During the undergraduate studies, I had participated:
in all classes, being a conscientious person;
more at classes where the topics were of more interest to me;
more at classes taught by a favourite professor;
at classes where I could attend, having other commitments to fulfil;
Q3: Attractive courses during my studies were those at which the professor:
transferred new knowledge;
used interactive methods;
a or b under the condition that the professor spoke with passion and enthusiasm;
a and b, under the condition that the professor spoke with passion and enthusiasm;
Q4: During studies:
I did not prefer any professor;
I was fond of at least one professor;
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I behaved distantly/objectively towards all professors;
I did not identify any behavioural professional models;
Q5: I learned with pleasure when:
I knew that the subject will be useful for my career;
the professor knew how to show me the important parts of the subject;
the professor was associated in my mind with someone close to me;
the professor interacted with me more than with other colleagues;
The distribution of the answers for the previous 5 questions are shown in the table 2. Table 2: The distribution of the answers for the previous 5 questions Q1 Q2 Q3 Q4 Q5
a 58 75 2 2 41
b 12 2 1 85 45
c 8 14 88 4 2
d 15 2 2 2 5
Total 93 93 93 93 93
In addition to this multiple choice test or questionnaire, the respondents were asked to write their thoughts related to two more open questions:
I would like to keep in contact with a particular professor because . . . . . . . .
Professors who better communicate with students are those who . . . . . . . . .
Figures and Diagrams
Number of respondents: 93 students at master programs;
62% of the respondents, meaning 58 students said that the professor was chosen by them on the basis of subjective reasons, being a more preferred professor than others;
80% of the respondents, meaning 75 students declared that they were serious students and that they had participated to all classes;
The concept of attractive course means for most of the students (94% or 88 students) the usage of new knowledge and interactive methods, when the professor spoke with passion and enthusiasm;
91% or 85 respondents said that during their studies they were fond of at least one professor;
48% (45 respondents) mentioned that they learned with pleasure when the professor had known to teach the nice part of the subject, while 44% (41 respondents) learned with pleasure when they knew that the subject would be useful in their career;
Main reasons for keeping in contact with a professor: students learned lessons of life from him or her, and they were linked by memories and emotions; students would like to tell the professor about their success in future careers; students admired the way the professor spoke with passion; the professor might offer a piece of advice or a useful life message; the professor‐student relationship can continue in time as it is a relationship based on respect.
The main expression of a better communicator among professors: use of interactive teaching methods; speaking with enthusiasm; showing teaching based on genuine internal motivation; welcoming feed‐back from students; showing openness and providing concrete examples.
4. Conclusions In contemporary society, considered by many to be a knowledge based society or economy, there is no longer a doubt that universities are learning organizations and active institutions. They are entities with high a propensity towards: systems‐based thinking, performance‐oriented human resource management, mental constructions of the future, shared institutional vision, team work and learning. University management is a co‐ordinated approach of strategic, quality and customer relationship management where students are considered the most important interested part. Therefore, changing strategic objectives, as well as improvements to the quality of academic processes are made for the students’ benefit. Being part of academic
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Magdalena Platis life, students learn to behave according to the university procedures and culture and make decisions that can influence their activities or processes within the university. The relationships between professors and students are a consequence of the transfers among them – of knowledge and emotions which are then transferred into learning outcomes, such as skills and competences. Together these are reflected in the graduates’ behaviour. Sometimes, professors act like managers and students like followers in a class management process. From the applied research point of view, it has been demonstrated that the students have reasons to maintain contact with a professor after they graduate, on the basis of feelings; they would like the professors to be proud of them, of their success in their careers. Therefore, the students see the professor as a mentor to guide them providing advice at different times, when needed. Students define an attractive course on the basis of their perception of the professors’ teaching style. In other words, professors should deilver teaching with passion and not behave like simple providers of knowledge. Subjective reasons – meaning feelings and emotions – contribute to the learning decisions made by students. Analysing the decisions made by students, it is clear that subjective reasons make them act in developing a relationship with a professor. In other words, they prefer professors with high competences in communication. This means emotions. The direct connection between the statements and the achievement of good results are presented in table 3. Table 3: Correlations between the statements and the results No. crt. 1
Statement
Result
Comment
The students’ education is based not only on transfer of theoretical and practical knowledge, but also on transfer of emotions
62% of the respondents had as coordinator of their thesis a professor chosen according to subjective reasons
2
Students’ behaviours are a consequence of their learning, including emotional knowledge
3
Transfer of emotions affects professionally the students
Learning is associated by students with learning with pleasure: 48% of the respondents mentioned that they learned with pleasure when the professors taught nicely the part of the subject 94% of the respondents defined the concept of attractive course by the usage of new knowledge and interactive methods when the professor spoke with passion and enthusiasm
Subjective reasons take into considerations feelings. Since emotions from the psychological point of view means feelings, the majority of the respondents decided to be coordinated based on their emotions. In addition, one og the reasons to keep in contact with a professor is the way he or she interacted with the students: with enthusiasm
This is transfer of feelings and emotions.
At the same time, young generations face a lack of models. Student’s behavior shows that: they do not always know why they should add extra meaning to their studies; they do not trust their own capabilities; they give up easily; they refuse to become involved in extra‐curricular activities Students need models to admire at least for some characteristics, if not for their whole university behavior – see figure 5.
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Figure 5: How can students find models Models can be appreciated only for one single feature, they can be from another profession from his/her own; they can be family, friends, strangers or even imaginary characters. In any case, they contribute to the construction of the young professional; Therefore, universities should: reconsider their priorities; take into consideration both objective and subjective aspects of different issues; invest in training of their professors in interactive methods of teaching; develop activities together with their students; control their own emotions in order to transfer to students only positive ones; reconsider their internal strategies from the real mission of the university: It educates people!
References Andriessen, D. (2004) Making sense of the intellectual capital, Linacre House, Oxford, pp 3, pp 60. Andriessen, D. (2007) Knowledge as love. How metaphors direct the way we manage knowledge in organizations, Proceedings of the 5th Critical Management Society Conference, July, 11‐13, 2007 Bratianu, Constantin et al (2011) Nonlinear Integrators and Intellectual Capital Dynamics, Editura Curtea Veche, Bucharest. Craciun, Bucur‐Matei and Dumitru, Stefan Bogdan (2011) “Knowledge Management – The Importance of Learning Theory”, Journal of Knowledge Management, Economics and Information Technology, Scientific Papers, Issue 7 December 2011, pp 19‐26. Drucker, P. (1992) Managing for the future, Truman Talley/ E.P. Dutton, New York, NY. Griffin, R.W. and Moorhead, G. (2006) Fundamentals of organizational behaviour. Houghton Mifflin Company: Boston. Humphrey, R. H., Pollack, J. M., & Hawver, T. (2008). Leading with emotional labor. Journal of Managerial Psychology, 23 Jensen, Scott, Kohn, Carolynn, Rilea, Stacey, Hannon, Roseann, Emotional Intelligence – A Literature Review http://www.pacific.edu/Documents/library/acrobat/EI%20Lit%20Review%202007%20Final.pdf Klein, Hans‐Michael and Kolb, Christian (2009) Psihologia conducerii eficiente, Editura ALL, Bucharest. Nonaka, I. and Takeuchi, H. (1995) The Knowledge Creating Company. How Japanese companies create the dynamics of innovation. Oxford: Oxford University Press. Shepherd, Dean, A., (2004), Educating Entrepreneurship Students about Emotion and Learning from Failure, Academy of Management Learning & Education Senge, Peter et al (1994) The Fifth Discipline, Field Book New York: Currency Doubleday. Watson, I. (2003) Applying knowledge management: Techniques for building corporate memories, Morgan Kaufmann Publishers. http://www.nwlink.com/~donclark/performance/understanding.html http://www.ualberta.ca/~tfenwick/ext/pubs/lngorgeric.htm http://www.knoco.com/model2.htm www.knowledgedynamics.ro http://www.perfassocinc.com http://www.systems‐thinking.org/dikw/dikw.htm www.km‐forum.org www.markmedia.ro www.gurteen.com www.gartner.com www.infed.ro http://www.studymode.com/essays/Literature‐Review‐Of‐Emotional‐Intelligence‐In‐511274.html
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Inter‐Organizational Knowledge Transfer for Supply Chains in Crisis Stavros Ponis1 and Epaminondas Koronis2 1 National Technical University of Athens, Greece 2 University of Lincoln & George Washington University, UK staponis@central.ntua.gr Abstract: Supply Chains are often required to absorb unexpected pressure, turbulent changes in demand and disruptions across their components. In this paper, we acknowledge both the inter‐organizational and collaborative nature of supply chains as well as their knowledge‐driven processes and aim at exploring how established logistics structures respond to conditions of crisis. We focus on identifying the knowledge management and sharing 'realities', the responsiveness of partnerships and to enrich our existing framework for a knowledge management of crises. Our research indicates that the role of networks and established inter‐organizational practices is critical and therefore three specific actions should be initiated, a) communication needs to be formalized and information exchange should be supported by well‐established and tested practices, b) inter‐organizational relationships need to use pre‐existing knowledge sharing and adaptation processes and c) an effective networked disaster supply chain system must be put in place, initiating and maintaining strong ties across the different parts of the operations. Future research includes the development of a normative framework that would address the processes of inter‐organizational integration for more effective Disaster Supply Chain Management. Keywords: knowledge management, supply chains, crises
1. Introduction Even under normal conditions, the management of a supply chain includes a large number of challenges and problems. These challenges increase in the event of high‐impact crises, disasters and catastrophes that require the immediate increase of the capacity of a system to forecast, assess, plan and deliver under intensive pressure and stress (Gordon Levitt, 2010). Responding to these challenges, in recent publications the role of integrated organizational networks and supply chains has become of critical importance. A growing number of researchers view organizational and societal resilience under a network perspective, arguing that inter‐ organizational networks, communications and coordinated action should be placed at the centre of theoretical and normative development (Quarantelli et al., 2007; Cumming et. al, 2005; Vogus & Sutcliffe, 2007). Moreover, a review of existing studies reveals that substantial theoretical work has been presented on organizational networks (Provan & Milward, 2001; Grandori et al., 2008; Granoveter, 1973), inter‐ organizational communications (Thompson, 1998) and supply chain relations (Balcik, et al., 2010; see also Christopher & Tatham, 2011). These developments currently remain disconnected from the Resilience and the Disaster Management agenda while the distinct field addressed as Disaster Supply Chain Management is in need of more concrete conceptual frameworks and applicable methodologies for the organizations' supply chains integration during the crises handling phase (Van Hassenhove, 2006; Sahin & Robinson, 2002; Hale & Moberg, 2005). Our conceptual considerations begin by analyzing the role of networks in the supply chain crisis management process. As Whybark et al. (2010) put it, unexpected actors and agents interfere in the supply crisis management situations, competing for resources and affecting processes. Crisis Supply Chain Management is not organizational but depends on the development of synergies and inter‐organizational networks (Berkoune et al., 2011). The very nature of networks in this tradition is that organizations are dependent on knowledge management resources and are involved in knowledge exchanges in an effort to reach their goals (Hughes, 2009). Studies have also revealed the knowledge‐intensive character of supply chain disruptions, as information needs to be exchanged, knowledge has to flow across the different logistics operations and such knowledge is characterized by complexity (e.g. Zhang et al., 2002). Unstructured or semi‐structured knowledge transfer processes need to take place under conditions of limited time and psychological pressure. It is precisely the aim of this paper to build on the rich tradition of inter‐organizational networks and supply chain relations and produce innovative conceptual and normative research for the development of more resilient Disaster Management Supply Chains. Previous research conducted has followed this new tradition by analyzing the integration of knowledge with crisis management (Koronis & Ponis, 2012), proposing a Resilience Supply Chain framework (Ponis & Koronis, 2012) and conceptualizing Organizational Resilience as heavily relying on the organizational ability to build external relations. Building on these primary considerations and
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Stavros Ponis and Epaminondas Koronis the rich consulting experience of the researcher in Crisis and Disaster Management, the proposed research aims at investigating the nature, challenges and structure of Supply Chain Networks for Disaster Management and Resilience. The paper draws on the analysis of the concepts of disaster management, inter‐organizational networks and focuses on research conducted on a case study of a complex supply chain under pressure. A process of informalization, dependence and lack of structured responsiveness is presented supporting the argument that the networked nature of supply chains creates important challenges in the event of disruptions or crises, including unexpected fluctuations in demand. Our analysis recognizes the current trends and changes in the Disaster Management models: first the increasing need to involve organizations as cells of resilience and second, the growing urgency for the development of disaster‐oriented processes and technologies for better supply chain management. Most importantly, this paper brings forward the need to establish inter‐organizational practices across organizations, suppliers and partners, thus preparing the supply chain for a structured response to crises or disasters.
2. Understanding disaster supply chain management (DSCM) While, in principle, Disaster Supply Chain Management would be expected to be a branch of supply chain management scholarship, there are important differences that make it a special body of knowledge with different requirements. It is commonly argued in literature that DSCM differs from traditional Supply Chain Management in that the "client" is a term used to identify affected areas and regions, victims of disasters while the supply chain entities include a variety of transportation, information management and human factors that are not met in traditional logistics operations (e.g. volunteers). Day et al., (2009) have studied extreme case studies from the Katrina Hurricane and identified eight (8) critical problems in disaster supply chain information flows (inaccessibility, inconsistency, inadequacy, low information priority, source invisibility, storage misalignment, unreliability). Some of these problems directly refer to the inability of social systems to collaborate and interact, even when exchange of resources is the rational method to use. DSCM, although based on the same principles with traditional logistics it requires the rapid adjustment of the system in conditions of pressure and lack of information (Kumar et al., 2009). As Balcik & Beamon (2008) argue, DSCM has to deal with shortened life‐cycles, reduced information resources and situation where urgent responsiveness is required under high uncertainty and often political pressure. Disaster Logistics require the use of technology, processes and standards in order to improve their operations but the lack a clear framework of analysis and implementation are often seen as a political and public administration issue (Blecken et al., 2009). As Whybark et al. (2010) put it, sometimes unexpected actors and agents interfere in the disaster management process (often with good intentions) competing for resources and affecting processes. Also, as Van Wassenhove (2006) argued and Beamon & Kotleba (2006) described in their investigations in Sudan, although DSCM is often in need of a leading organization and bureaucracy, it is often the case that "no single organization can independently create and maintain comprehensive information concerning the overall relief effort" (Day et al., 2009: 640). DSCM is therefore not organizational but depends on the development of synergies and inter‐organizational networks (Berkoune et al., 2011). It is therefore valid to say that as Disaster Supply Chains do not relate to individual firms or organizations and consist of situational and complex networks of entities, it comes as a surprise that research propositions are surprisingly scarce, although their importance is critical in saving lives, ensure social continuity and affected communities’ sustainability (Van Wassenhove, 2005; Day et al., 2009). However, a number of perspectives and approaches may be identified in current research and practice. First, a "strategy approach" investigates the role of Disaster Logistics in the overall Disaster Management process and studies its role within the existing policies and processes. In a collection of papers and studies, Christofer & Tatham (2011) summarize and articulate their objection to the development of the field as a technical matter of continuity and the tendency of the policy makers to look into disaster logistics as a functional issue solely relevant to the development of redundant capacity and resources management (ibid: 21). Their work, following Chandles & Pache (2010), calls for a strategic overview of DSCM and a technical‐political symbiotic model of action. In a series of cases analysis presented at INSEAD, Gatignon & van Wassenhove (2009; 2010) argued that it is critical that DSCM begins with the integration of different actors under the same information sharing, process‐based and resource exchanges framework where actions and decisions are more formalized
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Stavros Ponis and Epaminondas Koronis and less dependent on politics (see also Ratcliff, 2007). These studies add up to a growing and widespread concern in literature, stating that we are in need of a more holistic, integrated and synergetic framework that would allow different actors to work under the same strategy vision and goals, utilizing a common pool of resources. The above requirements for a more synergetic DSCM are supported by some advances that have been on what we might label as the more "social aspects". For instance, Sheffi's work in business literature considers the role of flexibility as equally important to the development of resource planning, building capacity and accumulating redundancy. His argument is that, precisely in times of disasters, social systems need to prove that they possess abilities relying on social capital, trust, adaptiveness and flexibility to change (see Sheffi, 2005; 2007; Christofer & Peck, 2008). In these studies, communities and organizations begin to evolve into crucial actors in the disaster management processes, as cells of disaster handling and resilience. Moreover, a cultural dimension is analyzed by scholars who saw in disaster logistics a high impact of human participation, history, culture, language and politics (see Dowty & Wallace, 2010; Dowty, 2011; Davidson, 2006). A number of scholars have been focusing on the communication and information management aspects of DRSCM. Maiers et al. (2005) present a set of principles for effective information disaster management that creates bi‐ directional spirals of knowledge within the managing groups and a knowledge base to be used by decision‐ makers. Brnasjaar & van der Merwe (2001) bring forward the dominant role of information technology in the handling of disaster incidents and Thomason (2010) examines a variety of cases in order to argue that knowledge and information are not tools in the hands of decision makers as during disaster; instead, they become the most important asset that mobilizes resources. In this later perspective, communications and knowledge exchanges are not a catalyst for improvement but merely the center of disaster logistics operations as normality is broken down and "nothing moves unless we know when, how and where to move it" (Disaster Management, 2009:31). Assuming the communications and information are of critical importance, their management is a challenge. This literature overview indicates that Disaster Supply chains, although challenged by technical and technological issues, they heavily depend on the ability of organization to interact, exchange resources and meet demands. This ability should be further conceptualized and the ‘social aspects’ of DSCM have to be investigated.
3. Inter‐organizational networks In organization and management studies, the role of inter‐organizational networks has been extensively studied. Despite differences, nearly all definitions address a relatively common base of topics, which are deemed important, including social interaction, relationships, connectedness, collaboration, collective action, trust and cooperation. In Brass et al. (2004) a network is seen as “a set of nodes and the set of ties representing some relationship, or lack of relationship, between the nodes”. Similar to Johnson et al. (2012), a very general view is presented focusing in particular on them antecedents and the consequences of networks at each of these levels. Podolny and Page (1998) include in their definition of inter‐organizational networks a variety of forms of cooperation, including mergers, joint ventures, alliances, collaborations and consortia. In a manner similar to Oliver (1990) in her earlier review of the inter‐organizational relationship research, Barringer and Harrison (2000) provide an overview of the different types of inter‐organizational relationships and analyze how each is different. Networks are defined as constellations of organizations that come together through the establishment of social contracts or agreements, rather than legally binding contracts. Such approach, builds on the tradition of Granoveter et al. (1992) who saw in the development of inter‐ organizational networks an emerging tendency in organizations to build dyadic or complex collaborations as the means for systemic survival, sustainability and growth. The very nature of networks in this tradition is that organizations are dependent on resources and are involved in transactions in an effort to reach their goals (Pfeffer & Salanick, 1978; Williamson, 1991). With the above studies revealing ‘why organizations form networks’, different streams of thinking addressed more critical questions related to the domains of inter‐organizational collaborations (focusing on learning, innovation, strategic development etc.). Other studies, relevant to this proposal explored the social conditions that enable organizations to effectively build networks. Among those, a number of scholars indicated the role of social capital (Granoveter, 1973; Burt, 1982; James, 2000). For example, James (2000) suggested that social capital mediates the relationship between difference and social support among organization managers.
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Stavros Ponis and Epaminondas Koronis Moreover, the role of organizational histories and experiences has been seen as of critical importance (see Baum et al., 2003; Powell et al., 2005). With a wide number of themes discussed in the theory of inter‐ organizational networks, this proposal sees in this tradition both a source of insights to be explored and transferred in the DSCM area (particularly as to which factors enable collaboration) as well as an opportunity to contribute by investigating the nature, development and operability of emergency organizational relationships.
4. Proposed integration and the next step in DSCM So what is the next step or the first step in building DSCM and improving resilience? The position taken in this paper is that a study of inter‐organizational networks to analyze the formation of collaborative DSCM is an open and highly important issue. In their recent work in the Journal of Supply Chain Management, Day et al. (2012) argue that Disaster Relief Supply Chain Management suffers from a diversity of conceptual approaches, terminological plurality (e.g. logistics vs. supply chain) and the lack of integration with relevant fields in management theory. Some analysts also argue that as disaster events increase, their impact is growing and the role of networks is amplified; we are in need of a convergence of different models into one integrated model focusing on inter‐organizational networks facilitated by technology and the appropriate cultures (Apte, 2010). Boin, Kenne & Whybark (2010) in their editorial in the International Journal of Production Economics argue that different fields (supply chain management, humanitarian assistance, disaster management, organization theory, public administration etc.) and various approaches (network, process‐based, resilience), inevitably lead to the formation of an emerging field: the study of disaster supply chain management (ibid: 1); this field, they argue, should be drawing on existing organizational knowledge and contribute with results. Moreover, similar to the argument of Kovacs & Spens (2007) they call for a dialogue process between the business field of logistics, management theory and disaster management and they propose the construction of bridges between the concepts and frameworks. Finally, it is evident that information and knowledge sharing become of primary importance and value as they make improvement possible and support the DSCM performance and identity (Larson et al., Kovacs, 2011; 2006; Larson, 2011). An integrated approach should incorporate the aforementioned directions and provide supporting arguments on the need for bridging the gap between the theory and understanding of inter‐organizational networks and for developing better aligned and integrated disaster supply chains. Organization theory has investigated the nature, processes and social conditions that make the formation of emerging and formal networks possible (e.g. Granoveter, 1973; Grandori et al., 2010) while Organization Resilience itself has been seen as dependent on collaboration and adaptation (Vogus & Suttcliffe, 2007). It is precisely the aim of the proposed research to build on the rich tradition of inter‐organizational networks and supply chain relations and produce innovative conceptual and normative research for the development of more resilient Disaster Management Supply Chains. Previous research has followed this new tradition by analyzing the integration of knowledge with crisis management (Koronis & Ponis, 2010), proposing a resilience supply chain framework (Ponis & Koronis, 2012) and conceptualizing Organizational Resilience as heavily relying on the organizational ability to build external relations (Koronis, forthcoming). Building on these primary considerations and the rich consulting experience of one of the authors in Crisis and Disaster Management, the paper aims at investigating the nature, challenges and structure of supply chain networks for disaster management and resilience. We argue that research is required in three key directions: a) Conceptual: by exploring the role of inter‐organizational networks in the development of resilient Disaster Supply Chains and the role of social capital constructs (trust, culture, commitment), collaboration (communication, information exchange) and technology as well as the nature of such transaction‐based collaborations, b) Exploratory: by empirically investigating (using data from a survey and a qualitative analysis of a sample of organizations) the current realities and challenges in the development of inter‐organizational collaborations and Supply Chain links in the event of large‐scale crises and disasters, c) Normative: by proposing an inter‐organizational Supply Chain Management model to be used by organizations in their effort to improve their exchanges during crises. In this introductory paper, we aim to provide insights into the reality, challenges and processes of DSCM with an emphasis on the role of networks in the development of effective responses. Furthermore, our empirical investigation attempts to contribute in this area of research.
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5. Research design The article investigates the case of a Supply Chain disruption within the context of a security operation, coordinated by the British Army 1 and implemented by a private firm in a foreign country. Given a sudden change of conditions and a mini weather disaster, the need for a complete reconfiguration of the supply chain demand and capacity has not been addressed effectively, as the different parts of the supply chain have remained in a state of knowledge isolation, resulting to insufficient delivery of fuel resources. The case, is set back in March 2011 and represents a typical 'supply chain disruption that was not addressed', leading to an ongoing discussion among the participants and the supply coordinators. A dramatic change in weather conditions, followed by political turbulence created the immediate need to increase the delivery, storage and coordination of fuel in special tanks, as this fuel was essential to continue operations. Our analysis reflected on the case, particularly by investigating the factors that would allow the complex supply chain to maintain its knowledge sharing capacity, to trigger processes of adaptation and the activation of effective inter‐organizational relationships that would lead to the mitigation of risk and the prevention of the supply crisis. Through a number of interviews with involved parties (n=12) and a focus group with the supply chain decision‐ makers in the UK offices, we attempted to examine the role and pathologies of knowledge management in these situations. It is important to highlight that although military operations tend to be focusing on in‐house operations and the development of internal competences, in our case they have been found to rely heavily on partnerships with local business while the supply chain involved non‐military vehicles and a key private local supplier. In fact, this supplier's role was critical in the continuity of fuel supply even before the disruptive period begins. We have conducted seven (7) interviews in the UK. The identity of participants was disguised and we codified the terms used and any location, although interviewees were keen to address details during the data collection period. Two (2) additional interviews were conducted on Skype while our research finally expanded to three (3) executive officials who were responsible for the analysis of the case. Our material was transcribed, codified into themes and finally our thematic analysis focused on exploring processes of knowledge management and the nature and functionality of partners and parts of the supply chain.
6. Case description On the 21st of March of 2011, the Logistics Officials in the UK HQ were informed that change of weather would affect operations abroad and that it would be critical to increase the quantity of fuel that would make future operations possible. However, when placing a request and informing the on‐site officials they got the response that such demand should be communicated to a local agent who could not be located. Further communication lead to the assumption that the request was depending heavily on the ability of the local logistics company and the supplier (2 different companies working under the same management) to respond to the demand. Two days later, events at the location increased the number of operations dramatically and consequentially the demand for fuel. In the words of a sergeant in charge, "we soon realized that we were asked to feed a jungle instead of the zoo that was our job a few hours before the events". As the crisis progressed, the supply chain started to fail to meet the demand and at that time an officer on‐site discussed with the headquarters the possibility of finding alternative fuel suppliers. A few hours later, he contacted the HQ and informed them that the current supplier could actually cover the demand but that he requested for additional fees and more time to respond. "It has been obvious that there was a problem of bad communication...everybody was doing a great job but it was nobody's job to talk to these [locals]" (Officer at HQ). Moreover, a discussion on contracts, obligations and plans was initiated in the middle of a complex negotiation about the delivery of fuels and the possibility of storage. In March 27th an officer admitted that "weather disruptions have been so severe that we needed to transport ten times the weekly amount of fuel within a few hours per day". While the immediate conclusion could have been focusing on the lack of cohesiveness across the supply chain, in fact further analysis reveals that the supply chain was relying heavily on informal relations between officers and the local supplier. As a sergeant said, "normality and good relations lead to the assumption that we would be able to respond to any demand......shaking hands and smiles, that's what it was all about". 1
Anonymity and confidentiality of participants is to be protected
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Stavros Ponis and Epaminondas Koronis However, this situation has led to the depreciation of the role of knowledge exchanges, scenario playing and information sharing. The informalization of the relations was followed by the construction of a culture of "perceived resilience" which was not based on realistic assumptions. The meetings, contractual discussions and informal chats with the suppliers have been institutionalized as "processes" thus excluding knowledge exchanges from the tasks and duties. This led to the orphanization of knowledge and the loss of the absorptive capacity and the oversight of learning processes (Cohen & Sproul, 1994). After the end of the disruption period (April 4th), research on the post‐disaster period indicates that relations with the suppliers became much more technical in an attempt to restore a sense of normality while assessing the needs for fuel in the recovery period. A key conclusion has been that the relationship with services and fuel suppliers should be based on "negative‐scenarios" and formalized forecasting procedures, while also establishing communication channels that would ensure the immediate and appropriate response.
7. Findings and discussion Our analysis revealed a number of interesting realities. First, it confirmed the phenomenon of the 'informalization' of supply chain knowledge management processes in the event of cultural differences across loose inter‐organizational networks. Second, it showed the lack of structured responsiveness and knowledge sharing in the event of unexpected incidents or radical contextual changes, as a result of a disassociation of the supply chain members from the actual problem. Knowledge has not been situated and it has not been socialized as part of a community that would adjust its practices to the changing environment. Finally, a key finding is the unveiling of a number of issues related to the formation of networks and knowledge management. We stress here the notion that strong inter‐organizational ties, particularly with suppliers, automatically generate a systemic ability to handle knowledge challenges and supply chain disruptions. Furthermore, by classifying inter‐organizational knowledge relations into categories, we identified the possibility of strong networking leading to the structuring of formal knowledge processes under certain conditions (preparedness, communication, stress‐testing). Our research indicates that the role of networks and established inter‐organizational practices is critical in three key directions. First, in order to ensure that the supply chain is able to transfer changing demand signals effectively and quick across different partners. In that direction, communication needs to be formalized and information exchange should be supported by well‐established and tested practices (Cooren, 2001). Second, inter‐organizational relationships, particularly in the event of disasters and under conditions of time pressure need to use pre‐existing knowledge sharing and adaptation processes. Such processes must be developed in the pre‐crisis period by ensuring that knowledge transfer capacities are enriched and that the active partners will be part of the crisis management response process. In this respect, resilience of the supply chain and an effective networked disaster supply chain system must build strong ties across the different parts of the operations (Grandori et al., 2010), but also to achieve a degree of formalization of processes. Finally, this case analysis reveals the need for the integration of people, processes and communications under a unified disaster supply chain management framework that would meet the increased demand or process‐ based challenges. Returning to the argument made by Sheffi (2005), it is precisely in times of disasters, social systems need to prove that they possess abilities relying on social capital, trust, adaptiveness and flexibility to change (see Sheffi, 2005; 2007; Christofer & Peck, 2008). Such values need to allow inter‐organizational networks to evolve into situational communities of practice and emerging networks of response (Wenger, 1997). It is the aim of the authors to expand this study by embedding its findings into the ongoing efforts for the development of a normative framework that would address the processes of inter‐organizational integration for more effective Disaster Supply Chain Management.
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Institutional Planning of Knowledge Generation1 Evgeny Popov, Maxim Vlasov, Anna Yu.Veretennikova Institute of Economics, Ural Branch of Russian Academy of Sciences, Ekaterinburg, Russia epopov@mail.ru mvlassov@mail.ru vay_uiec@mail.ru Abstract: The article explores how an increase in different types of explicit knowledge depends on the change in transaction costs. The data obtained allowed the authors to work out an approach for the quantitative evaluation of the institutional environment of knowledge generation at company’s level. Keywords: knowledge economy, institutional planning, transaction cost
1. Introduction Contemporary economic conditions prove knowledge and knowledge generation to be both the elements determining the development of an economic unit, and the most important factors underpinning country's economic growth. However, knowledge creation is often a chaotic and inconsistent activity, which does not always result in systemic innovation development, if such takes place at all. Securing the knowledge generation activity with essential norms will facilitate its structuring, further development, providing conditions for its planning, realization, monitoring, and timely optimization. Development of the institutional environment, underpinning knowledge generation, will not only reduce the uncertainty of such an activity, but also stir it up and help attract the resources required. Although the above‐mentioned issues are critically significant, the modern economic study has not come up so far with methodological tools that would allow the institutional planning of the knowledge generated by economic units. The purpose of this study is to develop a theoretical concept suitable for the assessment of the institutional environment of knowledge generation at mini‐economic level (Popov, 2005).
2. Background for assessing the institutional environment of knowledge generation The scholars of the Austrian economic tradition F. Hayek (1945) and J. Shumpeter (1952) did admit the significance of new knowledge in economic processes. However, they believed them «subjective», impossible to evaluate as measurable parameters and regarded no possibility of describing the processes of new knowledge creation. The term «information society» was coined in the 60s in the USA and Japan by F. Machlup and T. Umesao simultaneously, the authors having gained the worldwide recognition for the research into the dynamics of high‐tech industries. In his further works, F. Machlup proved the importance of the new knowledge generation for the economic activity development of economic agents (1962). American and European researchers focused on the role and impact not of information itself, but knowledge, which eventually brought about a range of new definitions of modern society, such as «knowledge society», «knowledgeable society», etc. These are knowledge, intangible assets and intellectual capital that play a pivotal role in such an economy, in contrast to the use of material assets and financial capital in the traditional one. Considering the effect of knowledge on economic growth, the analysis of the institutional environment in the area of interest is gaining a particular research attention. The investigation of the World Bank includes estimation of the economic and institutional regimes due to a number of indicators. Nevertheless, there is still a considerable gap in understanding the institutional environment of knowledge generation at company's level, which has encouraged the authors to tackle the development of the technique to its assessment. 1
The research is funded by the RAS Program №35 «Economics and Sociology of Science and Education» with the support of UB RAS Project № 12‐П‐7‐1006 «Regional Institutions of Science Development»
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Evgeny Popov, Maxim Vlasov, Anna Yu.Veretennikova The institutional approach to the economic analysis dates back to the T. Veblen works ‘Why is economics not an evolutionary science?’ (1898), as well as ‘The place of science in modern civilization’ (1919). Having rejected the concept of a person as an elemental subject of economic activity, T. Veblen offered a concept of institutions as ‘organic habits of thought’ being characteristic of large groups of people. R. Coase first introduced the concept of transaction costs in the 30s of the last century in the paper ‘The Nature of a Firm’. It was used to provide an insight on the existence of such a hierarchical structure as a firm, being oppositional to market. R. Coase explained the emergence of these ‘islands of consciousness’ with their respective advantages in terms of saving the transaction costs. Specifics of firm’s functioning he saw in suppression of the price mechanism and its substitution with the inner administrative control. In the framework of contemporary economic theory transaction costs have gained a variety of treatments. К. Arrow approaches transaction costs as the costs of economic system exploitation (1994). He compared the influence of transaction costs on economy with the one of friction in physics. Such suggestions triggered some conclusions like the closer an economy comes to the Walras’s general equilibrium model the lower the level of transaction costs it demonstrates, with the opposite being as true. D. North determined transaction costs as consisting of ‘the costs of assessment of useful properties of exchange goods and the costs of property rights security and enforcement on their execution’ (1997). These costs were believed to be the source of social, economic and political institutions. Following the notions of K. Arrow and D. North, we will define the cost assessment of an economic institution as the transaction costs of establishing the norm of interaction between economic agents.
3. Transaction costs of knowledge generation Investigating the institutional environment of knowledge generation, consideration was given to the interdependence of institutions and transaction costs in economic processes (Popov, Vlasov, 2012). The authors treat transaction costs as institutions’ cost estimation characteristic (Popov, Vlasov, 2012). The content of the knowledge generation process itself, or, in other words, the technology of transforming information in new formalized (explicit) knowledge, depends on the type of activity the company is involved in, its specifics, functioning of the management system, the norms and rules, which regulate this activity. Therefore, an institute of knowledge generation is a system of established formalized norms (rules), determining interaction between two or more economic agents in the process of structuring and classifying information into the formalized knowledge, and equipped with the mechanisms of coercion. The institutions of knowledge generation include the norms and rules, regulating the creation (production) of new knowledge directly, but also those kinds of activity, guaranteeing functioning of the knowledge generation processes. The institutions of knowledge generation, when functioning correctly, provide opportunities for an economic agent to develop. They make a platform for comparative advantages of the company's production, technology and development on the whole. Hence, the effective functioning of the knowledge generation processes, and as a consequence, of the company strongly depends on how the system of economic institutions has been designed at this level. So the authors have differentiated the following institutions of knowledge generation:
Institutions of database formation;
Institutions of invention creation;
Institutions of utility model creation;
Institutions of production prototype development;
Institutions of trade secrets legalization;
Institutions of trademark creations;
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Institutions of trademark and service mark development;
Institutions of designation of trade origin places;
Institutions of commercial designations.
4. Empirical results A technique for assessing the institutional environment of knowledge generation at company's level was developed during the empirical study undertaken at the enterprises of the Ural region. The CEOs of over 50 industrial enterprises were questioned on the dynamics of the transaction costs of knowledge generation in the period from 2007 to 2010. The study was aimed at testing the following hypothesis: the speed of knowledge generation depends on the change in transaction costs. Fig. 1 gives the average annual structure of the increase in transaction costs of knowledge generation. TC R&D expenditure
10
Noncapital expenditure, including updating the technology, production organization and management
8,7
Expenses of production and service certification
8,4
Incentive allowance
8,3
Intellectual property and means of individualization fees
7,3
Training costs
7,1
Recruitment expenses of staff engaged in innovation activity
6,9
Travel expenses
6,9
Consulting and similar services fees
6,9
Databases purchase and exploitation
6,7
Advertising
6,6
Software
6,2
Current marketing expenditure
5,3
Expenses of representation
4,7 0
2
4
6
8
10 d TC
12
Figure 1: Average annual structure of the increase in knowledge generation transaction costs Studying Fig.1 one should distinguish the expenses characterizing the institutions of functional, structural and operational knowledge generation. The analysis of the average annual structure of the explicit knowledge gain in the form of know‐hows, inventions, utility models and production prototypes (functional knowledge) showed its fraction to be 55,7%, pointing at company's orientation toward innovation activity development (Fig.2).
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Evgeny Popov, Maxim Vlasov, Anna Yu.Veretennikova
Structure of the explicit knowledge Know-hows
17,3%
R&D results published
15,6%
Inventions
15,4%
Utility models
12,7%
Data Bases
12,5%
Production prototypes
10,3%
Means of individualization
8,5%
Software
7,7% 0%
2%
4%
6%
8%
10%
12%
14%
16%
18%
20%
Increase portion of the explicit knowledge, %
Figure 2: Average annual structure of the increase in intellectual activity results and means of individualization
5. Dependence of the increase in knowledge generation on transaction costs (tc) Before investigating the effectiveness of institutional environment of knowledge generation, it seems justified to draw correlation between the gain in knowledge (IR – intellectual activity results, MI – means of indivifualization) and transaction costs (TC) increase. The example of the dependences obtained is given in Fig.3
innovation gain
3
2,5 y = 0,941x + 0,074
2
1,5 1 0,5 0 0
0,5
1
1,5
2
2,5
Figure.3: Increase in knowledge dependence on transaction costs change Here, the knowledge gain rate can be calculated with the following equation: (1) vij –rate of increase in j‐IR or MI under the change in i‐TC; dkj – increase of j‐ type of knowledge; dTCi ‐ increase of i‐TC.
580
3
3,5
TC on R&D gain
1,21
R&D expenditure
Noncapital expenditure, including updating the technology, production organization and management
Expenses of production and service certification
Incentive allowance
1
2
3
4
581
1,63 1,6
Intellectual property and means of individualization fees
Recruitment expenses of staff engaged in innovation activity Travel expenses
Consulting and similar services fees
Databases purchase and exploitation Advertising Software
Current marketing expenditure
Expenses of representation
6
7
8
9
10
11
12
13
14
Calculation of the knowledge generation rate vij can be useful in considering which activity to fund. 2,18
1,66
2,25 2,625
1,57
1,36
1,31
1,29
1,2
1,26
1,04
1,18
1,0
2,0
1,7
1,69
1,68
1,3
Training costs
5
1,54
1,33
Utility models 0,9
Production prototypes
0,9
1,59
1,32
1,17
0,97
0,93
0,95
0,91
0,93
0,73
0,9
0,76
0,63
0,69
Know‐hows 2,76
2,12
1,96
1,64
1,55
1,57
1,46
1,53
1,23
1,48
1,27
1,02
1,11
1,14
0,98
0,83
0,71
0,67
0,69
0,67
0,68
0,53
0,66
0,55
0,46
0,5
0,39
Means of individualization
0,9
1,0
0,89
0,73
0,64
0,61
0,62
0,6
0,61
0,48
0,59
0,55
0,42
0,46
0,35
Software
0,54
1,87
1,48
1,33
1,13
1,07
1,08
1,02
1,06
0,85
0,87
0,87
0,71
0,77
0,62
Data Bases
0,73
Results of intellectual activity kj
2,42
1,99
1,73
1,49
1,42
1,43
1,37
1,41
1,12
1,14
1,14
0,95
1,03
0,82
R&D results published
1,2
0,94
Transaction costs, ТСi
Inventions
Evgeny Popov, Maxim Vlasov, Anna Yu.Veretennikova
In the example given in Fig.2 v11 is 0,941. The factor vij gives the rate of knowledge gain under the 1% increase in transaction costs. Assessment of the factor vij allows evaluating the influence of particular transaction costs on the knowledge generation process development. If the knowledge gain exceeds the transaction costs gain, this activity functions effectively. Similarly, the dependences for all types of knowledge were drawn (Tab.1) Table 1: Empirical values of the rate of explicit knowledge gain under the change in transaction costs (vij)
Evgeny Popov, Maxim Vlasov, Anna Yu.Veretennikova Therefore, when planning the knowledge generation activity, it is necessary to consider what return the investments in a particular kind of activity, connected with and influencing the knowledge generation results, will bring. The data obtained were used to develop an assessment technique for the institutional environment of knowledge generation. The authors calculated the factor of institutional development of knowledge generation in a company (K) (2).
m
n
j =1
i =1
K = ∑ a j ∑ ( bi ∗ eij )
(2)
Here, j ‐ the order number of the intellectual activity results or means of individualization (IR of MI); i – the order number of a transaction cost; K – the rate of institutional environment development; aj ‐ the increase in j‐ IR of MI; bi ‐ the change of i‐transaction cost; eij – sensitivity of the increase in j‐IR of MI under the change in i‐transaction cost. ), The factor makes allowance for the gain in some explicit knowledge ( ), the change in transaction costs sensitivity of knowledge gain under the change in transaction costs (eij). It depicts the return of the money invested in the company on the whole. In other words, if K=2, the 1% weighted change in knowledge generation transaction costs gives the 2% explicit knowledge gain.
6. Efficiency of knowledge generation institutions It should be noted that K is an integrated value, showing the rate of knowledge creation in a company under the functioning institutional environment. The value of K characterizes the institutional conditions of company's innovation development. Table 2: Innovation activity described by K K K <0
Institutional conditions of knowledge generation System institutional trap
Innovation activity No innovation activity
К=0
Undeveloped institutional environment
No innovation activity
0< K ≤1
Developing institutional environment of knowledge generation
Conditions needed for its effective development
K >1
Developed institutional environment of knowledge generation
Institutional environment encourages company's innovation activity
If K<0, there exists a system institutional trap. In this case, it is necessary to reveal a reason and its origin, then to plan a way out. K=0 points at undeveloped institutional environment, i.e. there is likely no innovation activity at all. Here, the profound analysis of inner and out company's medium, problem field analysis are needed followed by drafting innovation development directions and working out a project for the institutional development of innovation activity. If 0<K≤1, we can talk of the developing institutional environment of knowledge generation, which implies providing better conditions for more effective development. The authors suggest highlighting key objectives of innovation development, working out a plan for redistributing the knowledge generation transaction costs, and then performing this plan of institution modification. K >1 means that the institutional environment of knowledge generation is developed and assists company's innovation development. If there is some potential for K increasing, the work should be done so as to advance the effectiveness of existing institutions.
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Evgeny Popov, Maxim Vlasov, Anna Yu.Veretennikova Min К was 1,01, with K being less than 1,15 in over 80% of enterprises studied. Max value was 4,46. The average value of K of all enterprises under study was 1,12. The value of K>1 indicates that although the increase of new knowledge in companies strongly depends on the funds being invested, there is still a positive dynamics of innovation activity. Thus, though the enterprises studied are resource‐dependent, they are aimed at innovation development. The study, undertaken to empirically assess the dependence of the results of economic activity at mini‐ economic level on the transaction costs of new knowledge generation, yielded the following theoretical and practical results. Firstly, high correlation between the transaction costs of knowledge generation and the performance of explicit knowledge generation activity has been proved. This can contribute to the design of the models of knowledge generation increase in an economy sector. Secondly, the dependence determined provides a condition for advancing the knowledge generation gain by increasing and redistributing the transaction costs. Thirdly, an approach to quantitative assessment of the institutional environment of knowledge generation has been developed, considering the structure of the knowledge created in a company, the structure of transaction costs of knowledge generation, dependence of the knowledge gain on the transaction costs, as well as sensitivity of the knowledge gain to the change in transaction costs of knowledge generation. The technique suggested allows evaluation of the system sensitivity to innovation development.
References Arrow, K. J. Methodological Individualism and Social Knowledge //American Economic Review, 1994, Vol. 84, N 2. Chugunov A. Systems of Indicators, Monitoring of Information Society and Knowledge Economy Development // Vestnik of International Organizations: Education, Science, New Economy / Analytical Journal of GU VPS, 2006, № 7. Civil Code of Russian Federation. Part 1,2,3,4 ed. by April, 1, 2011, Moscow, 2011. Coase R.H. The Nature of Firm //Economica, New Series. 1937. Vol. 4. N 16. Farnaz Rahimi, Mohamad Ebrahim Maroosi. Knowledge Management Assessment of Khorasan Razavi Gas Company (Viewpoint of employees) // Proceeding of the 12th European Conference of Knowledge Management . University of Passau, Germany. UK: Academic Publishing Ltd, 2011. Fayustova E., Popov E. Knowledge as Economic Category // Strategic Planning and Industry Development: proceedings, Moscow, 2006. Hayek F.A. The Use Knowledge in Society // American Economic Review. 1945. Vol. 35, № 4. Kozyrev A. Evaluation of Intellectual Property, Moscow, 1997. Machlup F. The Production of Information and Knowledge. Princeton, NJ: Princeton University Press, 1962. Moscovin V., Teng D., Barder A. Methodology of Knowledge Economy Assessment // World Economics, 2011, № 4. Nort D. Institutions, Institutional Changes and Functioning of Institutions, Moscow, 1997. North D. Structure and Change in Economic History. N.Y.: Norton, 1981. Popov E. Transaction Dimension of Institutions // Economics of Modern Russia, 2011. №2. Popov E., Vlasov M. Simulation of New Technological Knowledge Generation // Economic Analysis: Theory and Practice, 2009, №4. Popov E., Vlasov M., Veretennikova A. Resource Index of Knowledge Generation // Economic Analysis: Theory and Practice, 2011. № 17. С. 17‐21. Popov E.V. Minieconomics as a Separate Part of Microeconomics // Atlantic Economic Journal, 2005, vol.32, No. 3. Popov E.V., Vlasov M.V. Dependence of Research Productivity on Transactional Costs // Actual problems of Economics №5. 2012. Popov E.V., Vlasov M.V. Knowledge Generation within a Firm as an Object of Institutional Design//Actual problems of Economics №1. 2013. Shumpeter J.A. The Theory of Economics Development. London: George Alien & Unwin, 1952. Veblen T. The Place of Science in Modern Civilization and Other Essays. N.Y.: Huebsch, 1919. Veblen T. Why is Economic not an Evolutionary Science // Quarterly Journal of Economics. 1898. Vol. 12. N 4.
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Knowledge Audit: Findings From a Case Study in the Energy Sector Gillian Ragsdell, Steve Probets, Ghosia Ahmed and Ian Murray Loughborough University, Leicestershire, UK g.ragsdell@lboro.ac.uk s.g.probets@lboro.ac.uk g.ahmed@lboro.ac.uk i.r.murray@lboro.ac.uk Abstract: Knowledge audits are important processes through which organisations can understand what knowledge is needed, available and used for their current activities. They can also identify what knowledge is missing and how this omission restricts the organisation’s activities. Hence, knowledge audits can surface initiatives to improve the knowledge management (KM) processes of an organisation and, in turn, improve efficiency and effectiveness. An iterative cycle of knowledge audits allows for the organisation’s changing environment to be taken account of and for appropriate modifications to be made to the knowledge base. Despite the importance of knowledge audits, literature relating to their undertaking is sparse. This paper addresses the scarcity of such literature and reports the findings of a knowledge audit commissioned by an organisation that brings together public bodies and private organisations with the aim of maximising the collective knowledge, expertise and experience of its diverse members to address a nationally recognised research agenda. The audit included collecting qualitative data from a series of in‐depth interviews with a representative sample of employees from the four main departments within the organisation. Interviewees were asked about their own roles, procedures and knowledge needs; they were also asked about their department’s knowledge requirements and about knowledge interfaces with external partners. Views about the culture and structure of the organisation were also sought. Results were analysed at a departmental level to form two knowledge maps per department – one illustrating the knowledge required by the department, the knowledge shared with other departments and the mechanisms for sharing this knowledge; the other illustrated knowledge flows with external partners. The maps were then used in conjunction with the interview transcripts to identify the strengths and weaknesses of each department’s knowledge activities. This process focussed on the impact of organisational culture and structure as well as the effectiveness of technological and ‘soft’ solutions for knowledge sharing. Following from the departmental analysis, a cross department comparison enabled best practices and company‐wide weaknesses to be identified. Seven resulting recommendations were made that would support the sharing of departmental best practices and address organisational weaknesses: Developing a holistic approach to knowledge sharing Nurturing the organisational culture Clarifying the strategic message Improving the organisation of information Improving the availability of staff Developing inter‐departmental communication Commissioning future knowledge audits In addition to reporting the outcomes and outputs of the process, the paper also highlights challenges of the process and includes reflections on the suitability of the selected data collection and analysis methods for a knowledge audit. Keywords: knowledge, audit, maps, case study
1. Introduction Knowledge audits are important processes through which organisations can understand what knowledge is needed, available and used for their current activities. They can also identify what knowledge is missing and how this omission restricts organisational activities. Hence, knowledge audits can surface initiatives to improve the knowledge management (KM) processes of an organisation and, in turn, improve efficiency and effectiveness. An iterative cycle of knowledge audits allows for the organisation’s changing environment to be taken into account and for appropriate modifications to be made to its knowledge base. Despite the importance of knowledge audits, literature relating to their undertaking is sparse. This paper addresses the scarcity of such literature and reports some of the findings of a knowledge audit commissioned by an organisation that brings together public bodies and private organisations with the aim of maximising the collective knowledge, expertise and experience of its diverse members to address a nationally recognised research agenda.
2. Knowledge audits – an introduction Debenham and Clark (1994:201) described a knowledge audit as “a well‐defined, highly technical, structured report containing an overall, high‐level description of a restricted section of an organisation's knowledge resource and a description of identified individual "chunks" of knowledge in that section”. In more recent times, there has been less emphasis on the output of knowledge audits and a stronger emphasis on the related activities. The dynamic nature of knowledge audits has been recognised along with the benefits of following such a process. According to Levy et al (2010:114), knowledge audits are deemed as the “first critical step for
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Gillian Ragsdell et al. implementing knowledge management (KM) practices in organisations”. This is a view that is supported by Liebowitz et al (2000) who acknowledge a knowledge audit as the first stage of an organisation’s KM strategy, where its purpose is to lay a concrete foundation (Choy et al, 2004) and enable evaluation of all areas of KM processes (Biloslavo and Trnavčevič, 2007). Burnett et al (2004) suggest that a knowledge audit can help organisations to determine and illustrate the knowledge they possess, where this knowledge resides and how it flows through the organization. Furthermore, the knowledge audit allows mapping and proactive transference of organisational knowledge (Mearns and Du Toit, 2008) and, according to Biloslavo and Trnavčevič (2007), the results of the audit enable an organisation to identify the intrinsic strengths and weaknesses of its KM processes and give the ability to unveil and exchange best practices between different parts of the organisation.
2.1 Knowledge audits ‐ methods and techniques Several approaches have been taken to conduct knowledge audits; a variety of methods and techniques have been used in organisations. 2.1.1 Questions Firstly, the types of questions that are typically asked during knowledge audits could be put into two categories, (1) identifying the knowledge that currently exists and (2) identifying the knowledge that is missing (Dattero and Galup, 2007; Liebowitz et al, 2000). In addition, some studies have also designed certain knowledge audit questions around subjects such as individual characteristics of the participant, effectiveness of KM processes (Biloslavo and Trnavčevič, 2007), KM implementation problems, organisational culture (Gourova et al, 2009), tacit perceptions and cultural barriers (Levy et al, 2010), general barriers and problems (Burnett et al, 2004), and the degree of knowledge sharing interest in participants (Perez‐Soltero et al, 2006). However, a more common theme that has guided knowledge audit questions is the use of information technology systems and communication tools to support KM activities (e.g. Gourova et al, 2009; Reinhardt, 2003; Bontis et al, 2003; Debenham and Clark, 1994). 2.1.2 Questionnaires and interviews The use of questionnaires is a common method of acquiring data in a knowledge audit, often used in the preliminary phase or as part of multiple tools (Burnett et al, 2004; Choy et al, 2004; Hylton, 2002). However, questionnaires have also been used as the primary tool for data collection (e.g. Liebowitz et al, 2000). Though questionnaires can be a useful tool in knowledge audits for collecting structured or semi‐structured data, they can have limitations in terms of the quality, depth and context of qualitative responses. Therefore, Hylton (2002:7) argues that interviews are an essential part of a knowledge audit “to gain a deeper and more qualified insight into the true and objective knowledge management position of the company”. Furthermore, the use of semi‐structured interviews is an effective tool for finding KM needs and opportunities, whereas open‐ended interviews offer further opportunities to gain insights and understanding of participants’ perceptions (Gourova et al, 2009). Various knowledge audits such as Levy et al (2010), Mearns and Du Toit (2008), Burnett et al (2004) and Choy et al (2004), have employed either semi‐structured or structured interviews to acquire detailed responses from participants. 2.1.3 Maps The central activity of a knowledge audit is often the creation of a knowledge map that shows the “knowledge stock” (Dattero and Galup, 2007: 216). According to Wexler (2001: 250), a knowledge map is a graphically presented communication channel that provides excellent means to “capture and share explicit knowledge”. Knowledge maps have been adopted in various knowledge audit studies: for example, Bontis et al (2003) depicted the flows of codified knowledge via e‐mails, Choy et al (2003) combined knowledge maps with social network analysis to display knowledge exchange between individuals and Burnett et al (2004) produced individual knowledge maps per participant which depicted knowledge flows, sources and bottlenecks.
3. Case study organisation The case study organisation is a public‐private partnership between global energy and engineering companies and the UK Government that brings together the collective knowledge, expertise and experience of its diverse membership to address future energy challenges. More specifically, the organisation is working towards the
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Gillian Ragsdell et al. UK Government’s long‐term energy emissions reductions targets. With these targets in mind, the organisation initiates and supports projects that accelerate the development of affordable, secure and sustainable technologies. It has made investments in projects in offshore wind, carbon capture and storage, and bioenergy that bridge the gap between laboratory scale research and developments, and commercial deployment of large‐scale engineering projects. By working with a range of national and international partners – multi‐ national companies, SMEs, universities and research organisations – the organisation is able to create project teams at the cutting edge of science, technology and engineering. Integral to the success of its projects is the high calibre of expertise and knowledge of its project partners; thus the organisation is highly knowledge intensive. The organisation had recognised the potential of effective KM practices to improve efficiency across the organisation. Hence, KM was high on its strategic agenda and the organisation has been very proactive in this respect. In fact, the development of a KM strategy was already underway when the authors were invited to undertake a knowledge audit therein.
4. Knowledge audit design The objectives of the knowledge audit were agreed as follows:
Map critical knowledge flows (both tacit and explicit) throughout the organisation.
Determine what knowledge assets are most important in supporting specific organisational activities.
Identify any knowledge gaps and bottlenecks.
The audit was to be completed within two months and the budget allowed for one full‐time Research Assistant to work on the audit with some input from a small team of academics. An interpretivist paradigm was adopted for the knowledge audit and semi‐structured interviews were chosen as the primary data collection tools.
4.1 Interviews An interview schedule was designed to facilitate the collection of data and information from selected participants, focusing on the identification of knowledge inputs and outputs and the mechanisms for sharing knowledge between both internal and external stakeholders. The questions were designed to capture knowledge that could be analysed and presented as knowledge maps outlining the knowledge flows between stakeholders and the formal and informal systems by which knowledge is transferred. The interview schedule comprised of twenty‐three questions that were arranged in four sections. These four sections concerned i) knowledge required to perform the participant’s own tasks; ii) the participant’s view of the knowledge and information handling procedures required for the participant’s department to fulfill their role; iii) perceptions about the role of the organisation as a whole; iv) questions about the organisational culture. The questions were a mixture of open and closed questions and aimed to elicit individuals’ personal perspectives on various aspects of knowledge within the organisation.
4.2 Participants An organisational chart was used to determine an appropriate set of participants; twelve participants were selected so as to best represent the four main departments within the organisation. Participant selection was based on the size of department, the individual’s role and responsibilities, and their position in the organisational reporting structure. Gender and length of experience in the organisation were also taken into account ‐ while analysis at an individual level was not a requirement of this audit, (only departmental and organisational levels had been requested), consideration of these characteristics meant the sample more strongly reflected organisational characteristics. A pilot study involving four participants was undertaken to provide assurance of the appropriateness of the questions and minor changes to the interview schedule were made based on feedback from the pilot study. Each interview was designed to last around one hour and all participants were given the usual reassurances about anonymity and confidentiality.
4.3 Data analysis process The twelve transcripts were organised into four matrices – one for each department. Table 1 shows an extract from the matrix for Department A. The matrices were analysed and key phrases were identified that related to four specific themes – knowledge, systems, channels and stakeholders.
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Gillian Ragsdell et al. Table 1: An extract from the matrix for department A
1. What are the processes that you are responsible for?
2. What knowledge do you need to use to perform your role and how do you acquire it? 3. How do you organise and share the knowledge that you possess? 4. Once knowledge has been shared and used, how do you incorporate the feedback from this into the organisation’s existing knowledge?
Participant 1 (Time at organisation: 4 Years)
Participant 2 (Time at organisation: 1 Year)
Consistency technically across projects Overseeing projects Learning from projects Feedback into projects Engaging with members
Getting information to members Keeping people in the loop Organise presentations and seminars Two way dialogue Understand member requirements Writing headline insight documents Upgrade the Member Portal system Member Portal documents Direct conversations Board and Technical Committee Talk to and email people Headline insights Member engagement plans
External environment End system look like Future requirements Shared drive Advisory groups Emails Presentations Technical Committee Personal stores of information Advisory group papers Technical Committee papers Intranet Member Portal ‐
5. Which information, documents and systems do you use within your role?
Member engagement plans
Shared drive Email Member portal Team meeting Documents
The sets of key phrases were modelled using MindNode software for Mac OS so as to show knowledge flows. Two maps were created for each department: one showed the types of knowledge that participants used and generated during their workplace activities, and the range of mediums through which the knowledge flowed; the other map showed the range of people (internal and external) with whom participants exchanged knowledge. An example of the former type of map is presented in Figure 1 for Department A so as to illustrate the format and to highlight the complexity that was represented.
5. Findings For each of the four departments, the resultant maps were analysed and a narrative was developed that reflected the key departmental findings. This was followed by an identification of the strengths and weaknesses with respect to the department’s knowledge activities which led to suggestions as to how each department might improve its KM practices. The focus on Department A continues in the next section where the findings for this selected department are presented as illustrative of the audit process.
5.1 Discussion: Department A Two adjectives dominated discussion of the organisation’s culture ‐ ‘open minded’ and ‘analytical’. In addition, there was a strong sense of respect for the technical expertise of employees and an acknowledgement that storing and accessing such expertise was important. There was also evidence of a dominant theme regarding the management of generated knowledge within the organisation. Views were expressed that either there was no evidence of a KM culture or that it was in an embryonic form. Comments were made that stressed the organisation’s ability to generate knowledge but that it was less able to ‘decide what to do with it’.
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Figure 1: Map of knowledge inputs and outputs, and mediums The knowledge maps illustrated the range of systems and channels that are used to support the flow of knowledge but participants made use of these systems and channels to noticeably different degrees. The shared drive and e‐mail communication were used most frequently and the organisation’s website used the least. Variable levels of use were associated with the other mediums. While the shared drive was used frequently by all respondents, in some cases it tended to be simply a storage place for information rather than a facility to access information. The reason for this mode of operation was based on the lack of user‐ friendliness and difficulty to search across it. A lack of structure and cataloguing was reported within this system. Despite this difficulty in searching the shared drive, most individuals described the accessibility for the required knowledge for their role as ‘good’. While there seemed to be a shared willingness to communicate more widely in the organisation and levels of cooperation and collaboration within and between individuals in departments were generally good, there were barriers to achieving better communication at an inter‐departmental level. The primary barrier stated was that of people’s availability. Secondary barriers to more informal sharing were the quiet atmosphere of the department, which restricted the likelihood of informal communication and acknowledgement of the different ways in which departments work. It was said that the latter made ‘the interface between departments difficult at times from the view of data and information exchange.’ In general, there was clear recognition of the distinction between confidential and non‐confidential information; there was some uncertainty as to how the systems managed this distinction but there was a general level of confidence that ‘we have systems that organisations normally have’.
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5.2 Strengths The following positive aspects of managing knowledge were identified from analysis of the transcripts and maps for this department:
Individuals’ information is stored on the shared drive, so it exists within the organisation’s systems.
Information collected from groups and committees is collated into papers and made available on the intranet and on the member portal.
Initiatives for ensuring information are communicated to and from members.
Member engagement plans have been developed, which make explicit the communication network between the organisation’s employees and its members.
Use of guidance notes by some individuals enables continuity of tasks.
Working closely with the organisation’s communications team ensures accuracy of external communication.
Individuals are aware of the distinction between confidential and non‐confidential knowledge.
Various forums and meetings for sharing knowledge take place.
Individuals describe the organisational culture as ‘open’ or ‘open‐minded’.
5.3 Weaknesses The following were identified as areas of concern:
The organisation is not good at expressing technical knowledge in a basic way for other, less technically minded personnel within the organisation.
Some individuals store their documents centrally, while others tend to store their documents on their personal areas, which restricts availability.
There is reliance on certain individuals for project data, which can cause delays.
Individuals’ availability can be problematic.
Structure of data and lack of cataloguing/user‐friendly information search tools, on various systems can cause problems or delays.
Lack of access to academic journals and lack of coordination of subscriptions.
It is difficult to get access to or understand the sum of the organisation’s knowledge on a particular subject due to the information being too complex and technical.
Most individuals felt that work was needed to improve the completeness and richness of information.
Not knowing who has what knowledge.
No central visibility at the department level of communication and collaboration.
There can be duplication and inconsistency in the communication to external stakeholders at times.
Departments are “too quiet” which can restrict informal conversations and prevent knowledge sharing from taking place.
5.4 Recommendations The following recommendations were derived so as to maintain and support the development of current departmental strengths and to overcome the stated weaknesses:
Continue the development of a knowledge culture including support for informal exchanges.
Develop and implement a policy to organise and catalogue information maintained in the organisation’s most commonly used systems, such as the shared drive.
Be mindful of the need to use layman’s terms in certain circumstances.
Improve access to academic journals.
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Make individual knowledge‐bases more transparent.
Provide regular updates in the organisation’s Executive meetings to enable other departments to better understand the department’s work and be more proactive in communication and collaboration with other departments.
In this section, findings have been considered at a departmental level. In practice, the same process was applied to the other departments, before the process of analysis continued with a cross comparison of the four departments. While acknowledging that each department is unique, the cross‐comparison enabled inter‐ departmental learning for KM and recommendations to be developed that contributed to the organisation’s overall KM strategy as presented in the next section.
6. Implications for the organisational KM strategy The case study organisation’s proactive approach to managing its knowledge stems from the acknowledgement that it is a knowledge intensive organisation. Commissioning this audit is just one example of such proactivity and demonstrates that the organisation needs to maximise the benefits of working in the higher layers of the DIKW pyramid outlined below.
Figure 2: The data, information, knowledge, wisdom pyramid (Ackoff, 1989) Despite this acknowledgement, the management of knowledge to support the achievement of organisational goals is complex. A combination of distinct characteristics contributes to this complexity and brings a unique identity to the case study organisation. From an organisational perspective, it is clear that the organisation has distinctive intrinsic qualities. For example, the combination of the ‘blue skies’ nature of the business; a high level of intellectual capital; a project based mode of operating; and the need to build relationships and sustain meaningful interactions with a range of external (often competing) organisations, is a rare set of characteristics. In fact the atypical nature of the organisation makes it all the more necessary for the design and implementation of a bespoke KM strategy. While such a personalised approach limits the ability to benchmark its practices, it does mean that the strategy is specifically tailored to its requirements. In addition, the process of developing the strategy lends itself to contributing to the process of organisational learning and a commitment to continuous improvement. From an internal perspective it is clear that the high level of complexity continues. There is diversity amongst the departments in terms of the services that they offer and the way that they function; this diversity is further complicated by the dominant organisational culture which is founded on employees of high intelligence who are resourceful but appreciate the full value of timely and accurate information and knowledge. The resultant scenario is one in which employees are independent seekers of information; in turn, this leads to the use of a great range of sources of information coupled with a variety of communication mechanisms through which to transmit and receive information. Hence, it would be very difficult to standardise processes; instead, the diversity of departments and the individuals operating within those departments needs to be respected in the development of any KM strategy. Despite this diversity, it was evident that there was some commonality in themes that had caused concern for participants. It was felt that these commonly occurring themes would be more effectively addressed at the organisational level and, as such, form the basis for recommendations that could inform the organisation’s KM strategy. To enable the recommendations to inform the development of an actionable KM strategy and, due to
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Gillian Ragsdell et al. the perceived challenges associated with each recommendation, each of the seven recommendations was designed to be implementable via a separate knowledge‐based project. However, the interconnectedness of the cultural and technical aspects is not acknowledged in linear textual presentation – their individual listing below neither suggests a particular order of priority or implies that they should be treated as discrete entities. The success of any KM strategy is dependent on both aspects being addressed and being addressed with a holistic mind‐set.
6.1 Holistic approach It is recommended that the practice of holistic thinking underpins any further development of a KM strategy. Without a holistic stance it is possible that there will be improvement in the information and knowledge (IKM) practices of each individual department but it must be acknowledged that optimising each department is no guarantee to ensuring that the IKM practices of the organisation as a whole will be improved. In fact, by attempting to optimise each department’s IKM practices, there is a danger that there will be sub‐optimisation of the overall IKM activities. This may arise if internal optimisation impacts cross division knowledge flows. Therefore, recognising the dependency of each department on another is important in adopting a systemic view of the organisation. At a practical level, each department could share its map of knowledge flows with other departments and identify the interfaces between departments. A process of enquiry about the appropriateness and ease with which each type of knowledge flows between the departments could trigger a process of improvement. While appreciating the potential need to align technical systems with those of external stakeholders, it was deemed important to focus on the internal knowledge processes. Only when these are robust should there be consideration of interactions with external stakeholders. This approach should make progress more evident to staff – quick wins will motivate them – and confidence will be gained for improving external processes.
6.2 Nurturing the organisation’s culture The organisational culture, as expressed by the participants, is something for the organisation to be proud of. Open mindedness and awareness of the need to avoid the ‘silo mentality’ are key components of a knowledge culture. Supporting, encouraging and rewarding these attitudes throughout the organisation will reinforce their value until they are natural practices for all employees. The nature of the organisation’s work means that employees are highly intelligent and are experts in their specialist field. They are also experts in sourcing information and this is an attribute to hold in high esteem. By commissioning the knowledge audit, the organisation has made a good start in raising awareness and generating a feeling of ownership towards the emerging KM strategy. Inviting genuine participation from a wide cross section of staff (if not all) in the process of developing the KM strategy will surface current good practice in the organisation and will promote commitment to the implementation of the strategy.
6.3 Clarity of the strategic message The participants in the knowledge audit provided firm evidence that there was a need to improve internal communication of the evolving strategic message for the organisation as a whole. The organisation is currently going through a mid‐term review analysing its future role, but the knowledge audit re‐emphasised the importance for staff of clear strategic messages about the organisation’s purpose and future. Greater clarity will lessen confusion and uncertainty amongst employees, and result in a clearer sense of purpose, both in general terms and with respect to information and knowledge needs. In turn this will have a positive impact overall on information dissemination and on decision making in projects.
6.4 Better organisation and searchability of information It is evident from participants’ comments that huge sources of rich information are stored. However, in order to improve the current practices and maximise the benefits of this information, the following recommendations are made:
Information needs to be organised and tagged in a more consistent way to reduce the time spent finding ‘relevant’ information.
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Individuals in all departments need to be made aware of the structure in which information is to be organised and stored.
There needs to be better search mechanisms in place for the various systems, in particular the shared drive.
Technical information needs to be translated into simplified summaries to enable individuals from non‐ technical backgrounds to develop a better understanding of the full range of projects undertaken.
Where possible, systems, or their interfaces, should be integrated to provide a single view of information.
Consideration should be given to creating an ‘Information Manager’ role to implement the recommended changes and improvements.
Policies for handling confidential information differently to non‐confidential information should be communicated clearly to employees across the organisation.
6.5 Face to face interactions within the organisation It was clear from the interviews that participants benefitted from conversations with colleagues. However, there was no obvious ‘water cooler’ effect and some departments seemed to have an atmosphere that hindered informal discussion. So, although there is a good selection of communication tools in place at the organisation, face‐to‐face interactions need to be prioritised and the availability of individuals needs to be increased so as to achieve this. In addition, informal discussion should be explicitly valued.
6.6 Overcome departmental communication barriers Some participants conveyed a strong message about the lack of communication between departments. An increase in informal inter‐departmental collaborations could prove beneficial. For example, more informal team activities, team days and ‘speed‐updating’ sessions, would provide opportunities for individuals to learn about and ask questions to individuals in other departments and consequently improve knowledge sharing.
6.7 Regular knowledge audits The results of this audit were intended to inform and contribute to the organisation’s KM strategy and help understand the current knowledge state. For continuous learning and improvements, it is recommended that further knowledge audits be carried out periodically. The outcomes from this audit will empower the organisation to refine further audits, and assist in identifying particular areas to focus on. It is hoped that they will also generate greater ownership of the strategy by employees and trigger a greater uptake for the data collection methods.
7. Summary Three key principles arise from the knowledge audit process. Firstly, it is important to support and enhance the emerging knowledge culture. Successful management of ‘softer’ issues are key to any change programme – implementing a KM strategy is no different. Secondly, the recommendations highlight a need to resolve issues related to the management of organisational information. Improving information management practices in the organisation before progressing the KM strategy will raise the chances of its success. Finally, it is apparent that a greater understanding of the organisation’s strategy by all staff will enable future IKM practices to align more firmly with the overarching organisational goals. Lessons were also learned about the process of undertaking a knowledge audit. Herein the audit has been more than Debenham and Clark’s (1994) management document; rather it is an important stage in the development and implementation of the organisation’s KM strategy, and, as such, was not without its challenges. First, there were difficulties in collecting data. Conducting interviews is an intense and time‐ consuming process but was made more problematic by the participants’ busy schedules. The researchers were also aware that, since knowledge audits generally involve only a sample population of the organisation, the entire organisation may not be represented accurately. Analysis of the data is also a time consuming process. In this instance, analysis at the departmental level proved to be an effective approach since interviewees at the departmental level tended to use similar terminology and have related roles, requirements and expectations. The knowledge maps compiled at this
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Gillian Ragsdell et al. level were very useful in illustrating the communication channels and systems used to share knowledge. In this particular case study, composite inter‐departmental knowledge maps, akin to Burnett et al (2004), were not felt to be realistic nor informative as value and meaning could be extracted more effectively at the departmental level. Despite this, the maps were useful as input to the cross‐departmental analysis of communication channels/systems and helped inform the organisation‐wide analysis. Finally, when undertaking analysis at the organisational level it was important to ensure that the outcomes and recommendations could lead to an actionable strategy. Obtaining employee buy‐in for KM initiatives is important and involving staff in the knowledge audit process helps engender a sense of ownerships; quick wins and timely developments help communicate to employees the importance and value of the KM process.
References Ackoff, R.L. (1989) From Data to Wisdom, Journal of Applied Systems Analysis, Vol 15, pp 3‐9. Biloslavo, R. & Trnavčevič, A. (2007) Knowledge Management Audit in a Higher Educational Institution: A Case Study. Knowledge and Process Management, Vol 14, No. 4, pp 275‐286. Bontis, N., Fearon, M. & Hishon, M., (2003) The E‐Flow Audit: An Evaluation of Knowledge Flow Within and Outside a High‐ Tech Firm. Journal of Knowledge Management, Vol 7, No. 1, pp 6‐19. Burnett, S., Illingworth, L. & Webster, L., (2004) Knowledge Auditing and Mapping: A Pragmatic Approach. Knowledge and Process Management, Vol 11, No. 1, pp 25‐37. Choy, S.Y., Lee, W.B. & Cheung, C.F., (2004) A Systematic Approach for Knowledge Audit Analysis: Integration Of Knowledge Inventory, Mapping and Knowledge Flow Analysis. Journal Of Universal Computer Science, Vol 10, No. 6, pp 674‐682. Dattero, R. & Galup, S. D., (2007) The Knowledge Audit: Meta‐Matrix Analysis. Knowledge Management Research and Practice, Vol 5, No.3, pp 213‐221. Debenham, J. & Clark, J., (1994) The Knowledge Audit. Robotics and Computer‐Integrated Manufacturing, Vol 11, No. 3, pp 201‐211. Gourova, E., Antonova, A. & Todorova, Y., (2009) Knowledge Audit Concepts, Processes and Practice. WSEAS Transactions on Business and Economics, Vol 6, No. 12, pp 605‐619. Hylton, A., (2002) A KM Initiative is Unlikely to Succeed Without a Knowledge Audit. Knowledgeboard.com. Levy, M., Hadar, I., Greenspan, S., & Hadar, E., (2010) Uncovering Cultural Perceptions and Barriers During Knowledge Audit. Journal of Knowledge Management, Vol 14, No. 1, pp 114‐127. Liebowitz, J., Rubenstein‐Montano, B., Mccaw, D., Buchwalter, J., Browning, C., Newman, B. & Rebeck, K., (2000) The Knowledge Audit. Knowledge and Process Management, Vol 7, No.1, pp 3‐10. Mearns, M.A., & Du Toit, A.S.A., (2008) Knowledge Audit: Tools of the Trade Transmitted to Tools for Tradition. International Journal of Information Management, Vol 28, No. 3, pp 161‐167. Perez‐Soltero, A., Barcelo‐Valenzuela, M., Sanchez‐Schmitz, G., Martin‐Rubio, F. & Palma‐Mendez, J.T. (2006) Knowledge Audit Methodology With Emphasis On Core Processes. In European and Mediterranean Conference on Information Systems, pp 1‐10. Reinhardt, R., (2003) Theoretical Basis of a Knowledge Audit: An Integrative Measurement Approach. In Proceedings of I‐ KNOW ‘03–3rd International Conference on Knowledge Management, pp 2‐4. Wexler, M. N., (2001) The Who, What and Why of Knowledge Mapping. Journal of Knowledge Management, Vol 5, No. 3, pp 249‐264.
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Shared Knowledge: Eliminating the “Ba” Thomas Schalow University of Marketing and Distribution Sciences, Kobe, Japan ijinkan@mac.com Abstract: In one of the supreme ironies in ICT, social networking (SN) companies such as Facebook, search engine (SE) companies such as Google, and other major knowledge management (KM) organizations continue to rely on Nonaka and Konno’s (1998) “Ba,” or physical space, for the internal creation and management of new ideas. Apparently, these companies believe employees still need to congregate in a physical place ‐ an office ‐ in order to share ideas, as opposed to creating and sharing knowledge in a totally virtual environment. In this theoretical exploration of physical space I propose that the next challenge facing knowledge management is eliminating the “Ba,” and in this paper we examine how that will facilitate the successful sharing of knowledge in a world where place truly ceases to have meaning. The challenge is formidable, because human beings still seem to require a common time and place to meet in order to be synchronous. We see this in all aspects of our lives, from our work offices to our educational institutions. Although we have become adept at knowledge sharing in virtual spaces in an asynchronous mode, we are still bound by the chains of human culture and psychology in the synchronous mode. The reason seems to be that we still rely on physical places to reference and give organization to human societies, and thereby provide the conditions for what sociologists refer to as homophily. Mäkelä et al (2007) have shown that we still tend to share knowledge to a greater degree with people who resemble us, or share our cultural identities, but Jameson (2007) has shown that in a globalized world, where national borders have become blurred, it can no longer be presumed that cultural identity is equivalent to nation, or to some shared physical space. In fact, in virtual spaces we need to accept that knowledge will increasingly be created and shared by individuals who are not homophilous, as discussed by Murthy (2013) in regard to Twitter, and therefore need to adapt to a world without “Ba”.Nonaka et al (2000) have identified four types of “Ba”: originating “Ba”, dialoguing “Ba”, systemising “Ba” and exercising “Ba”. This paper will look at each of these “Ba” and how they could be eliminated. Nonaka has stated that although “Ba” resemble Wenger’s (1999) communities of practice (CoPs), “Ba” need energy to operate, while communities of practice remain functional, to some extent, even though they are neither nurtured nor maintained. I believe it is this need for energy that limits the “Ba,” and ultimately limits our ability to optimally engage in knowledge sharing in a world where space and place holds less and less meaning for more and more people. When we are finally able to expend less energy clinging to our “Ba”, we will have more time and energy for creating the knowledge and ideas that were the reasons people first began to construct their “Ba” ‐ their physical communities of practice ‐ in a time and place before the existence of what we now know as cyberspace. Keywords: knowledge management, communities of practice, virtual environment, situational workplace, self‐structuring, homophily
1. Introduction The Indian economic historian Dharma Kumar is said to have joked that “Time is a device to prevent everything happening at once; space is a device to prevent it all happening in Cambridge.” (Hodgson, 2001) In the immediate post‐WW2 world there was little danger of everything happening at Cambridge, with or without the confines of space, as the geographic center of the world inexorably shifted to America. A recent decision by top management at Yahoo to suspend the telecommuting privileges of it employees seems designed to ensure that it will be the office space at company headquarters in Sunnyvale, California that defines the center of the world for knowledge creation and sharing, within the time parameters of office hours between 9 am and 5 pm. Yahoo is, of course, pursuing a very strict definition of the workplace as a physical space where work is done. In past generations we referred to this space as an office, factory, or workplace, but in the past two decades it has become fashionable among knowledge management scholars and consultants to speak of this space as a “Ba” ‐ a term first popularized by the Japanese scholar, Ikujiro Nonaka, along with Noboru Konno (1998). Welsh scholar Dave Snowden (2000) has attempted to put a Western, and specifically Welsh, face on the theory with the term “Cynefin,” but the concept of a physical place where work is done remains basically the same. Nonaka, Toyama, and Konno (2000), in a subsequent interpretation of Nonaka’s original idea, further divided their “Ba” into four distinct categories. Echoing Adam Smith’s ideas about the division of labor, they identified an 1) originating “Ba,” where knowledge is created, a 2) dialoguing “Ba,” where ideas can be discussed and refined, a 3) systematizing “Ba,” where knowledge is embedded, and an 4) exercising “Ba,” where ideas are
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Thomas Schalow translated into action. This very industrial age interpretation, similar to Smith’s observations about the production of pins, sought to identify the place where each of these aspects of knowledge creation or management would occur. In a nod to evolving visions of virtual environments, Nonaka does suggest that the “Ba” need not necessarily be physical, and Nordberg (2007) develops this concept to its logical conclusion when he posits the space could even be virtual. Even virtual space, though, retains the concept of “place,” where actors come together to interact. Place seems so important to us because it so often defines how we perceive reality. Yet, it is not relevant to all aspects of our lives, and is a concept we can dispense with if we choose. We do not normally emphasize, for example, that we are breathing air from a particular location, unless that air is significantly different from the air in another location. We might emphasize or reference place when the air is polluted, or thin, or particularly refreshing, but most of the time it is just air. It surrounds us, and the place we choose to breathe it has little significance. In fact, the air does not really exist in a place, as air does not need to exist within boundaries, unless we choose to confine it and impose the concept of location upon it. It is everywhere, and thus not in any one place. Eventually, we will see the network ‐ what we today think of as the Internet ‐ in this manner. It will exist all around us, and we will exist within it, and a sense of place will cease to be important, unless we choose to define our location. At present we find location‐based services useful, or at least useful to companies trying to market their products to us. Location provides structure, and structure allows for control. This is the reason for Yahoo’s decision to require its workers to occupy a physical space. Even most ICT companies are not yet comfortable with the relatively modest idea of work being done in an unsupervised, virtual environment. Without a physical realm in which to reign, management is acutely aware of its fading value in a world where knowledge is created beyond the traditional confines of time and space. The fact that ideas about “Ba” and knowledge management within physical work spaces have come from Japan will present no surprise to students of Japanese social, economic and labor systems. Perhaps more than any other system in the world, the Japanese system, social and economic, stresses the importance of relationships nurtured by physical presence as the key to cooperation and productivity. (Nakane, 1972 and Doi, 1981) Nonaka and Nishiguchi (2001) acknowledged this when they said that sharing knowledge requires “sharing the same experience through joint activities such as being together, spending time, or living in the same environment.” In this context, an actual physical space is a requirement for the creation and exchange of knowledge. As both Nakane and Doi note, however, maintaining the Japanese social structure, and harmony within the workplace, requires incredible inputs of energy. Doi, in particular, has gone on to study the tremendous psychological burden the need to maintain harmony via physical presence places on Japanese workers. Companies that attempt to control physical spaces also need to employ psychologists and sociologists to minimize conflict, and thereby facilitate knowledge sharing, within their physical space. Societies and nations need to engage police and military forces to maintain control over their physical spaces. This attempt to control physical space produces tremendous pressures as well as conflict. More importantly, the attempt to control physical space is moving in the opposite direction of trends toward ad‐hoc and situational workplaces, where the idea of “Ba” is essentially irrelevant. Even virtual working environments – online schools and virtual offices, for example ‐ should be seen as representing a transitional phase that will eventually fall out of favor. No matter how virtual the “Ba” becomes, it nonetheless relies on a structured environment within which workers interact. Social and economic developments, however, are pushing us inexorably toward a free agent world, where it is the workers themselves, and not knowledge managers, who decide how meaningful work is accomplished. One could say that the workers will become knowledge managers, but it would be more correct to say that the system will self‐structure, and the network itself will become the knowledge manager. We are already seeing this trend toward self‐structuring in Wenger’s (1999) communities of practice (CoPs). Ad‐hoc communities do not need to expend the vast amounts of energy required by societies or nations to maintain their membership, and could be constituted by methods not requiring physical or even virtual spaces. Due to the focus of their members, they can be better vehicles for sharing or creating knowledge than the
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Thomas Schalow artificial creations expressed in the physical world through societies or nations, without the need to enforce Nonaka’s requirement for “living in the same environment.”
2. The network as a knowledge management device If space is a device to prevent everything from happening in Cambridge, the network is a device to eliminate the need for a human knowledge manager. The self‐structuring we are already beginning to see with Web 2.0 will continue to develop, as we move away from a need for human beings to occupy a synchronous space‐time nexus. Thousands of years of social evolution have been preparing us for this new future beyond the limits imposed by space and time. It began with communication at a distance, allowing our knowledge, ideas, and even our spirit to be transmitted in space, and eventually over time with the invention of the written word, without the need for our physical presence at the point of reception. By merely speaking, shouting, or broadcasting, we were able to communicate our instructions to others, and thus share knowledge beyond the limitations of our physical space. We are quite comfortable with these forms of communication today, and it might therefore seem somewhat surprising that we still apparently require sharing common times and places for so much of our existence. We continue to depend on schools as places for students to meet with teachers at a certain time. Offices are places work is done by employees engaged to work between certain hours. Factories are places where products are assembled over fixed periods of time. Knowledge management theorists continue to depend on the concept of place to refine their theories of knowledge. (Kaiser and Fordinal, 2010) It is difficult for many of us to imagine a world where perceived requirements for synchronous interaction in a defined physical space could cease to be a factor in knowledge creation and sharing. Of course, some schools are now online, and some office workers have the freedom to work from home. However, a key obstacle to the continued growth of both online schools and telecommuting programs arises with the issue of supervision. Monitoring even virtual spaces is difficult, and supervising without the confines of space is perhaps impossible. Yahoo recognized this problem, and chose to return to a system that made supervision of employees easier, within the confines of a defined workspace. Online schools don’t have this option. No matter how many safeguards are put in place in order to ensure that students are doing required work on their own, or taking tests without outside assistance, certain levels of trust and uncertainty remain embedded in the system. In abandoning the “Ba,” or the place where knowledge exchange takes place, online schools are also abandoning a great deal of the supervision. The online schools, of course, have in a sense substituted the physical space of the classroom and examination halls for virtual equivalents. In fact, they rely on this virtual space to assert the small degree of supervision they yet control. The future of knowledge creation and sharing, however, is not in virtual spaces, but in placeless, or at least ad‐hoc, and situational spaces. We no longer require space as a device to prevent everything from happening in Cambridge. Everything can happen anywhere, as well as everywhere, according to one’s viewpoint. The creation of knowledge, and even the sharing of that knowledge, takes place in a context, and not a place. (Chun and de Alvarenga Neto, 2010)
3. High priests of knowledge and knowledge nomads As human beings we need to physically exist in a place. However, we do not need to inhabit that space, in the sense of becoming an inhabitant of, or part of, that space. We may merely, and temporarily, occupy a space, like the air we breathe, without assuming its attributes. Jameson (2007) shows us that physical space does not need to translate into identity, in the way that inhabitation would suggest. Like nomads, we could wander over vast spaces without being tied down to the cultures or societies that define those places, or even the conversations and knowledge exchange we happened to be engaged in at the moment. We would not need to be immediately and irrevocably defined by the cultures or societies of the spaces we physically occupy. Instead, we might choose to define ourselves by the events and experiences we accumulate along our journey, unbound by spaces. The ideas that we create, the knowledge that we share with others, would be a product of the confluence of events and experiences that occur during the time defined by our physical life. The seeds for our ideas could be gathered from distant corners, and they would grow based on the context in which they were planted. The difference between the web as a context, and the web as a location, is substantial. It is embodied in the difference between learning in a classroom, and learning from a chance meeting with someone sharing a
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Thomas Schalow different idea from our own. The knowledge that is created and shared in a classroom presumably has been structured to some degree, with a desire to yield a certain outcome. That outcome can be tested, and supervised by a knowledge manager. The chance meeting, however, has not been designed to yield any particular result. It cannot be supervised, and might yield any result. It is the essence of creativity. One of the most severe criticisms of Nonaka’s theory of “Ba” is that it fails to identity the place where knowledge is created. As Nordberg (2007) notes, providing a place for knowledge creation still falls short of explaining how it is achieved. Before Nonaka, Luft and Ingram (1955) confronted the same issue. Their “Johari Window” theory posited the existence of four distinct quadrants where knowledge resided. They suggested that it was the goal of group work to expand quadrant one, the open area, involving behavior known to self and others, in order to achieve knowledge creation. They presumed that this expansion of quadrant one was to be at the expense of quadrant four, the unknown, with things that are presently inaccessible to the individual or the group. How that expansion actually occurred, however, and how new ideas were actually created, was left unspoken. Almost since the beginning of time it has been assumed that knowledge managers were God’s instruments for the distribution and sharing of knowledge. These managers became the high priests of knowledge, and they retained their power by asserting that knowledge was bestowed upon them alone, by the god of their knowledge structure. The people over whom the priests, or ruling kings and queens supposedly anointed by God, ruled, received knowledge from above, but did not create it. It required a (Protestant) revolution to suggest that each and every person was responsible for their own interpretation of the truth, their own creation of knowledge. Not even the Protestants, however, were willing to relinquish full control of knowledge creation to the people. Societies still required people to be supervised, and knowledge managers were still useful conduits for sharing knowledge, and controlling populations. After all, how could cities be planned, factories be organized on an industrial scale, and societies and economies be harmonized without knowledge managers? In fact, it did take knowledge managers to create Windows (Microsoft, not Johari) juggernauts, and we will probably yet for many years require high priests to keep our social, economic, and network systems functioning in the manner to which we have become accustomed. However, as systems such as Linux have demonstrated, it is possible to create with minimal supervision. What the Internet has shown is that the network can also grow organically. Eventually, the network itself will be able to assume supervision of the digital nomads who will inhabit it, and create the ideas that will push humanity into the future.
4. Knowledge as a confluence of events and experiences This brings us back to the issue of how knowledge is created. For most of human existence, we have been reminded of our need to occupy a physical space in order to produce food, tools, or the products of our everyday lives. Ideas and knowledge were assumed to require the same physical space, though it was always recognized they seemed to come from the ether, or perhaps the spirit of some Muse, even when they took shape within the brain of a particular person, in a particular place. The intuitive recognition that they were born out of the ether, however, should have suggested they were not truly a product of a particular location, and did not require a physical space to come into being. They merely required a medium, which happened to be a human mind situated at a particular space‐time nexus. Knowledge creation, though, is essentially free of space‐time limitations. It coalesces as a result of context, but does not depend on that context to self‐generate. In other contexts it will take different forms, but will retain its essential features. What this means for knowledge creation and sharing is profound. When we set knowledge free from the space‐time that we have previously used to define it, we will move beyond the structure that now limits our attempts to push human evolution to its next level. Physical schools and substantial factories may still continue to provide the vast majority of people with the skills and products they need to survive in a physical world, but true knowledge workers will need to be set free from these physical limitations in order to produce the knowledge that will drive the next stage in human evolution. Evolution, of course, does not occur in a straight line, and at present many societies are witnessing a strong backlash against multiculturalism, and the perceived need for shared beliefs in a shared physical space might
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Thomas Schalow seem to be our only hope to escape chaos and destruction. Yet, a careful reading of history shows that it is actually the tribalism that has characterized humanity for most of its time on this earth that presents us with the greatest dangers and limitations. Within our tribes we exhibit what sociologist refer to as homophily – a love for sameness, providing us with a convenient reference to define ourselves. As Makela, Kalla, and Piekkan (2007) have shown, we still tend to share knowledge, for the most part, with people who are like us in some respect. One of the reasons this is true pertains to the simple matter of language. Without the benefit of real‐ time translation we find it difficult to share knowledge, or create it, with persons who use a language that is unintelligible to us. Culture can also be an obstacle to the sharing of knowledge – perhaps even more than it has been a force for the creation of knowledge. Not all cultures share basic values, and as a result they may find it difficult to express or share those values with outsiders. If we think about all the differences that divide one individual from another, we might even conclude it is a wonder we manage to communicate any knowledge at all. Our tendency toward homophily limits the creation and sharing of knowledge at least as much as it facilitates it. Yet, as we move beyond the limitations imposed by our languages, our cultures, our religions, and our basic beliefs, we find there is indeed much we wish to share, or need to share, in order to grow and prosper. In the virtual Twitter space we can come together with people who are quite different from ourselves in order to share those things that matter to us, as Murthy (2013) has shown. Facebook connects us with friends, or at least acquaintances, from all over the world. Google makes it possible to find information in languages we are not fluent in, and thereby exposes us to views we may never have otherwise considered. Space has in the past defined us, and limited us. We can anticipate an explosion of knowledge creation and sharing when space becomes irrelevant to what we aspire to be and know, and knowledge becomes a confluence of events and experiences.
5. In conclusion It is ironic that Yahoo, and other companies which profess to be knowledge companies, should be seeking to define the conditions and places where knowledge can be created. What this should tell us is that these companies have ceased to be innovators, and are now in the business of controlling their virtual realm. They may be mighty kings and queens at present, but they will eventually prove irrelevant to the future history of humanity. Our theoretical examination of the concept of space has shown that physical realms, whether corporate or national, will eventually become unimportant to humanity. We will no longer define ourselves by our location, and our knowledge will also be set free from the limitations of space and time. Knowledge creation will take place in an ether undefined by space. We will be bathed in new ideas, innovations, and knowledge, just as we are bathed in the air that nourishes our existence. Maintaining a physical presence in a specific location requires tremendous inputs of energy. Today we find it relatively difficult to create knowledge because we expend so much energy in merely maintaining our attachment to locations defined by languages, cultures, beliefs, and relationships. When humanity becomes nomadic, like the wind, it will be free to flow in entirely new directions, with only minimal inputs of energy. Creativity will become the essence of our universe. The new world, undefined by physical location, will need to be self‐structuring in order for there to be any meaning to whatever is created. Meaning will come into being spontaneously, and most often merely temporarily. Maintaining meaning for long periods of time merely requires too much energy, and is unimportant for defining where humanity is moving. In this self‐structuring confluence of events, experiences, and knowledge, knowledge managers will become superfluous, and perhaps even obstacles to further growth. The system will provide all the supervision and structure needed to manage the knowledge that is created. Of course, we can expect a great deal of protest and resistance from today’s knowledge managers as we move toward this more democratic world, where knowledge creation no longer relies on the humbug of “Ba”. Context will replace space as the creative force, and knowledge creation and exchange will eventually become freeform. There will be no optimal outcomes to be measured or supervised – only possibilities. Each outcome will be valid in its own way, and there will no longer be any need for space to define who we are, what we
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Thomas Schalow believe, and what we can create. When we are able to recognize the implications of this statement, we will be ready to unleash a tremendous force for the creation and exchange of new knowledge.
References Chun, Wei Choo and de Alvarenga Neto, Rivadavia C.D. (2010) “Beyond the ba: managing enabling contexts in knowledge organizations,” Journal of Knowledge Management, Vol 14, No. 4, pp 592‐610. Doi, Takeo. (1981) The Anatomy of Dependence. Kodansha, Tokyo. Hodgson, Geoffrey M. (2001) How Economics Forgot History: The Problem of Historical Specificity in Social Science. Routledge, London. Jameson, Daphne A. (2007) ”Reconceptualizing Cultural Identity and Its Role in Intercultural Business Communication,” Journal of Business Communication, Vol 44, No. 3, pp 199‐235. Kaiser, Alexander and Fordinal, Birgit. (2010) “Creating a ba for generating self‐transcending knowledge,” Journal of Knowledge Management, Vol 14, No. 6, pp 928‐942. Luft, Joseph and Ingham, Harrington. (1955) “The Johari window, a graphical model of interpersonal awareness,” Proceedings of the western training laboratory in group development, Vol 5, No. 1, pp 2‐6. Makela, Kristina, Kalla, Hanna K., and Piekkan, Rebecca. (2007) “Interpersonal similarity as a driver of knowledge sharing within multinational corporations,” International Business Review, Vol 16, No. 1, pp 1‐22. Murthy, Dhiraj. (2013) Twitter: Social Communication in the Twitter Age. Polity, Boston. Nakane, Chie. (1972) Japanese Society. University of California Press, Berkeley. Nonaka, Ikujiro and Konno, Noboru. (1998) “The concept of ‘Ba’: Building a Foundation for Knowledge Creation,” California Management Review, Vol 40, No. 3, pp 40‐54. Nonaka, Ikujiro and Nishiguchi, Toshihiro. (2001) Knowledge Emergence: Social, Technical, and Evolutionary Dimensions of Knowledge Creation. Oxford University Press, Oxford. Nonaka, Ikujiro, Toyama, Ryoko and Konno, Noboru. (2000) “SECI, Ba and Leadership: a Unified Model of Dynamic Model Creation,” Long Range Planning, Vol 33, pp 5‐34. Nordberg, Donald. (2007) “Knowledge Creation: Revisiting the ‘ba’ Humbug: People and ‘Latent’ Knowledge in Organizational learning,” Icfai Journal of Knowledge Management, Vol 5, No. 6, pp 7‐16. Snowden, Dave. (2000). "Cynefin: a sense of time and space, the social ecology of knowledge management", in Despres, Charles and Chauvel, Daniele, eds. Knowledge Horizons: The Present and the Promise of Knowledge Management. Routledge, London. Wenger, Etienne. (1999) Communities of Practice: Learning, Meaning and Identity. Cambridge University Press, Cambridge.
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Correlation Between Individual Knowledge and Organizational Learning Process Christian‐Andreas Schumann and Claudia Tittmann University of Applied Sciences Zwickau, Germany christian.schumann@fh‐zwickau.de claudia.tittmann@fh‐zwickau.de Abstract: A team is a working group characterized by a certain amount of people who work at a certain amount of projects (multiple relations). Each individual of the team has different experiences and knowledge background. That means each individual followed different learning processes in its life. This leaded to a different knowledge structure. Exactly this fact, to see the individual as a knowledge resource, which is a in this context defined as knowledge node that can be part of one or more social networks, in private and/or in professional environment, will be described in this paper. By abstracting and projecting the individual’s knowledge into the model of a knowledge node, there are interesting possibilities to improve the learning processes. A knowledge node is defined by knowledge domains, levels, and volumes. Furthermore, the knowledge node has a behavior on one hand for improving the knowledge, on the other hand for interacting with other knowledge nodes. Each knowledge node is in a permanent learning process. There are diverse motivations for learning, like curiosity, professional purpose, personal interest. The result is a complex individual learning process. Objective of the paper is to describe a learning process inside a social group, like a working team, on the basis of the individual knowledge resources – using the model of knowledge node ‐ and learning processes of each group member. Results shall be the improvement of the group learning (organizational learning) process, which provides the knowledge sharing and improvement inside the group; the effects on the learning behavior of each individual; and the influence on the knowledge node, the knowledge structure of the individual, itself. Keywords: knowledge node; learning process; individual knowledge
1. Background and objectives Knowledge is the wholeness of skills and abilities, which is used by individuals for the solution of tasks or problems (Probst, Raub et al. 1999: p.44). The quantity of knowledge includes the theoretical insight, but also the practical rules, facts, and activities of the daily life. Fundamental for knowledge are data and information which are set into a context. Further, knowledge forces the development of competences, the purpose‐orientated acting (North 2005), and the competitiveness. It plays an important role in our current development to the knowledge society. Knowledge can be categorized in different ways, depending on the view or examining focus. The most used is the differentiation of Polanyi ((Polanyi 1967)) into explicit and tacit knowledge. Explicit knowledge can be articulated, expressed, codified, and saved on media (Broadbent, FitzGerald et al. 1986). It is more or less common or inside a community available or accessible knowledge. The (economic) view on the value of this kind of knowledge leads to following assumption in the economic perspective seeing knowledge as intangible assets: When the explicit knowledge is new, it has a high level of innovation and with this a high knowledge value. The more this knowledge is shared, the less it is worth. Hence, the tacit knowledge represents an individual’s experiences and results of applied knowledge. It cannot be easily transferred or expressed; there are special methods (e.g. storytelling) necessary (Lerner 1992). Additionally to the explicit and tacit knowledge exists the potential knowledge ((Tittmann and Schumann 2009)). That is the knowledge which is not yet appreciated by the individuals; it is not realized and aware by them that they own it. With all activities new data and information are saved in mind, but not all of them can be set in a useful context; in most cases in a later point of time. Within organizational structures where many people are busy with complex structure of data and information, another kind of knowledge develops: the organizational knowledge. This is the wholeness of tacit and explicit knowledge inside an organization (Mescheder and Sallach 2012:p.13‐14). Examinations of organizations show
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Christian‐Andreas Schumann and Claudia Tittmann that the explicit knowledge is only a small part in relation to the huge amount of tacit knowledge inside an organization (Keller and Kastrup 2009:p.14). Knowledge plays an important role for the individuals and the organization: because of special tasks there is special and new – not always common – knowledge. Further, individuals join and leave the organization; with the individuals fluctuates also their tacit knowledge. This makes a knowledge base for the organizational knowledge necessary. Each individual is involved in a lifelong learning process, always depending on new tasks, challenges, and ideas. A part of this knowledge stays also within the organizational knowledge base where the individual is active and means the organization learns. The part of knowledge that stays only in the minds of the members of the organization is lost for the organization, when the member leaves (Argyris and Schön 1996:p.27). Figure 1 show this cycle of learning with the development of individual tacit knowledge to organizational explicit knowledge by origination, communication, collectivization, and exercising from the individual up to the organizational network. It is the process of organizational learning, in a systemic view the process of organizational knowledge transformation (Wolf and Hilse 2009:p.125). Organizational learning means on one hand a social scientific view on the learning organization with the behavioral patterns, causes and effects, and the examinations about the structure of the organization. On the other hand organizational learning can be concentrated on the learning process on the organizational level. In this paper we will follow the second path.
Figure 1: Cycle of organizational learning There are individuals who belong to at least one organization and they influence the organizational knowledge. But also in the other direction, the organizational knowledge influences the individual knowledge. They can participate on the existing knowledge base and the working projects. As knowledge is the contextual use of information and data, it leads to abilities and skills, finally to competences (North 2005:p.36). Competences are the ability of acting in a certain correct and successful way. It is the quality of being adequately or well qualified physically and intellectually. It is an expression of the capability, skill, and (very important) the inner will to solve tasks in a special subject field or problem area. If
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Christian‐Andreas Schumann and Claudia Tittmann only the capability and the skill exist, we can talk about a „qualification“. It is there, but does not need to be used. But the practical use and application of the qualification by the individual for the fulfillment of tasks makes a competence out of it. That’s why the term competence is more and more used instead of the term qualification. It is created out of specialized knowledge and practical experience with this knowledge, embedded into lifelong learning.
2. Learning scenario Learning is the permanent process for individuals of acquiring information, knowledge, skills, etc. But it takes place not only on the individual’s level, also on the level of team/group and organization. It consists of the two dimensions: intentional learning for reaching a focused knowledge goal and implicit learning, the learning by doing and from others. That means one aspect of learning is that we learn from the history and tradition (Popper 1987:p. 61) of ourselves and the mankind and the use of the experiences of the past. Also the behavior patterns can be derived from the previous times. But in our fast changing world develops more and more the second aspect, the learning by and with others/within teams and by applying this knowledge. In this way of learning the individuals reflect quickly the new learnt facts with feedback and evaluation. The third aspect of learning is the continuous communication of the vision and strategic plans of the organization (Kemin‐Buch, Unger et al. 2008:p.23). This strategy forces the members of the organization to learn and enrich knowledge and develop competences into focused directions. Additionally, each individual represents a different learning type, influenced by genetic preconditions, by environment, and by the own evolution process (Vester 1999). That means there are different styles of learning and thinking; and also different abilities in optical/visual, auditory, haptic, and cognitive learning. Following, this reflects to the learning process of the team/group and organization. Generalized it is to say that learning takes place in different levels, contexts, and dimensions. A look into a typical scenario of project orientated working organizations shows that there is – besides of other resources ‐ an amount of individuals which work on an amount of projects dealing with complex data, information, and knowledge. Each individual can be involved in several projects as project member (PM), and each project is realized by at least one individual. For the realization of projects ‐ as precondition ‐ special knowledge and competences are necessary. The knowledge comes on one hand from previous projects and is collected in the organizational knowledge base (organizational knowledge); on the other hand it is the knowledge of the individuals including their competences (Figure 2). For fulfilling a project goal, it is essential to close competence gaps by learning and/or integrating individuals with the needed competences.
Figure 2: Enriching individual’s knowledge and competences
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Christian‐Andreas Schumann and Claudia Tittmann The ingoing knowledge and competences into a project influence the success and results of the project. The outgoing (produced/developed) knowledge of the project is saved in the organizational knowledge base and reflects to the individuals which worked on that project and increases their individual knowledge and improves the competences in the project affecting fields. Thus, each project brings a knowledge‐ and competence‐plus to the participating individuals! Not only the subject‐specific competences, but also e.g. social competences. So far it seems that the win‐situation is more on the side of the individuals. That’s why organizations should profit and be influenced by those aspects for the organizational learning process (Argyris and Schön 1996:p. 32):
Reflecting and analyzing previous projects/experiences of successful and non‐successful activities
Conclusions because of causal dependencies between activities and results
Description of the changing/adapting organizational environment and the challenge for further achievements
Analysis of the possibilities and limitations of alternative organizational strategies, structures, and information‐/knowledge systems
Description of contrary opinions and interests which arise because of complexity and insecurity inside the organization
Planning a strategy for the organization and processes to reach these goals
Analyzing experiences of other organizations (or former phases of the current organization)
All results of these reflections are chances for the organizational learning and the evolution of the organization. In this learning path the individuals and the organization can stabilize existing competences, create new competences, and develop the existing competences.
3. Projection into a model All the information about projects, learning, and knowledge shall now be integrated into a model to derive rules (a) from individual to organization: between the development of organizational competences out of individual knowledge grouped by projects; (b) from organization to individual: improvement of individual’s knowledge and competences caused by project Each individual increases its knowledge by learning, intentional or implicit. The increasing and applying of knowledge develops the qualification and influences the competence of the individual. In the economical view it increases the own intangible assets and its market value. If you look at the knowledge of an individual, the individual as a knowledge node (Schumann and Tittmann 2010), than you can say that knowledge itself – not important which knowledge domain or level – has not a real value. Only the use or usability of knowledge makes the value. The application of knowledge leads to competence and extends the knowledge. Figure 3: Individuals as Knowledge Node (Tittmann and Schumann 2012) Our thesis is that each individual can be seen as a knowledge node (Figure 3). And each knowledge node consists of different knowledge resources which contain knowledge in a specific domain, quantity, and quality. A competence concentrates knowledge from several knowledge domains and applies them. The correlation between competence and knowledge is now explained. Further, the impact of project is analyzed. More and more the working processes are divided in to defined tasks and so called projects. Projects are started for different kinds of reasons:
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Projects fitting to the existing competences of the organization
Projects for enclosing new competence fields
Projects for cooperation with other organizations (competence‐crosslinking)
Projects for monetary profit
Projects for reputation
Advantages are the framed structure and ability for control and organize the processes. In the context of a project which has to fulfill a certain goal, the following strategy inside an organization can be used (figure 4):
Finding out which knowledge and which competences are necessary to fulfill the project goals
Finding out which knowledge and competences already exist
Finding out the knowledge and the competence gaps
Analyzing how the knowledge and competence gaps can be closed by learning and/or by adding external competences
Closing the competence gaps
Fulfilling the project goal
Figure 4: Fulfilling project goals Not all necessary competences are already available inside the organization. Therefore, a permanent need for learning is there. When the members of the organization have a well‐marked learning competence, the knowledge and/or competence gaps can be closed by the organization itself.
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Christian‐Andreas Schumann and Claudia Tittmann Besides of important economic advantages like cost‐, resource‐planning, the structuration of projects leads to a better overview and use of competences and a specialized learning which improves the knowledge. The result of absolving such a project is that:
The project goal is fulfilled and with this a research or task is fulfilled
Each participating individual improves its knowledge and competence
The team/group increases the knowledge base
The organization develops new competences (business fields)
For further examinations it is interesting to bring the relations between knowledge, competences, projects and individuals/organizations into the known model of knowledge nodes (Figure 5).
Figure 5: Analogies between knowledge and competence of an individual Each knowledge node contains several knowledge resources, which are mainly determined by a knowledge domain. The competences of the same individual are developed out of these knowledge resources into competence resources. That means, an individual is a knowledge node and also a competence node, depending on the point of view and the context. A competence resource consists of parts of several knowledge resources. Normally is not the complete knowledge resource included, only the necessary part. For increasing the competence, it is interesting that more than the minimum part of the knowledge resource is used. Further, there are several processes of evolution and development of the knowledge and competence resources:
Advancement of the knowledge resource, what influences indirectly the competence resource
Advancement of the competence resource, what influences indirectly the knowledge resource
To fulfill a project task it is necessary to apply existing knowledge and competences. Those applications lead to the stabilization and bring out new knowledge and new competences. As normally more than one member works at the same project and each is seen as a knowledge node (KN1, KN2, KNx), an exchange of knowledge is activated. Because of working at the same main task, communication with transporting mainly tacit knowledge between the KN is needed. This way, during the progress of the project tacit knowledge is transformed more and more into explicit knowledge and increases the knowledge of the other individuals, the group and finally, the organization. This affects that each project externalizes tacit knowledge and let the organizational knowledge grow. But in the same project a lot of new knowledge is created, too. This causes on the different subtasks which have to be fulfilled by single project members or a subgroup. It results in the fact that new tacit knowledge is generated in the individual and increases the individuals’ competence.
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Christian‐Andreas Schumann and Claudia Tittmann That means, it exists a kind of cycle in each project, which brings on one hand parts of the tacit knowledge of each KN to the organizational knowledge base; but on the other hand in the same timeline new tacit knowledge inside the KN arises.
4. Conclusion Fact is that each individual as well as the groups and organizations it belongs to or is involved into a continuing process of learning. But this learning takes not place in the same manner. E.g. you cannot say that when the member of an organization achieves new knowledge by learning and applying existing knowledge, the organization has the same learning effect. The new knowledge of the individual goes not in a one‐to‐one copy into the organizational knowledge. There is a more decelerated process. Only in further projects, that knowledge is externalized. But exactly then the individual already develops new tacit knowledge. The individual – each group member – is or can be a knowledge driver for the organization. But as more knowledge is hidden by the individual, as more it restricts the innovation of the organization. This can be partly stimulated by increasing the motivation for sharing knowledge. It is important to integrate special methods of knowledge transfer inside the organization.
References Argyris, C. and D. A. Schön (1996). Organizational Learning II. Theory, Method, and Practice. London, Addison‐Wesley. Broadbent, D. E., P. FitzGerald, et al. (1986). "Implicit and explicit knowledge in the control of complex systems." British Journal of Psychology 77(1): 33‐50. Keller, C. and C. Kastrup (2009). Wissensmanagement. Wissen organisieren‐Wettbewerbsvorteile sichern. Berlin, Cornelsen. Kemin‐Buch, B., F. Unger, et al. (2008). Lernende Organisation. Sternenfels, Verlag Wissenschaft&Praxis. Lerner, G. H. (1992). "Assisted storytelling: Deploying shared knowledge as a practical matter." Qualitative Sociology 15(3): 247‐271. Mescheder, B. and C. Sallach (2012). Wettbewerbsvorteile durch Wissen. Berlin/Heidelberg, Springer Gabler. North, K. (2005). Wissensorientierte Unternehmensführung ‐ Wertschöpfung durch Wissen. Wiesbaden, Gabler. Polanyi, M. (1967). The Tacit Dimension. London, Routledge & Kegan Paul PLC. Popper, K. (1987). Auf der Suche nach einer besseren Welt. München, Zürich, Piper Verlag. Probst, G. J. B., S. Raub, et al. (1999). Wissen managen ‐ wie Unternehmen ihre wertvollste Ressource optimal nutzen. Wiesbaden, Gabler Verlag. Schumann, C.‐A. and C. Tittmann (2010). Evolution analysis of knowledge potentials by pattern matrices. ECKM2010 ‐ European Conference on Knowledge Management, Famalicao, Portugal. Tittmann, C. and C.‐A. Schumann (2009). Potentials for Externalizing and Measuring of Tacit Knowledge within Knowledge Nodes in the context of Knowledge Networks. Cultural Implications of Knowledge Sharing, Management and Transfer. D. Harorimana. Hershey, IGI Global: 84‐107. Tittmann, C. and C.‐A. Schumann (2012). Multilayer Structure of Knowledge Nodes. Proceedings of the 13th European Conference on Knowledge Management. J. G. Cegarra. Cartagena: 1079‐1081. Vester, F. (1999). Denken, Lernen, Vergessen. München, Deutscher Taschenbuch Verlag. Wolf, P. and H. Hilse (2009). Wissen und Lernen. Praktische Organisationswissenschaft. Lehrbuch für Studium und Beruf. R. Wimmer, J. Meissner and P. Wolf. Heidelberg, Springer.
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Heuristic for Unscheduled Public Transport Navigation System José Sendra Salcedo and Osvaldo Cairó Battistuti Department of Computer Science, ITAM, México DF, México Río Hondo 1, 01080 Mexico DF, Mexico josess1990@gmail.com cairo@itam.mx Abstract: In Mexico City there are 12 subway lines and over 350 bus lines that do more than 12 million trips daily. All this lines are unscheduled and information about their stations location is limited. Only expert public transport users have enough knowledge to choose a fast rout to their destination using this transport system. The city transport system is a big maze for users; this situation makes inexpert users avoid public transport services, preferring taxis or their own car. In an environment so full of uncertainty, how can users choose the fastest route to their destinations? This paper tackles the unscheduled public transport fastest route search problem in Mexico City proposing as solution a Public Transport Navigation System (PTNS). Although solutions for minimum delay rout search problems have been widely studied, and many algorithms have been created, Mexico City public transport data is highly variable and uncertain making impossible to implement any of them, therefore the necessity of finding a solution not based on the public transports uncertain data but in their users knowledge. The system developed in this work finds fast routs to a destination in public transport by using a search algorithm with a knowledge based time dependent heuristic. The proposed heuristic aims to capture the knowledge of public transport expert users and combine it with data given by transport companies to calculate the fastest routes available. The heuristic considers the transport line´s speed, waiting time and the trail distance. These factors make the heuristic search algorithm produce routes that are faster than the ones considered by any public transport user. The heuristic is a time dependent function and therefore the estimated time of arrival (ETA) generated is precise. Test were made comparing trip times of persons with and without the PTNS installed in their mobile devices. The results showed that persons that followed the PTNS suggested rout had significantly shorter trip time than the ones who did not use the system. Keywords: heuristic, graph, experts knowledge, navigation system, knowledge extraction
1. Introduction Mexico City is one of the largest cities in Latin America; it has more than 20 million habitants in 7,850 km2. Having so many people in such a large extension of terrain generates great infrastructure and services challenges. One of them is public transport, how do you transport efficiently so many people from one point to another in one of the biggest cities of the world? Public transport systems in large cities are formed by many trolley, bus and subway lines. In the case of Mexico City, there are 12 subway lines and over 350 bus lines that do more than 12 million trips daily. Most of these lines are not scheduled and have no accurate map of their course. As a result, it is hard for users to choose efficient routes and calculate the time of arrival to their destinations. Only expert users that have lived long enough in the city to know many transport lines and their average speed and waiting‐time, can choose a fast rout to their destination predicting an accurate ETA. This situation makes inexpert users avoid public transport services, preferring taxis or using their own car. Consequently, there are more traffic jams and pollution, some of the major problems in the city. Currently there are no PTNS for Mexico City due to the lack of information about the schedules, speed and station location of the transports available. In this work a PTNS based on public transport expert users knowledge is proposed as a solution to the chaos in Mexico City public transport network. This system is able to find fast routs to destinations in public transport by using a search algorithm with a discrete time dependent heuristic. The PTNS counts with a database of all subway and rail stations, and also every station of five bus lines. The public transport lines in the database were chosen because they are the most used in the city and the better documented. At the present time if a person wants to go to a location using public transport, he will schedule the trip based on the remoteness of his destination, usual speed of the transports to be used, transfer time between different lines and the estimated waiting‐time. The estimation of the travel time is more accurate if the person
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José Sendra Salcedo and Osvaldo Cairó Battistuti uses the same route frequently. The PTNS copies this human behavior when finding the optimal route resuming it in four knowledge‐based factors:
Time that it takes to change of transport services, e.g. subway to bus.
Average speed of each transport.
The date and hour in which the trip will be made.
Waiting‐time for each transport line.
Given this factors the system estimates travel times while generating each possible route, always choosing the one with less estimated travel time and delivering at the end an optimal rout with the shortest ETA according to the criteria before stated. The goal of this paper is to make the reader aware of the manner in which exper users knowledge can be managed and combined with public tanspor industries information to find fas tranportroutes using a PTNS; In addition, this paper will provide the reader with an understanding of the PTNS rote search heuristic construction. In first place this work will describe the existing technologies and past works, describing numerical route search algorithms and solutions that use real time data applied in public transport navigation systems in many countries. Afterwards, a description of the PTNS proposed in this work will be made, deepening on the way the knowledge was extracted from public transport expert users, the methods followed to group it and how was it used to build the search heuristic. Afterwards, a description of how does the fastest route search algorithm works will be made. Next, the heuristic of the PTNS is validated showing results obtained of tests made in 1,200 public transport uses. Finally conclusions about the search algorithm heuristic are made and future work and applications are proposed.
2. Existing technologies The evolution of navigation applications has been accelerated in the last years, many new car navigation apps have flooded the market for mobile devices, this has brought a lot of attention into algorithms that can find optimal routes in a map given certain conditions and restrictions like traffic, trip time and users preferences. Given the data of the public transport network, the problem of finding the fastest path between two stations is generally modeled as a shortest path, minimum delay problem in a graph, where the nodes of the graph represent the transport stations, the edges the connections between the station and the weights the time between stations. There is a lot of literature about shortest path algorithms that has been studied widely; readers interested in theory about algebraic path problems are referred to (Dijkstra, 1959) (Bellman, 1958). Some of these algorithms model the weight of the edges as a time dependent variable (Orda et al., 1991). This kind of problems can be solved with linear programming modified Dijkstra Algorithm (Orda, 1990). Though Dijkstra can find the solution, the process is long, and static. Nonetheless, a heuristic approach can be made to accelerate the search (Hart et al., 1968), (Chuang et al., 2005). Tulp and Siklóssy decided to incorporate heuristics on a timed table network to speed up the fastest path search (Tulp et al., 1991). Now we will discuss how this problem has been concretely addressed to public transport and different solutions that have been proposed. Google has made lots of research in the area; they have developed navigation systems for walking, car and public transport. Nevertheless, Google maps public transport navigation systems is only available in a small amount of highly developed cities. This is because they calculate public transport routes using real time data that is uploaded by tans companies in a standardized format called General Transit Feed Specification (GTFS). The GTFS format requires many parameters; some of the most important are stops location, stop times, frequencies and transfers. This information is public; so many developers use it to create accurate routing algorithms (Ludwig, 2009).
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José Sendra Salcedo and Osvaldo Cairó Battistuti K. Nachtigal modeled railroads transport network in Germany as a discrete time dependent network and implemented an algorithm that searched using Artificial Intelligence (AI) heuristic techniques and a label correcting technique to find fastest paths between two stations, in his work he shows that AI search techniques are more efficient than algebraic search like Dijkstra for one to one search methods (Nachtigal, 1995). To speed up computer processing Jerald Jariyasunant proposed in his work pre‐generating all possible paths from station X to Y, and after that, looking for the optimal path in the result set instead of calculating the optimal paths each time the user requires it. This method has been already used in some train navigation systems in Germany using real time data (Jariyasunant, 2005). Although many of these solutions are widely proven and efficient, they cannot be applied to Mexico City public transport system. This is because the transport system lines are not properly regulated and there is no source of real time data of any of them. To this problem we must add that the transports speed is highly variable and arrival and departure times are not scheduled for any of them.
3. Public transport navigation system The PTNS designed in this work generates the fastest path between input stations A and B using knowledge of expert public transport users and displays the path on a mobile device. The PTNS is composed by four layers: mobile I/O layer, neighborhood and ETA layer, route search layer, and transport database layer. The architecture of the dataflow can be seen on Figure 1.
Figure 1: PTNS architecture and data flow When a user wants to get the fastest route from station A to B, he access to the Mobile I/O layer, in this layer the user’s desired origin and destination is retrieved given a landmark or a direction specified using Google maps API. The user also provides the time in which he desires to do the trip. The mobile application sends this information to the Neighborhood and ETA Layer. In that layer a web service uses the provided information to locate in the database all stations that are 500mts round of the destination coordinates. In case there are no stations that close, the system looks for the nearest station, these will be the destiny stations DS. The same process is made with the origin coordinates, generating the origin stations OS. After this, the Route search layer will look for an optimal route from each station in OS to ach in DS; each of these routes is found by finding the path of minimum delay in a graph where the nodes represent the transport stations, the edges the connections between the station and the weights the distance between
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José Sendra Salcedo and Osvaldo Cairó Battistuti stations. The system will search for an optimal route to the destination in this graph, using an heuristic driven search, the heuristic used is described in point 3.2. After finding the optimal route from each station in OS to ach in DS, the web service provides the Mobile I/O layer the three fastest routes found, including the stations that form it and the ETA. In this layer, the mobile device used by the user displays these routes in a list so the user chooses the one that best fits him; the chosen route is displayed in a map. A step‐by‐step guide and ETA are also displayed. The database used by the system contains 320 public transport stations, the estimated waiting time for each transport line, the estimated speed of each transport and the transfer time for stations where transfer is possible. Each station in the database has its coordinates, the name of the service and line it belongs to.
3.1 Expert users and knowledge extraction A public transport expert user is defined as a person with the following characteristics:
Uses a public transport at least five times in a week.
Has no car.
Used the public transport for at least four years.
Is familiar with all public transport lines of Mexico City.
Knows the average speed of each transport line.
Knows the average waiting time for each transport line.
Knows many public transport station locations.
Knows average transfer time between all transport lines.
Due to their experience, expert users knowledge is a key feature in creating a heuristic that can be used in a search algorithm that imitates their process of fast public transport routes calculation. The knowledge extracted from expert users for the creation of the PTNS and expert users heuristic was the following:
Stations Location
Line waiting time
Line speed
Transfer time
To extract this knowledge from expert users, different surveys were made for each transport line, 100 expert users were surveyed for each regulated transport line (subway, trans, train, regulated bus lines) and 400 were surveyed about chaotic bus lines. A total of 1,200 users were surveyed. The stations location extraction process is necessary as only subway and few bus lines station locations are available. Buses in México are poorly regulated by the government and therefore only expert users know their stations location. To extract the bus station locations from expert users, in all of the 1,200 surveys made they were asked for at least 5 chaotic bus lines origin and destination terminals location. With this information a research was made in each terminal to determine the routes of all bus lines in the site and store each one of them in the PTNS database. The line waiting time knowledge extraction was made by asking expert users in each line survey for the schedule in which they commonly use the transport line and how much time do they wait. The waiting times were grouped in short, standard, long, being long the waiting times in the below 33% percentile cut point of the line waiting time distribution, standard those between the 33% and 66% cut points, long those above the 66% percentile cut point. Each group was assigned a value; this value corresponded to the average of the waiting times in the group. Once the waiting time was grouped, clusters were made generating time intervals with an assigned waiting time category; an example of these intervals for the subway can be seen on table 1.
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José Sendra Salcedo and Osvaldo Cairó Battistuti The line speed knowledge extraction was made by asking expert users in each line survey the schedule in which they commonly use the transport line and how fast did they consider that line compared with all the other transports available. The answers available were very fast, fast, normal, slow and very slow. The 100 survey answers were analyzed and clustered given the users trip schedules. As a result time intervals with transports speed categories were made. This results were stored in the PTNS database. Table 1: Subway waiting time knowledge extracted Waiting time table Waiting Time Long(14.6 min)
Days Mon‐Fri 19:00‐23:30
Standard(7.3 min)
Sat‐Sun 6:00‐23:30 Mon‐Fri 6:00‐11:00 Mon‐Fri 11:00‐19:00
Short(5.2min)
To calculate a numeric value for each speed category the speed of the transport lines was physically recorded in the time intervals generated by the clustering process. 20 measures were made for each time interval and the average of those measures was assigned to the time interval speed category. Finally to extract the transfer time knowledge from expert users all the 1200 users were asked in their line survey how much time did they do to make a transfer from the line of which they were surveyed to a subway service, bus service, Metrobus service, train service and trolley service. Given all the answers the average transfer time form each line to each different service was calculated and stored in the PTNS database. Once all knowledge of Expert users was extracted the expert users heuristic was created.
3.2 PTNS expert users heuristic The Expert Users Heuristic (EUH) imitates the behavior of expert Mexico City public transport users; this heuristic is built by the following components.
Distance to destination and trail
Transfer time
Line waiting time
Line speed
These components are explained in 3.2.1 – 3.2.4. 3.2.1 Distance to destination and trail factor The Distance to Destination and Trail Factor (DTF) helps the system search algorithm look for the shortest route from the origin to destination, this factor is measured using the A* search algorithm distance plus cost heuristic. This value consist on calculating for each path from Origin station (O) to station X the length of the trail plus the distance from X to the destination (D) (1)
where n is the index of the last station in the route and
station i of the route
= Distance between stations (2)
,calculated using the harvesine formula
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José Sendra Salcedo and Osvaldo Cairó Battistuti The DOFTS is included in the PTNS to resemble the expert user knowledge of shortest routes, though the system will have the advantage of being able to quickly compute the shortest path between any two stations. Nevertheless, the shortest path is not the fastest one. Therefore more factors taken into account by expert users must be analyzed. 3.2.2 Line waiting time factor The Line Waiting Time Factor (LWTF) is used to model the time a person has to wait for a transport in a specific line to arrive. The time between transports in Mexico City is highly variable depending on time and day and type of transport. Expert users know the estimated mean waiting time for each transport and therefore they know which are better to use depending on time and day. To measure the mean waiting time for each transport line, a sample of times between transports was made for each transport line; each sample consisted of the hour, day, transport line, and time between transports in minutes. After carefully analyzing the data, it was clustered in three main categories for each transport line: high, low and standard waiting time. This factor is also used to check if transports are available at a certain hour, if a user wants to travel at 1:00 AM to certain destination many transports will not be available, to model this the waiting time for the unavailable transports in near to infinite, this will force the fastest path search algorithm to avoid paths that include unavailable transports. In Metrobus transport for example the clustered time table obtained for waiting time can be seen on Table 2. Table 2: Clustered waiting time table Waiting time table Waiting Time Long
Days Mon‐Fri 19:00‐23:30 Sat‐Sun 6:00‐23:30
Standard Short
Mon‐Fri 6:00‐11:00 Mon‐Fri 11:00‐19:00
Not Available
23:30‐6:00
3.2.3 Transfer factor Due to the huge extent of Mexico´s City transport system it is probable that users cannot arrive to their destinations using only one mean of transport, therefore they frequently need to make multiple transfers. Expert users know that a fast route generally implies doing a small amount of transfers because stations are generally distant and doing a transfer takes a lot of time. The Transfer Factor (TF) imitates this expert user’s behavior by calculating the time it takes to perform a transfer from one service to another. This factor is calculated by measuring the average time a person takes to walk from one line station (A), to another (B) and adding the LWTF of the new line: (3) The walking time was calculated by measuring the time in the stations where transfer is possible and stored into the web service database. This factor is used when the search algorithm is generating possible routes, when it checks if it is good to move from station x to station y, in case station y has a different line or service than station x, the appropriate TF value would be retrieved from the database.
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José Sendra Salcedo and Osvaldo Cairó Battistuti 3.2.4 Line speed factor When an expert user chooses a transport, one of the main aspects taken into account is its speed, if he wants to arrive to his destination in the shortest time he will use the fastest transports available. Expert users calculate the transports speed based on their experience, knowing that at certain hours, in the case of buses, traffic decreases speed drastically. In the case of subways, during midnight the service is slow. The transport line speed (LSF) can imitate this expert users behavior by estimating the speed according to the time and day. This data was calculated measuring the time different transport lines take to do round trips, and given the length of the route calculate the speed. Following the same procedure of LWTF we can categorize the speeds of each transport depending on time and day.
3.3 The fastest route search method The EUH is used in a search algorithm that expands nodes (stations) of the transport network and compares them to see which one is the best option as a next stop in the route that is being calculated. Given the origin station O, The EUH is calculated for all available sub paths from O to X, where X is a station connected directly to O. The EUH is calculated as follows:
(4)
. = line speed of the
.
Line waiting time factor for the transpot line of the origin station O. Once the EUH is calculated for each station X the algorithm stores the pairs of X and their EUH score in the set with minimum EUH score obtaining the tuple and Ω, afterwards the algorithm looks for the X , where is the station X with minimum EUH and is the EUH score. The station is trespassed from set Ω to set Ƈ and it is expanded, calculating the EUH value for every station Y directly connected to station . The process is as follows:
With: Y = station directly connected to station = station with minimum EUH score in set = Route from station to station Y D = destination station
=
EUH score
The results are stored in Ω, the process is repeated until the destination station S is reached or there are no more nodes in Ω to expand, in case there are no nodes this means there is no path form station O to station D. When the station D is reached a backtracking algorithm will rebuild the fastest route using the information of pairs stored in Ƈ.
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4. PTNS test and results The PTNS was tested on 200 persons of ages between 19 and 35. From the 200 persons 140 were expert public transport users and 60 were users unfamiliar with Mexico City public transport system. The group was divided in two subgroups, A and B. Group A was conformed by 100 persons that had the PTNS installed in their mobile device, of those, 30 were inexperienced public transport users. Group B was formed by 100 persons that didn´t have the PTNS installed on their cell phones, of those, 30 were inexperienced public transport users. The tests were conducted by applying a small survey to two persons to identify their transportation habits and classify them as expert public transport users or inexperienced users and see if at least one of them had a smartphone with the requirements for the PTNS. Afterwards they one of them was assigned to group A, and the other to group B, the PTNS was installed in the phone of the user of group A. They were both assigned a destination, each one departed with a ten minute time difference. Once they arrived to their destination they reported their time of arrival. If the user was of group B he also indicated the ETA predicted by the PTNS. This test was repeated on different pairs of people until it had been tested on 200 persons. The destination and origin in each test case was different and every transport service was used in the tests. The results obtained from the tests were that persons of group A arrived earlier to their destination 90% of the times. The members of group A had a trip time 20% shorter than those of group B. In the test pairs where both members were expert users, members of group A arrived earlier 95% of the times. These results clearly show how the EUH used by the PTNS generates routes that are faster than the ones calculated by any user manually in almost all the cases. In the test pairs where the member of group A was an inexpert public transport user and the member of group B was an expert, the first ones arrived earlier 62% of the times, in average their trip time was 10% shorter. The explanation of this low advantage compared with the first results is because inexpert users are not familiar with Mexico City public transport system, therefore they required more time to find the transport line stations suggested by the PTNS and it is easier they commit mistakes. Nevertheless the fact that inexpert users arrived 62 out of 100 times earlier than an expert user shows how the EUH manages and unifies the knowledge of expert users in such a way that the routes it generates are so superior than those generated by experts users that even an inexpert user can arrive earlier to his destination than someone that has years of experience using public transport. The ETA generated by the PTNS, when compared with the measured arrival time, showed in average a precision of +/‐10 minutes. The trips in which buses were used had a precision of +/‐ 15 minutes while precision in trips where no buses were used was of +/‐ 6 minutes. This is because bus waiting time and speed greatly vary depending on driver’s skills and traffic. In tests made by inexpert users that had the PTNS the ETA had a precision of ‐25 min. This is because the system doesn’t take into account the expertise of the users and assumes they know how to use the public transport system and will follow efficiently the line proposed when calculating the ETA. The ETA generated by the PTNS is close enough to the measured arrival time to consider it a good estimator; with this data users can plan their trips more effectively.
5. Conclusions The present work shows how the extraction of the knowledge public transport expert users possess its analysis and use in a heuristic search algorithm can help create a PTNS and avoid the transport company’s information gap by the estimation of this data. The PTNS presented in this work reduces users travel time and helps them schedule their journeys. This navigation system affronts the chaos and uncertainty in a complex transport dynamic system by grouping the knowledge of its users, which individually might be insufficient but, when grouped, can solve the system efficiently. This solution is presented for Mexico City but as it depends on users experience and not on companies information it can be applied in other cities with similar conditions, in case the transports are better regulated or have real time data available, it can be included in the PTNS.
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José Sendra Salcedo and Osvaldo Cairó Battistuti Future work includes further research of poorly regulated unscheduled bus transport characteristics that can help model better that transport’s speed and waiting times so these characteristics can be added to the heuristic proposed.
Acknowledgements This work has been founded by Asociación Mexicana de Cultura A.C.
References Bellman R. (1958) “On a routing problems”, Quarterly of Applied Mathematics, Vol. 16, No 1, pp 87–90. Chuang T., and Kung J. (2005) “The fuzzy shortest path length and the corresponding shortest path in a network”, Computers and Operations Research, Vol. 32, No. 6, pp 1409‐1428. Dijkstra E. (1959) “A note on two problems in connexion with graphs”, Numerische Mathematik, Vol. 1, No. 1, pp 269–271. Forstall R., Greene R., and Pick J. (2009) “Which are the largest? Why lists of major urban areas vary so greatly”, Tijdschrift voor economische en sociale geografie, Vol. 100, No. 3, pp 277‐297. Hart P., Nilsson N., and Raphael B. (1968) “A formal basis for the heuristic determination of minimum cost paths”, IEEE Transactions on Systems Science and Cybernetics, Vol. 4, No. 2, pp 100–107. Jariyasunant J., Mai E., and Sengupta R. (2010) “Algorithm for finding optimal paths in a public transit network with real‐ time data”, Transportation Research Record: Journal of the Transportation Research Board, Vol. 2256, No. 1, pp 34‐ 42. Nachtigal K. (1995) “Time depending shortest‐path problems with applications to railway networks”, European Journal of Operations Research, Vol. 83, pp 154–166. Orda A., and Rom R. (1991) “Minimum weight paths in time‐dependent networks”, Networks, Vol. 21, pp 295‐319. Orda A.,and Rom R. (1990) “Shortest‐path and minimum‐delay algorithms in networks with time‐dependent edge‐length”, Journal of the ACM, Vol. 37, No. 3, pp 607‐625 Tulp E., and Siklóssy, L. (1991) "Searching time‐table networks", Artificial Intelligence for Engineering, Design, Analysis and Manufacturing, Vol. 5, No. 3, pp 189‐198
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On Some Knowledge Issues in Sciences and Society Dan Serbanescu Safety and Risk Expert, Bucharest, Romania dan.serbanescu1953@yahoo.com Abstract: The paper presents some aspects related to a new proposed approach for the evaluation of the knowledge creation and knowledge management issues. The approach considers that, from knowledge creation and knowledge management perspective, sciences pass in a repetitive manner through a set of phases. These phases have dominant characteristics dominated by some type of patterns, as previously described in the literature (Kuhn, 1962). However it is considered that the switch itself and the next direction in sciences, due to this switch from one phase to another, have similarities with other areas of knowledge creation and knowledge management in the society, as for instance in the use of the concepts of belief. The approach uses a theoretical basis and provides a set of practical guiding rules to look for the causes of difficulties in searches for solutions on various issues, while using scientific approaches and to use a set of paradigms and framework guided searches for the definition of the main features of the type of solutions to be searched. The approach considers the criteria to be used in order to define the phase in which a certain issue reflected by sciences is at a certain moment, the main contradictions and problems in solving it, their most probable causes and eventually to indicate a systematic guiding process to look for a solution. This approach was applied in support to the searches for solutions on the issues of risks for complex systems evaluations – qualitative and quantitative (Serbanescu, 1993, 2008 2011) in various fields, i.e. in modelling new nuclear energy cases, renewable energy (in general and photovoltaic and hydrogen storage in particular), security of energy supply (local and regional), decision making in energy systems, use of risk analyses in the design of complex systems etc. During those case studies the search for solutions was also compared, using the proposed methodology and the results obtained, with some other approaches applicable to the studied cases. The results confirmed convergence of the results and the fact that for such issues of the risk evaluations for diverse complex systems the approach proposed lead to stable, repeatable and auditable solutions, which were in accordance with the solutions obtained by using other guidance. However in addition to other methods the advantage of this approach proved to be its systematic and predictable character, as well as the possibility to use it in difficult new situations of evaluating issues, which have high degree of uncertainties of all types and/or high complexity. It is also considered that the continuation of evaluation of its applicability to other areas, aside with extended comparisons with other methods available now in the knowledge management area, as for instance (Snowden, 1996), could consolidate the results obtained so far and bring new potential useful applications. On the other side, if the applicability and usefulness will be confirmed for other applications, this could be an important possible switch at the theoretical level in the search for methodologies and strategies to be adopted by using various sciences to solve specific problems Keywords: science, belief, risk, paradigm, myth
1. Introduction The approach presented in the paper on the evaluation of the knowledge creation and knowledge management considers that, from knowledge creation and knowledge management perspective, sciences pass in a repetitive manner through a set of phases. During each phase a set of relationships are defined between scientific issues for real objects having as one of their purpose to define the relations and rules connecting relationships between known facts about the real objects. Scientific method is using specific tools to derive and manage knowledge. As it was shown by (Kuhn, 1961) the important moments in science are driven by dominant characteristics dominated by some type of patterns, called paradigms. This view on the important moments in sciences is more focused on the driving force of the changes. However as this paper is trying to show and as it was previously published for some specific scientific cases in complex systems and risk analyses (Serbanescu, 2007), it could be of equal pragmatic importance a more detailed evaluation of the guiding rules to search for solutions in various crucial and controversial moments in a science. The paper is proposing to consider that the switch itself from one group of paradigms to another, as well as the next direction to be followed by using this switch, need more investigation and might have a high impact on diverse approaches to the issue: epistemology, knowledge management and specific tools used in various sciences. It is proposed to consider from this perspective, in line with a series of researches as for instance (Snowden, 1996) that these switches have a connection – at least a certain degree of analogy if not a deep unrecognized siphoning effect- with other areas of knowledge creation and knowledge management in the society, as for instance a “Myth” (the general cultural pre concepts deep embedded in the society) or “Art”. The choice of three branches – three windows to view the same reality objects – is assuming that the search of solutions in those spaces is driven by some strong forces, deep embedded and changing content from one group to
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Dan Serbanescu another. “Belief” is one of them. The content of “Belief” is different in science or myth and it has another understanding for the artistic masterpieces of humankind. Belief is intensively used for all of them to derive and manage humankind knowledge.
2. Method 2.1 Assumptions The topic of study was how to formalize, if possible, the actions to be performed so that to have more systematic guidance in solving crisis moments in various topics studied by science. The main proposed goal aims to show how to consider some external interferences and general approaches specific for complex situations existent in science in the action to find the best generic approach to search for solutions. This paper presents results for a specific domain of complex systems, i.e. the study of complex systems and related to them issue of risk objective function. The studies were referenced in the papers mentioned (Serbanescu, 19932011). The main assumptions considered in order to develop a method on how to develop solutions for systematic search of solutions for conflicting and/or lacking solutions in various scientific cases are as follows:
The possible philosophical approaches to reality, science, truth or beauty are not considered, in the method, For the purpose of the research, i.e. to try to identify mechanisms for guiding forces of dramatic changes in science and their analogy with other approaches it was considered that theories for specific topics in science do not influence the main features of the method.
It is considered – as in Figure 1 – that there are two important triads in the definition of the objects for this research:
One is defined by Science, Myth and Art as a group of views on reality that cover diverse facets of the reality (including virtual one).
The second is defined by the tools possible to use to study of such sets: Absolute (to some extent analogues of deterministic views in many sciences) evaluations, Probabilistic (as a set of tools to address uncertainties in knowing the objects and their relationships) and CAS (Complex Systems, in special some of a specific type- complex systems regenerating themselves – see examples from (Serbanescu, 2005-2011). This assumption is related to the tools used to analyze data for the specific cases studied by the author and it is reflected in the results, too. Details on those connections are largely presented in (Serbanescu, 2005-2011).
The application of those two triads over the set of relationships (“f” type in Figure 1) generates structures - spaces of solutions and interferences between them, as shown in Figure 1. These relationships generate for each type of science a specific algebraic structure (if considering also the rules for their combination and inferences, no matter in which philosophical approach). This illustrates the fact that diverse views are possible for the same elements of those structures, but their content is totally different from science to science or from one triadic element to another- for example the study of solar strength has different outcomes for the three triadic groups. However their common feature is that the results are generated by deep believes and they do interfere, even if it is not recognized. Continuing the example and its relevance to the paper one can notice that in moments of switching from one theory of light to another deep mythological (in the understanding mentioned before in this paper) or artistic believes played an important role in the adopted theories in crisis moments.
The expected output of the proposed method is a set of guiding rules and procedures to evaluate the causes of a problem in a given scientific structure and to direct the type of expected results that could solve the existing “deadlock”. The details on the connection between this assumption and results are presented for the specific cases studied by the author in (Serbanescu, 2005-2011).
The interfaces and algebraic structures are assumed not only in two dimensional representation as in Figure 1, but also as a three dimensional structure – on various degrees and levels (as shown in Figure 2): level 1 for basic rules-in any format (theories, experiments guided in general by some paradigms); level 2 for basic methodological searches to solve the detailed reality cases by using the level 1 structures and level 3 for cases and verifications, management of acquired knowledge and finally feedback to the restart of the whole process as a result of the need for a “catastrophic hypothesis, theory etc ) the only one apparently able to move the process of management of acquired knowledge in a non conflicting manner.
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Figure 1: Representation of the triadic approach and two dimensional forms of the structures generated by knowledge process
2.2 Main features of the method Figures 2 and 3 represent the main features of the proposed method. The method followed and the tools used to analyze data are presented below and detailed for the specific case of searching for risk objective functions of complex systems in (Serbanescu, 2006). The papers (Serbanescu, 2005 - 2011) present examples of results illustrating the method and approach presented in this paper. In Figure 2 the phases of science history are represented. The method considers that there are three phases at three levels. Each phase is actually representing an algebraic structure (depending on the type of science we consider, i.e. the set of relationships it evaluates, the rules-theories, experiments, hypothesis etc). There are several types of connectors: between phases at the same level, feedback at the same level, a jump from level to level of high or low importance. They are represented in Figure 3. These transfer algebraic structures assure the transformation of the input on the knowledge existent at a certain phase into a knowledge nodal moment (I) (phase) â&#x20AC;&#x201C; K at another phase and all this process has a dynamic, repetitive character.
Figure 2: Representation of the knowledge process in three dimensional forms
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Figure 3: Representation of the transition matrix from one state to another for the knowledge process in a three dimensional form The phases are defined based on the characteristics listed in Tables 1 and 2. Table 2 identifies the main features of the interface of governing principle, paradoxes and believes for each phase. Table 1: Main characteristic of the phases in history of science Nr 1 2
Phase Define the Need Clarify the Difference
3
Develop main tools
4
Check the stability of the theories
5
7
Identify main believes preventing Evolution Refine and increase Usefulness Attempt to merge tools
8
Manage the built up
9
Attempt to solve the unsolved yet issues and expand the object
6
Main characteristic An unique Source Basic science features as a dual Approach Creation of new in science governed by a triple way \approach Durability of a theory shown as robustness to Paradoxes Each historical stage is an engine powered by intuition and believes For any theory has to be useful God is not playing dice- Absolute and relative knowledge Hierarchy and science theories structures involve the need for management Irrepressible need to reach perfection as a cause for restarting the whole cycle
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Dan Serbanescu Table 2: Main characteristic of the phases in science history
Pha se
Main characteristics of the phases in science history
Main governing principle of a given phase
Main features of the interface of governing principle, paradoxes and believes
1 Define the Need
An unique Source
Clarify the Difference
Basic science features as a dual Approach
Develop Main tools
Creation of new in science governed by a triple way \approach
2
3
4
Check the stability of the theories
5 Identify main believes preventing historical move 6
Refine and increase Usefulness
7 Attempt to merge Tools 8 Manage the built up
9 Attempt to solve the unsolved yet issues and expand the object
An isomorphism exists between the mythical thinking in general and the scientific thinking acting to solve Paradoxes Instinctive impulse exists to bring the issues to a duality in defining the truth, applicable criteria and methods for its evaluation and value of results Creating the new in science has a triple facets in order to try to overpass the dual approach of the basics and look for the third option, starting from paradoxes
Durability of a theory shown as robustness to Paradoxes Each historical stage is an engine powered by intuition and believes For any theory has to be Useful God is not playing diceAbsolute and relative knowledge Hierarchy and science theories structures involve the need for Management Irrepressible need to reach perfection as a cause for restarting the whole cycle
Continue quest from phase 3 and check the science robustness on the embedded in it paradoxes
Identify the believes behind the paradoxes identified in previous phases
Clarify and refine the value of the acquired set of knowledge Support the need for creating the new with merged tools in the attempt to make a synthesis of those ones appearing as fundamentally opposed Manage the gained corpus of knowledge in consolidation of the tools and results obtained so far.
Attempt to solve the unsolved yet issues and expand the object
By applying the rules for the phase definition and how the main features of the interface of governing principle, paradoxes and believes for each phase interact for transformations of knowledge represented in Figures 2 and 3. As science passes through similar phases not only once and this happens for all phases, the structure generates a space that can be represented as in Figure 4 (a particular case of the results for knowledge structure in a â&#x20AC;&#x153;scientific drivenâ&#x20AC;? type of knowledge system for the evaluation of Complex Systems) (Serbanescu, 2008). It is important to note that for the cases presented in the next paragraph related to evaluation of risk in Complex Systems the knowledge space can be represented as a Platonic Polyhedra because the form is governed by the number and placement of faces of passes from one phase to another.
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Figure 4: Representation of the results for knowledge structure in a â&#x20AC;&#x153;scientific drivenâ&#x20AC;? type of knowledge system As another generic result of the phase triadic approach in a three dimensional view the research identified the types of knowledge systems (Figure5). Driven by the distribution of facets and if the polyhedra are filled in or hollow, various types of cultures and civilizations (Antique Greece, Egypt, India, modern western etc) and belief driven phase changes depending on what type of relationships exist between the three main components of the evaluated triad (Science, Art or Myth). As a particularity of this observation one can notice and compare the driving forces of various societies in science depending on what kind of cultural system was specific for it. These aspects can be evaluated in more detail in future researches.
Figure 5: Main characteristic of the phases in science history
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3. Some results for specific cases For the specific case of evaluation performed for Complex Systems (Serbanescu, ,,,), in which there was a need to be able to perform predictions of the tools that are applicable in a systematic and predictable manner and/or for limit situations in crisis management for evaluations of the risk as the most important objective function of the Complex Systems. During those case studies the search for solutions was also compared, using the proposed methodology and the results obtained, with some other approaches applicable to the studied cases. The results confirmed convergence of the results and the fact that for such issues of the risk evaluations for diverse complex systems the approach proposed lead to stable, repeatable and auditable solutions, which were in accordance with the solutions obtained by using other guidance. However in addition to other methods the advantage of this approach proved to be its methodologies and strategies to be adopted by using various sciences to solve specific problems The method was applied in support to the searches for solutions on the issues of risks for complex systems evaluations â&#x20AC;&#x201C; qualitative and quantitative (Serbanescu, 1991, 2007 - 2011) in various fields, i.e. in modelling new nuclear energy cases, renewable energy (in general and photovoltaic and hydrogen storage in particular), security of energy supply (local and regional), decision making in energy systems, use of risk analyses in the design of complex systems etc For some specific cultural approaches and/or limitations due to misuse and of the risk analysis tools, specific biases were considered. Some could be considered as myths on science (Mc Comas,1996) MyS1 MyS2 MyS3 MyS4 MyS5 MyS6 MyS7 MyS8 MyS9 MyS10
Hypotheses become theories which become laws Hypothesis is an educated guess A general and universal scientific method exists Evidence accumulated carefully will result in sure knowledge Science and its methods provide absolute proof Science is procedural more than creative Science and methods can answer all questions Scientists are particularly objective Experiments are the principle route to scientific knowledge All work in science is reviewed to keep the process honest
Others as myths on risk analyses(Hansson, 2000) MyR1 â&#x20AC;&#x153;Riskâ&#x20AC;? must have a single, well-defined meaning. MyR2 The severity of risks should be judged according to probability weighted averages of the severity of their outcomes. MyR3 Decisions on risk should be made by weighing total risks against total benefits. MyR4 Decisions on risk should be taken by experts rather than by laymen. MyR5 Risk-reducing measures in all different sectors of society should be decided according to the same standards. MyR6 Risk assessments should be based only on well-established scientific facts. MyR7 If there is a serious risk, then scientists will find it if they look for it. In this cases a systematic review of how to find out the appropriate type of tool for a given task of risk analysis the steps from the previous paragraph were applied. As an illustration Table 3 presents a sample of solving paradox issues for risk analyses in Complex Apoietic Systems (CAS)( Serbanescu, 2008)
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Dan Serbanescu Table 3: A sample of solving paradox issues for risk analyses in Complex Apoietic Systems PARADOXES
1
2
3
4
5
6
Main paradox
Main paradox short
The evaluation of CAS risk leads to the need for a unique theory based on the assumption that there is a unique source to describe. However the unification attempts lead to the reality that there are different possible approaches for the same CAS The models and methods consider both the deterministic and the probabilistic approaches as describing correctly CAS. However they exclude each other in some areas as for instance the need for confidence possible to be obtained from deterministic reasoning The combination of deterministic and probabilistic approaches implies allocation of subjective weights to a given judgment on a random variable, which generates values on qualitative statements Sensitivity and uncertainty analyses - model benchmarking checks the stability of the model to paradoxes. However the testing generates an acceptance of the initial paradigms of the model and methods by any cross check performed in order to see how stable Stages follow in principle one after another but it is not mandatory and assuming a s8mooth transition from one ph9ase to another creates the paradox of unexplainable breaks appearing every time a fundamental change happens Value function depends on actors, cases etc and defining a value functions creates a limitation not intended for CAS by the initial goal to get a global set
CAS unique source needs unitary theory, which leads to multiple diverse risk facets to model
Deterministic vs. probabilistic makes Impossible that only one of them will cover CAS risk evaluation, which leads to a situation that we have to accept theory based on "Contraria non contradictoria sed complementa sunt" N.Bohr
CAS risk in deterministic and probabilistic approaches need "Meta - Logic" to combine them, what leads to the fact that the third facet on the issue cannot be from inside the theory and appeal to a new level of abstraction is required Detailed list of assumptions lead to paradigms, which help to build models in a systematic manner. This action creates in its turn systematic errors and requires checks of the model robustness. Those verifications lead in such approaches to an unexpected reverse reaction, i.e. it identifies weak points of the whole construction.
Unknown/Unformulated assumptions leading to paradoxes General science Risk myths myths MyR1 MyS7 Science “Risk” must have and methods a single, wellcan answer all defined meaning. questions
MyR6 Risk assessments should be based only on wellestablished scientific facts.
MyR2 The severity of risks should be judged according to probability weighted averages of the severity of their outcomes. MyRx Verification and validation (V&V) by benchmarking and feedback from operation are sufficient evidence to prove validity of a model (Gödel’s theorem)
MyS5 Science and its methods provide absolute proof
MyS3 A general and universal scientific method exists
MyS4 Evidence accumulated carefully will result in sure knowledge
Set of CAS risk paradigms leads to an Isomorphism from for the corresponding set of believes. The decision to know the unformulated assumptions leads to statements assumed true
MyR7 If there is a serious risk, then scientists will find it if they look for it.
MyS2 Hypothesis is an educated guess AND MyS9 Experiments are the principle route to scientific knowledge
Define value function for clarification of CAS risk model, what leads to limitations not intended for CAS while setting up the initial goal
MyR 3 Decisions on risk should be made by weighing total risks against total benefits.
MyS8 Scientists are particularly objective
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Dan Serbanescu PARADOXES Main paradox 7
8
9
of values Merging deterministic and probabilistic models lead to the intent to have certainty on uncertain by default phenomena (probabilistic ones) and vice versa for deterministic ones which is in conflict with the intent of merging to eliminate contradictions Management of risk model leads to need to freeze the model and/or change it in controlled steps and procedural manner, which could actually generate the validation by procedures of all the systematic assumptions and errors. The restart of the process is done in the same steps after the accumulation of the paradoxes making impossible use of the theory assuming hat evolution could give a solution, while it is clear that a revolution could give it
Main paradox short
Unknown/Unformulated assumptions leading to paradoxes General science Risk myths myths
MyR5 Risk-reducing measures in all different sectors of society should be decided according to the same standards.
MyS10 All work in science is reviewed to keep the process honest
Try to solve theory merging issues by "theory - managerial issues" and management of theory This Induces more uncertainties and leads to worsening of the initial "uncontrolled " phase
MyR4 Decisions on risk should be taken by experts rather than by laymen.
MyS 6 Science is procedural more than creative
The tend to perfection lead to a set of paradoxes making unusable the results New reviews and cycles are needed
MyR7 If there is a serious risk, then scientists will find it if they look for it.
MyS1 Hypotheses become theories which become laws
Merge two approaches deterministic-probabilistic to get a better tool. The result is a tool with difficult to evaluate degree of assuring the certainty level expected by users
4. In conclusion The proposed method was used for some specific cases (as for instance Complex Systems analysis) and it proved to be helpful and confirmed theoretical background in this case. In the referenced papers (Serbanescu, 2005-2011) there are more details on how the general conclusions presented in this paper are connected to the assumptions The proposed method was used for specific cases of Complex Systems in defining objective functions as risk. The results illustrated that the triadic approach for the objects to be known and the methods used to achieve this goal, description of the science/knowledge phases and their governing principles and expected solutions, as well as the representation of the knowledge process by using this phase approach and the principles governing the search for solutions are all leading to specific solutions for the case studied. It was also shown for the specific cases studied in (Serbanescu, 2005-2011) that the results represented in specific three dimensional view are of real pragmatic value and bare interesting general conclusions on their implications. However further tests for other areas/topics are under way, including reactor physics, as a new approach for next generation nuclear reactors. A thorough investigation and comparison with other approaches in knowledge management are envisaged, too.
References Kuhn, Thomas (1962). The Structure of Scientific Revolutions. Chicago: University of Chicago Press Mc Comas, William (1996), Ten Myths of science: Reexamining what we know, vol 96, School Science & Mathematics, 0101-1996, pp10 Serbanescu Dan; Vetere Arellano Ana Lisa; Colli Alessandra (2008) On Some Aspects Related to the Use of Integrated Risk Analyses for the Decision Making Process and Non-nuclear Applications T2-G Symposium: On some aspects of performing probabilistic risk assessment for regional renewable energy systems in Overcoming Risks Inherent to Renewable Energy Technologies and Systems, Baltimore, SRA conference, USA December 6-9, 2009
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Dan Serbanescu Serbanescu , D(2008), Science and mythology SRA conference Boston 2008 Serbanescu, D (2011) SRA Conference 2009 - Risk Analysis:The Evolution of a Science -Symposium: Overcoming Risks Inherent to Renewable Energy Technologies and Systems On some aspects of performing probabilistic risk assessment for regional renewable energy systems (T2-G.2) Baltimore, USA, Dec 6, 2011 Serbanescu, D, A. Colli, B.J.M. Ale (2008) PRA-Type Study Adapted to the Multi-crystalline Silicon Photovoltaic Cells Manufacture Process, in “Safety, Reliability and Risk Analysis: Theory, Methods and Applications”, Martorell et al. (Eds.), Taylor & Francis Group, London, ISBN 978-0-415-48513- 5, for ESREL 2008 & 17th SRA Europe Annual Conference, 22-25 September 2008, Valencia, Spain., 2009 Serbanescu, D (2006), SSR 2006, Some considerations on the risk analyses for complex systems Serbanescu, D (2005), Integrated Risk Assessment, ICRESH2005, Mumbay, India Serbanescu, D (1993) A new approach in nuclear risk theory in The use of PSA in the regulatory process" IAEA Vienna, 2629 April 1993 Snowden, David J. (1996). "Story telling: an old skill in a new context". Business Information Review 16 (4): 30–37 Sven Ove Hansson (2000) Myths on Risk - Talk at the conference Stockholm thirty years on Progress achieved and challenges ahead in international environmental co-operation Swedish Ministry of the Environment, June 17-18, Royal Institute of Technology, Stockholm
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Using the SECI Model to Analyze Knowledge Creation in Students’ Software Teams Mzwandile Shongwe Department of Information Studies, University of Zululand, Kwadlangezwa, South Africa shongwem@unizulu.ac.za Abstract: Knowledge creation is a process in which individuals, groups and organizations are engaged in activities that generate new ideas. Organizations must constantly create knowledge to be competitive and stay viable in today’s markets. The knowledge creation phenomenon has been studied extensively in many fields (knowledge Management, Computing, Information Sciences, and many others). In most of these fields knowledge creation studies are conducted to address issues affecting big organizations using professionals as subjects of study, largely ignoring educational settings such as universities and students. For example we know a lot about how professional teams create knowledge but we know less about how students create knowledge. The study aimed to address this short coming. A case study of six students’ knowledge creation teams (software development teams) is presented. The case study reports on how the teams create knowledge (a software product).The theory of organizational knowledge creation by Nonaka and Takeuchi was used to analyze knowledge creation practices in these teams. This theory has been used extensively to analyze knowledge creation practices in professional teams but used less to analyze student’s teams. The results indicate that knowledge is created through the interplay between tacit and explicit knowledge. The knowledge creation process starts with individuals and then the whole team. This happens through the socialization, externalization, combination, internalization (SECI) process. Socialization happens during face‐to‐face meetings, lectures, and presentations. Externalization happens when students write down ideas such as code, project plans, and the system manual. Combination happens when teams use readily available knowledge in emails, the Internet, books and chat services. Internalization happens when they read and understand text books, class notes, and Internet information and apply that knowledge to the project. The results also revealed that intention, autonomy and creative chaos are major knowledge creation enablers in students’ knowledge creation processes. The knowledge creation process starts with sharing tacit knowledge, then the definition and adoption of concepts, building a prototype and the end product. It has also emerged in the study that mobile devices such as cellphones, and Blackberry devices facilitate knowledge creation among team members. In this study’s context, knowledge creation refers to software development. Keywords: knowledge creation, software development, case study, students’ teams, SECI
1. Introduction Knowledge creation is defined as value adding outcome/ output such as a product, service or process (Mitchell & Boyle 2010). It involves the definition of a problem and makes use of complex and discontinuous events and to strive to deal with the situation accordingly. Knowledge creation primarily happens in teams (Parent et al. 2000; Styhre, Roth & Ingelgard 2002). It could be defined from several perspectives; as a process or as an output/ outcome (Mitchell & Boyle 2010). It is a process when activities and initiatives are undertaken to generate new ideas or objects. When defined as output/outcome knowledge creation refers to the generation of new ideas that reflect a significant enrichment of existing knowledge. It could also mean that new knowledge is diffused, adopted and embedded as new products, services and systems (Mitchell & Boyle 2010). Knowledge creation has been investigated in many fields such as Knowledge Management and Computing. In the Computing field, knowledge creation studies usually focus on processes of knowledge creation. A number of theories have been developed to explain how knowledge is created. According to Imani (2007), widely used theories are; the organizational knowledge creation theory; communities of practice, knowledge integration theory and the complex responsive process of relating theory. The theory of organizational knowledge developed by Nonaka and Takeuchi has been select to inform the study. This theory has been hailed as the most influential theory that explains knowledge creation. Unfortunately, most of the studies conducted and the theories used (including the theory of organizational knowledge creation) focuses mainly on knowledge creation in business organizations and professionals within those organizations largely ignoring educational institutions and students’ teams. This study took a different direction and focused on students’ teams. In the context of the study, knowledge creation refers to software development (Bailin 1997).
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2. Theoretical framework 2.1 Data, information and knowledge This sub‐section will define data, information and knowledge. Knowledge is an abstract notion. There is no agreeable definition what knowledge is. Two schools explain how knowledge is created. One school states that knowledge stems from data and information and the other states that data and information stem from knowledge (Bierly, Kessler & Christensen 2000; Braganza 2004). It is therefore important to distinguish between data, information and knowledge because these terms are often used interchangeably. Data is unprocessed information. Data are sets of objectives facts about an event or just structured records of a transaction (Zins, 2007). Information is data that has been processed into a useful purpose and can be used for decision making. It is data with value to the understanding of a subject and in context (Chaffey & Wood 2005). Knowledge is “information possessed in the mind of individuals: it is personal information related to facts, procedures, concepts, interpretations, ideas, observation and judgment” (Alavi & Leidner 2001:109; Aven 2013). Knowledge could take several perspectives/forms: a state of mind, an object, a process, a condition of having access to information or a capability (Alavi & Leidner 2001). In software development knowledge could take the object perspective. The object is the output or end product (the software). Software development is regarded as knowledge creation (Bailin 1997). Knowledge is either tacit or explicit (Polanyi 1962; Nonaka & Takeuchi 1995). Tacit knowledge is what the knower knows, which is derived from experience and embodies beliefs and values (Nonaka & Takeuchi 1995). It is personal and difficult to formalize, making it difficult to communicate and share with others (Li & Gao 2003; Elfving & Funk 2006; Kulandaisamy & Ramanujam 2011). Explicit knowledge is knowledge that has been articulated and, more often than not, captured in the form of text, tables, diagrams, and product specifications (Puusa & Eerikäinen 2010; Kothari et al 2011).
2.2 Knowledge creation theories A number of theories have been used to investigate knowledge creation. They include among others the organizational learning theory (Argyris & Schon 1978), communities of practice (Wenger 1998), the theory of organizational knowledge creation or SECI (Nonaka & Takeuchi 1995), Experiential learning (Kolb, Boyatzis & Mainemelis 2000), the learning organization (Senge 1990) and the knowledge integration theory (Grant 1996). Argyris and Schon’s organizational learning theory explains how learning (knowledge creation) takes place in organizations. They state that organizational learning “happens when individuals, acting from their images and maps, detect a match or mismatch of outcomes to expectations which confirms or disconfirms organizational theory‐in‐use” (Argyris & Schon 1978:19). They report on three learning loops that happen in organizations; single loop learning; double loop learning and deuteron‐learning. Kolb et al.’s experiential learning assumes that learning happens through experience. It assumes that learning requires polar opposite abilities and that the learning must choose which learning abilities they will choose for a specific learning situation. The theory portrays two dialectically related gasping modes; concrete experience and abstract conceptualization and two dialectically relate transforming experience modes; reflective observation and active experimentation. Grant (1996) focuses on knowledge integration and innovation within a firm. Grant explores the coordinating mechanisms of how individuals in a firm integrate knowledge to foster innovation. The Learning Organization theory explains how the whole organization learns. That is, how whole departments and sections of the organization learn which leads to the whole organization learning. Senge mentions five core principles of the Learning Organization; personal mastery, systems thinking, team learning, mental models, and building a shared vision. All these theories explain how knowledge is created in organizations. They may differ in approaches but they all emphasize on one thing: knowledge creation. They all agree that in organizations, knowledge is created and held by individuals who they integrate it in organizational routines/processes/procedures to make the organization perform better. This study is informed by the theory of organizational knowledge creation by Nonaka and Takeuchi. The theory is discussed in the next sub‐section.
2.3 The theory of organizational knowledge creation The theory of organizational knowledge creation explains how individuals, teams and entire organizations create knowledge. The core of the theory lies in the description of four modes of knowledge conversion that are created when tacit and explicit knowledge interact with each other. The theory assumes that knowledge creation lies in the mobilization and conversion of tacit and explicit knowledge in four modes. The four modes
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Mzwandile Shongwe are: socialization, externalization, combination, and internalization (SECI). These four nodes constitute the engine of the entire knowledge creation process. Socialization is the conversion of tacit knowledge to tacit knowledge. Externalization is the conversion of tacit knowledge to explicit knowledge. Combination is the conversion of explicit knowledge to explicit knowledge. Internalization is the conversion of explicit knowledge to tacit knowledge (Nonaka & Takeuchi 1995; Nonaka, Tayoma & Konno 1998). Figure 1 depicts the four modes of knowledge creation.
Source: Adapted from Nonaka and Takeuchi, 1995, p. 19 Figure 1: The SECI process Its ontological assumption is concerned with the levels of knowledge creating entities: individual, group, organizational, and inter‐organizational levels. The theory assumes that knowledge is created first at individual, then at group, organizational, and inter‐organizational levels (Nonaka & Takeuchi 1995). The theory of organizational knowledge creation explains five knowledge creation enabling conditions: Intension, autonomy, fluctuation and creative chaos, redundancy and requisite variety (Nonaka 1994; Nonaka & Takeuchi 1995). Intension means organizational aspirations to its goals. It could be the business strategy. Autonomy means that individuals working within an organization should be given as much autonomy as possible to their work. This allows them to do trial and error thus creating and refining knowledge. Fluctuation and creative chaos promotes the interaction between the organization and its external environment. Organizational fluctuation causes creative chaos which triggers knowledge creation. Redundancy means the intentional overlapping of information about organizational activities. The sharing of redundant information promotes tacit knowledge creation. Requisite variety means the availability of diverse information to organizational members for knowledge creation (Nonaka 1994; Nonaka & Takeuchi 1995). The theory also presents five methods of the knowledge creation process in organizations: sharing tacit knowledge, creating concepts, justifying concepts, building archetypes, and cross leveling of knowledge. During the tacit knowledge sharing stage, individuals share emotions, feelings, and mental models through face‐to‐face interactions. Creating concepts involves the sharing of both tacit and explicit knowledge. After sharing tacit knowledge, organizational members then articulate the tacit knowledge into explicit knowledge through written concepts. Justifying concepts is a phase whereby created concepts are determined whether they are useful or not in the organization. The justified concepts are then converted into tangible or concrete product or model called an archetype. After knowledge has been created it is then leveled across the organization. This could be inter‐organizationally or intra‐organizationally (Nonaka 1994; Nonaka & Takeuchi 1995).
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Mzwandile Shongwe The theory was extended to include the concept of ‘ba’, a shared context where knowledge is created. ‘Ba’ provides the energy, quality and places to perform the individual knowledge conversions. It could be physical place such as an office or a virtual space. Four types of ‘ba’ are given: originating, dialoguing, systematizing, and exercising ‘ba’ (Nonaka, Toyama & Konno 1998:16). Many researchers have used the theory to investigate knowledge creation in software development. Among others are Linden and Cybulski (2009), and Wan et al. (2010) among others. These studies investigated knowledge creation processes in software development in the context of the SECI process. On the other hand, the SECI model has been criticized by Gourlay (2006), Poell and Van de Krogt (2003) among others. These authors state that the theory is flawed because of lack of empirical rigor, the omission of tacit knowledge and the subjective definition of knowledge. They also dispute Nonaka’s notion that workers learn within the boundaries set by management and its misunderstanding of Polanyi. . Nonetheless this theory is hailed as one of the best knowledge creation theories.
3. The case As part of the curriculum, third year Computer Science and Management Information Systems students at a South African University are required to complete a software development project. The students have to develop and deploy a working system (an interactive website in this case) for a real client. They have to go through all the stages of systems development: system analysis, prototyping, development, testing, and deployment. In this case six software development teams were to complete this task. They had to find a rural client to develop the system for. Their first task was to collect background information about the client’s business. The client then indicated the functionalities the system should have. The teams would then decide on the approach. After requirements analysis the teams were expected to draw up a project plan. The project plans included major milestones and the entire timeframe of the project. After this stage, the teams were required to design a prototype, then the actual development of the system. The project lasted the whole semester, which is four months long. Data were collected over the course of the semester. The researcher attended lectures, presentation and meetings that the teams had. These activities took place in the students’ computer laboratory, lecture hall and library. The aim was to be part of the teams as if the researcher was one of them. Lectures were held once a week for the whole semester. Presentations were held based on need. For example, if there were milestone deliverable (e.g. project plan). The teams also had meetings almost daily to discuss and develop the project. During these activities (lectures, presentations and team meetings), the researcher observed and interviewed teams. During observation, detailed notes were taken on how the students developed the project. The researcher observed how, where and what ideas teams shared and the impact of these ideas on the completion of the project. Group interviews were conducted to supplement observation data. It was used to seek further clarification on certain development processes such as, what information sources and communication channels teams used in their projects. Qualitative Content analysis was then used to analyze the responses.
4. Results and discussions 4.1 Knowledge creation processes 4.1.1 Individual knowledge tacit and explicit knowledge creation Individual team members played important roles in all the stages of the knowledge creation process (system development process). Individual members had specific tasks such as: coding, documentation, preparing power point presentations, designing the user interface, research and information gathering, sourcing finance and other relevant tasks. One respondent stated that “I was mainly the website designer, but I also did documentation such as project plan, feasibility study and other tasks”. Another reported “as an individual, I created forms in the prototype”. Others reported on other minor tasks that they did such as minute writing, and arranging meetings. For these kinds of tasks, students used both tacit and explicit knowledge to complete them. They used their tacit knowledge in the form of judgments, and innovation. Tasks such as design required a great deal of tacit knowledge. Tasks such as documentation, coding were explicit in nature.
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Mzwandile Shongwe The importance of individual knowledge contribution was emphasized by all group members. One team stated that, “the contribution of each member was great. For every task we had to break it down to individual tasks. In so doing we shared ideas which enabled us to finish the project quicker”. Another team stated that “every topic that we discussed, each and every person shared ideas and came up with one solution that the whole group agreed upon”. 4.1.2 Team knowledge creation Individual team members would then present their individual tasks during group meetings for further debate and possible adoption. During the meetings, teams would brainstorm, further refine individual ideas and then adopt them as project ideas. This was usually a lengthy and emotional process because teams would disagree. It was during such meetings that teams finally adopted a unified stance on an idea which was then regarded as a team idea. One team member indicated that “during group meetings, we were always brainstorming and coming up with new ideas”. Another one stated that “the group meetings helped us come together and share ideas”. Tacit knowledge was mostly shared and created during group meetings. The tacit knowledge would then lead to the creation of explicit knowledge in the form of notes, project plans, system documentation, power point presentation and other project relevant documents. System development was also mostly done as a group when individual tasks were put together. One respondent stated that “after brainstorming we would write down the ideas either for submission or for future use”. These results are consistent with many knowledge creation theories (Argyris & Schon 1978; Senge 1990; Nonaka & Takeuchi 1995; Wenger 1998) that show that knowledge is created first by individuals, then refined by the group before it could be organizational knowledge. In this case knowledge creation ends in these two levels. It does not continue to organizational level. This is because the students only create temporal knowledge, they are only learning. What can also be deduced from these results is that students’ teams use four of the five methods of knowledge creation. Students share tacit knowledge during face‐to‐face meetings where they justify concepts before they can be adopted as team knowledge. Once they are accepted they are written down as project deliverables (explicit knowledge).
4.2 Information sources for knowledge creation A number of information and knowledge sources were used by the student teams during the knowledge creation process. The sources used include class presentations, lecturer and lecture notes, books, the Internet, senior students and professional software developers. Class presentations were considered to be a good source of information. Upon achieving a major milestone such as the completion of system analysis, budgeting, prototyping, documentation, etc., the group would present the outcome to the whole class and the lecturer. One student stated that: “Presentations helped us to know what we did right, where to correct and how to improve the project.” Another concurred: “Presentations helped us to get clarity as to what is really required from us”. All the groups agreed that in one way or another, the presentations helped them to gain new knowledge about the project. The lecturer also played a crucial role in the knowledge creation process. Other than giving them the problem to solve, the lecturer was also involved in helping the teams solve the problem. This was confirmed in many of the comments: “The lecturer gave us motivation to finish the project”; “The lecturer helped by explaining what is expected”; and “the lecture made us to attend classes, [sic] and provided us with technical skills”. Students indicated that they relied a lot on the Internet for information. Most teams indicated that they used the Internet to conduct research on the project’s activities (e.g. how to budget, how to perform systems analysis, etc.) and also to find technical information (e.g. coding information). They surfed the Internet to find solutions to specific technical problems. One team indicated that they used the Internet to find information on how to set‐up the Apache, MySQL, PHP for Windows (WAMP) interface. The w3schools website was said to have provided much support to the teams. They also relied on books, senior students and professionals to complete their projects.
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4.3 Enabling conditions for knowledge creation Nonaka and Takeuchi (1995) state that intension, autonomy, fluctuation and creative chaos, redundancy and requisite variety are enabling conditions for knowledge creation in organizations. Some of these enabling conditions were discovered in students’ teams. The teams indicated that their main intensions were to learn to develop a working system and obtain maximum marks for the project. One team indicated that “our intension is to learn how to run a project and create a working website”. Another teams stated that “we want to gain knowledge and we want to pass”. These intensions made students to work harder and successfully finish their projects. Autonomy also played a very important role. The students’ teams acted autonomously. They decided what they wanted to do, what features to include in the system and how the system would work. There was no pressure on how they should work and where they should work and at what time they should work. This autonomy enabled them to be creative. Creative chaos is generated when an organization faces challenging tasks. It could be evoked on purpose by management to challenge workers to solve complex tasks (Nonaka & Takeuchi 1995). In this case, the lecturer evoked creative chaos by giving students a challenging task to complete; developing a working software system. It was a task they had never done before. They had no idea how to do it. One group stated that “at first, we had no idea what we were supposed to do and how to do the project”. Another group concurred “we were scared of the project, we had no project experience”. This task created a lot of confusion among the teams. The confusion made them to think, to be innovative. It was after they have discussed, refined their ideas, and sought clarification that they understood what to do which lead to the creation of a working system.
4.4 The SECI processes in students’ knowledge creation activities The results indicate that students’ software development teams also go through the SECI knowledge creation process. 4.4.1 Socialization Socialization happens when students share ideas face‐to‐face during group meetings, presentations, and lectures. During group meetings, team members share their tacit (ideas and experiences) knowledge. They also learned by trial and error, that is, they code the system, deal with bugs until they have a working system. They learn by actually doing the project. During these instances students share their tacit knowledge (ideas and experiences) refine it and further refine it until they develop more refined useful tacit knowledge in the form of group adopted ideas. Teams also indicated that they were helped by senior students and professionals during their project. Socialization happens when senior students and professionals help them with some of the coding tasks. The seniors and professionals show them how to code which the students then learn by seeing what the seniors and/or professionals are doing. The seniors and professional share their tacit knowledge when they show the students how to code and advise them on how to develop the system. 4.4.2 Externalization After sharing the ideas (tacit knowledge) during meetings, teams then document the ideas and tasks in hardcopy or electronic formats (explicit). The documented knowledge comes in the form of project deliverables. The deliverables were: power‐point presentations, the project plan, the budget, system manual, and other documents (minutes of minutes, risk analysis) and the completed system which is regarded as knowledge (Klint & Verhoef 2002; Morner & von Krogh 2009). 4.4.3 Combination Combination happened mostly over the Internet. Students indicated that they sought information from the Internet, used that information to create knowledge. They could copy and paste code from the Internet onto their project to achieve their goals. Students would also send each other messages via email, social networks, and instant messaging services. These messages would be project ideas which were either written down as part of the project documentation or ideas to be discussed during group meetings. Books were also used in the combination process. Students would consult books for project tasks such as budgeting and systems analysis. They would use such information for the deliverables (e.g. presentations).
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Mzwandile Shongwe 4.4.4 Internalization Students indicated that they used a number of sources for the project. They used books, the Internet, class notes and other printed and electronic sources. They read these sources internalized the knowledge and applied it in the completion of the project. For example when they read notes or information from a website on how to code a certain function and then used that information for the actual coding of their project. A summary of the SECI process is depicted in the figure below. Socialization
1. face‐to‐face meetings 2. lectures 3. presentations 4. learning by doing (actual development, apprenticeship by senior students and professionals) 1. reading text books, class notes, internet information 2. experiences from the whole project
Internalization
Externalization
1. project plan 2. power‐point presentations 2. budgets 3. system manual 4. Other documents (risk analysis, systems analysis, etc.) 1. Internet information 2. messages from fellow team members (WhatsApp, BBM, Facebook) 3. email 4. books
Combination
Figure 2: Students’ teams SECI processes
5. Conclusions, limitations, and further research The study used Nonaka and Takeuchi’s theory of organizational knowledge creation to analyze knowledge creation activities in students’ software development teams. The study revealed that knowledge is created at individual and group levels in students’ teams. In the students’ case knowledge is created only at two ontological levels; individual and group levels. The knowledge is not used by the whole organization. Once the projects are marked the students disappear with their knowledge. Students use information sources such as books, class notes, the Internet, senior students and professionals and lecturers to create knowledge. The study also revealed that four knowledge creation methods (sharing tacit knowledge, creating concepts, justifying concepts, building archetypes) are also applicable in students’ groups. Only cross leveling is not application because the knowledge created is not used at organizational level. Knowledge is created through the interplay between tacit and explicit knowledge through the SECI process. The major limitation of the study is the fact that only one group of students, in one university was studied. The results could be different if more teams from different students could be studied. Further research should be conducted with more teams so that the results can be generalized.
References Alavi, M. and Leaidner, D.E. (2002) Knowledge Management Systems: issues, challenges and benefits. In S. Barnes, Knowledge Management Systems Theory and Practice, (pp 15‐32). Oxford: Thompson Learning. Argyris, C. and Schon, D. (1978) Organizational learning: a theory of action perspective, Addison‐Wesley Publishing Company, Reading. Aven, T. (2013) “A conceptual framework for linking risk and the elements of the data–information–knowledge–wisdom (DIKW) hierarchy”, Reliability Engineering & System Safety, Vol111, March, pp 30–36. Bailin, S. (1997) “Software Development as Knowledge Creation”, International Journal of Applied Software Technology, Vol3, No.1, March, pp 75‐89. Bierly, P.E., Kessler, E.H. and Christensen, E.W. (2000) “Organizational Learning, Knowledge and Wisdom”, Journal of Organizational Change Management, Vol13, No.6, pp 595 ‐618. Braganza, A. (2004) “Rethinking the Data – Information – Knowledge Hierarchy: towards a case‐based model”, International Journal of Information Management, Vol24, August, pp 347 – 356. Chaffey, D. and Wood, S. (2005) Business Information Management: Improving Performance Using Information Systems, Pearson Education Limited, London. Elfving, S. and Funk, P. (2006) “Enabling Knowledge Transfer in Product Development and Production – Methods and st Techniques from Artificial Intelligence”, Paper presented at the 1 Nordic Conference on Product Lifecycle Management, Goteborg, Sweden, January. Gourlay, S. (2006) “Conceptualizing knowledge creation: a critic of Nonaka’s theory”, Journal of Management Studies, Vol47, No.7, November, pp 1415‐1436.
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Mzwandile Shongwe Grant, R.M. (1996) “Towards a knowledge‐based theory of the firm”, Strategic Management Journal, Winter, Vol17, pp 109‐122. Imani, Y. (2007) “Knowledge creation, business and art: exploring the contradictions and commonalities”, Journal of Visual Art Practice, Vol6, No.2, October, pp 141‐153. Klint, P. and Verhoef, C. (2002)“Enabling the creation of knowledge about software assets”,Data & Knowledge Engineering, Vol41, No. 2‐3, June, pp 141‐158. Kolb, D.A., Boyatzis, R.E. and Mainemelis, C. (2000) Experiential Learning Theory: Previous Research and New Directions. In R. J. Sternberg and L. F. Zhang (Eds.), Perspectives on cognitive, learning, and thinking styles. NJ: Lawrence Erlbaum, 2000. Kothari, A.R., Bickford, J.J., Edwards, N., Dobbins, M.J. and Meyer, M. (2011)” Uncovering Tacit Knowledge: A Pilot Study to Broaden the Concept of Knowledge in Knowledge Translation”, BMC Health Services Research, Vol11, pp198‐208. Kulandaisamy, D. and Ramanujam, B. (2011) “Protocol Based Approach for Tacit to Explicit knowledge Conversion”, Computer Technology and Application, Vol2, No.2, February, pp 75‐79. Li, M. and Gao, F. (2003) “Why Nonaka highlights Tacit Knowledge: a Critical Review”, Journal of Knowledge Management, Vol7, No.4, pp 6‐14. Linden, Tanya and Cybulski, Jacob (2009)“Knowledge creation in an application domain : a hermeneutic study”, in ICKM 2009, Proceedings of the 6th International Conference on Knowledge Management : Managing knowledge for global and collaborative innovations, ICKM, Hong Kong, China, pp. 1‐13. Mitchell, R. and Boyle, B. (2010)“Knowledge Creation Measurement methods”, Journal of Knowledge Management, Vol 14, No.1, pp 67‐82. Morner, M. and von Krogh, G. (2009)“A Note on Knowledge Creation in Open‐Source Software Projects: What Can We Learn from Luhmann’s Theory of Social Systems”,Systems Practice and Action Research, Vol22, No. 6, December, pp 431‐443. Nonaka, I. and Takeuchi, H. (1995) The knowledge creation company: how Japanese companies creation the dynamics of innovation, Oxford University Press, Oxford. Nonaka, I. (1994) “A dynamic theory of organisational knowledge creation”, Organisation Science, Vol5, No.1, February, pp 14‐37. Nonaka, I., Toyama, R. and Konno, N. (1998) “SECI, Ba and Leadership: a unified model of dynamic knowledge creation”, Long Range Planning, Vol, 33, February, pp 5‐34. Parent, M., Gallupe, R. W. Salisbury, W.D. and Handelman, J.M. (2000) “Knowledge creation in focus groups: can group technologies help”? Information and Management, Vol38, No.1, October, pp 47‐58. Poell, R.F. and Van der Krogt, F.J. (2003) “Learning strategies of workers in the knowledge‐creating company”, HRDI, Vol6, No.3, September, pp 387–403. Polanyi, M. (1962)“Tacit Knowing: Its Bearing on Some Problems of Philosophy”, Reviews of Modern Physics, Vol34, No.4, October, pp 601‐616. Puusa, A. and Eerikäinen, M. (2010). “Is Tacit Knowledge Really Tacit?” Electronic Journal of Knowledge Management, Vol8, No.3, November, pp 307–318. Senge, P.M. (1990) The fifth discipline: the art and practice of the learning organization, Doubleday/Currency, New York. Styhre, Alexander, Roth, Jonas and Ingelgard, Anders (2002) “Care of the other: knowledge creation through care in professional teams”, Scandinavian Journal of Management, Vol18, No.4, December, pp 503‐520. Wan, J., Zhang, H., Wan, D. and Huang, D. (2010) “Research on Knowledge Creation in Software Requirement Development”, Journal of Computing and Applications, Vol3, May, pp 487‐494. Wenger, E. (1998) Communities of practice: learning, meaning, and identity, Cambridge University Press, Cambridge. Zins, C. (2007) “Conceptual Approaches for Defining Data, Information, and Knowledge”, Journal of the American Society for Information Science and Technology, Vol58, No. 4, February, pp 479–493.
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Do it Like the European Union (EU) Does: The Applicability of EU Knowledge Cost Management to Start ups Evangelia Siachou and Dimitris Apostolidis Business and IT Division, Hellenic American University, Manchester, USA esiachou@hauniv.us dapostolidis@huaniv.us Abstract: Knowledge acquisition is one of the prerequisites for any organization operating across the world and aiming at the generation of innovative business portfolios. Despite the significance and the necessity of this process, which often promises increased organizational performance outcomes and efficiency of operations, there are hidden knowledge costs, which should be revealed and managed. Extant literature on knowledge costs is limited and often focuses on knowledge search and transfer costs. We analyze the European Union’s (EU) manner in managing knowledge costs, thus building a proposed framework for start‐ups which are in great need of effectively managing such costs so as to achieve successful implementation of innovative projects. In our efforts to expand prior work on knowledge costs, we also distinguish and discuss two more types of knowledge costs that are, knowledge acquisition and implementation. European Institutions by their nature represent central knowledge processors –i.e. they simultaneously operate as both sources and recipients of knowledge. As such, based on the nature and mandate of the Institution the knowledge acquired processed and subsequently transferred, represents a well integrated and highly flexible intangible asset ready to be adjusted to the pertinent circumstances. The methodological schema lies in the analysis of the EU case study which reveals that certain practices adopted and followed by the EU when implemented by start ups may minimize knowledge costs. Keywords: knowledge acquisition, knowledge costs, start‐ups, European Union
1. Introduction The acquisition of external knowledge is, among other things, one of the principal actions undertaken by organizations in their function as innovative performers. Based on extant literature, knowledge acquisition provides organizations with new insights, know‐how or patterns, which are found to be influential for their innovative actions. In most cases that have been empirically investigated, obtaining knowledge –either tacit or explicit– from various external sources has a direct positive impact on organizational performance (Arrow, 1974; Cohen and Levinthal, 1990). That is, when organizations acquire knowledge externally, they also broaden and modify the existing knowledge, thus adding considerable value to the ongoing growth of their knowledge stock. In this manner, organizations are able to make good use of the necessary knowledge which they may lack at an earlier stage hence being at distance from innovative movements. Nevertheless, the positive effect of external knowledge acquisition on an organization’s performance is occasionally questionably anchored in specific factors, which may disable the potential organizational benefits. In the existing literature various features have been considered as accountable in restraining the outcome of a knowledge exchange process. As such, the nature of the relationships between subunits (strong vs. weak ties) in an organization and their impact on knowledge search and transfer efforts has been proposed by Hansen (1999). Additionally, the significance of time constraints for the incoming knowledge to be effectively implemented in organizational routine tasks and activities is also discussed (Edmondson, 2003). Even as, the importance of the contribution of third parties to various projects undertaken by innovative organizations is also considered as a point of reference which may also facilitate the acquisition of knowledge from external sources (Magretta, 2002; Davenport et al, 1996; Subramaniam and Venkatraman, 2001). Additionally, the absorptive capacity of organizations, i.e., the value of knowledge that is externally derived, its assimilation and implementation in commercial ends is also recognized as an antecedent with an impact on knowledge acquisition (Cohen and Levinthal, 1990). This study is based on the relational view of competitive advantage (Dyer and Singh, 1998; Hitt et al., 2000) “… that focuses on dyad/network routines and processes as an important unit of analysis” (Dyer and Singh, 1998, p. 661) and focuses on three motions of linkages between the start‐ups and their stakeholders in the context of knowledge acquisition. Each of these motions is investigated and evaluated based on its effectiveness as potential source of international and/or local competitive advantage gained by start‐up corporations.
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Evangelia Siachou and Dimitris Apostolidis Our aim is to draw on the EU’s knowledge acquisition processes and effectively integrating it into a common, readily applicable, framework for start‐ups which they should effectively deal with knowledge costs in their attempt to launch innovative business models before their competitors. European Institutions by their nature represent central knowledge processors –i.e. they simultaneously operate as both sources and recipients of knowledge with the latter residing in multiple sources. As such, the entire process results in a well integrated and highly flexible intangible asset adjustable to pertinent circumstances.
2. State of the art 2.1 Knowledge acquisition and knowledge costs Existing literature identifies that knowledge management activities are often followed by costs which may impede their effectiveness. References in the literature related to knowledge costs date from the late ‘70s when Teece (1977) “found that the principal determinants of transfer costs are the degree of previous experience with transferring the technology, the age of the technology, and the number of firms using similar technology” (In Kogut and Zander, 1993, p. 631), however, are not frequently discussed in the last decade. In a most recent perspective Hansen et al. (2005), make use of search costs and costs of transfers in seeking knowledge across subunits, whilst earlier Hansen (1999) referred to the search‐transfer problem in the context of weak inter‐unit ties to link the complexity of knowledge acquisition processes with the potential search and transfer costs. Similar perspectives, also view the acquisition costs in the context of technology transfer in terms of the difficulty to imitate technological knowledge itself (Dierickx and Cool, 1989; Reed and DeFillippi, 1990; Simonin, 1999). Regardless of which knowledge cost perspective is adopted by scholars, there is a consensus in the literature, that costs related to knowledge refer to the specific time spent by corporations to find out and access valid knowledge sources as well as to effectively implement the newly accessed knowledge in their routine tasks and activities. In addition, such costs are often recognized as search and transfer costs with main reference to knowledge that is externally derived. Despite the aforementioned cases, few studies, to date, have focused on managing knowledge costs in identifying manners applicable to organizations or start‐ups to minimize them. Our study contributes to this theoretical gap by providing a conceptual framework which is based on the EU’s knowledge management processes. Furthermore, we estimate that along the lines with search and transfer costs ‐already identified and discussed throughout the literature‐ two additional types of costs (i.e., knowledge acquisition and implementation) should be also reveled and appropriately managed. To address the concept of the four distinct types of knowledge costs to be examined (i.e. search, acquisition, transfer and implementation) we built upon evaluation of existing work to shape implementations for the decrease of the aforementioned costs when they are adopted by start‐ups, For the scope of this study all of the knowledge costs were constructed based on the variable of time and their conceptualization is discussed in section 2.3 of the paper. The section which follows depicts the EU’s manner in acquiring knowledge that is externally derived and outline three motions for knowledge costs minimization with potential applicability to start‐ups.
2.2 The case of the European Union (how the EU acquires knowledge) The EU is a complex organization balancing state sovereignty and European integration. It is an organization based on the exchange of information and knowledge, the processing of them through its institutions and their dissemination to both its members and all relevant institutions. Integration implies that this knowledge exchange is based on the development of processes that will facilitate knowledge acquisition and transfer between the interested parties, thus creating various Communities of Practices (CoP) (Wallace, 2000). The acquisition of knowledge by the EU is based on a complex system of information exchange. As the evolution of the Union (from a single market to the free movement of peoples and capital) as well as directives and regulations addressing various aspects of the Union’s functions reflect, the purpose of such a complex system of exchange is not to duplicate efforts but share the cost of collecting and transferring knowledge. Principles such as subsidiary and reciprocity assure that certain body of knowledge need not be transferred beyond its immediate area of implementation. Overall, the EU is best described as knowledge processor. The Union’s structure is comprised of a number of institutions that follow different decision mechanisms subject to
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Evangelia Siachou and Dimitris Apostolidis their respective powers and mandates, thus are in need of acquiring different types and amounts of knowledge. Hence, each institution, based on both, handles and processes the acquired knowledge differently. Some of them manage knowledge with the aim to formulate policy, as is the case of the Commission. Others become simply a repository of knowledge as is the case with Eurostat, whilst a third category, develops knowledge through the processing and analysis of information. Irrespective of the nature of the institution involved, knowledge is accumulated from various sources at regional, state or European level. EU acquires knowledge based in three motions each addressing a specific set of recipients. More specifically, the first motion establishes knowledge acquisition from the periphery by the center. That is, from the member states of the Union to its center (i.e., Brussels) and the respective Union Institutions. The second motion addresses knowledge acquisition by the periphery (the member states) from the center. This reflects the process by which initiatives by the Commission, through the appropriate processes, turn into formal policy, which is then communicated to the respective members along with the means for its implementation. This in essence is the acquis communautaire’ and is communicated either as a directive or a regulation. The third and last motion is that between the EU and prospective members. The center offers the candidate state the know‐ how and the financial means to transfer and implement the necessary knowledge to elevate its institutional efficiency to a level comparable to the existing members. Most importantly, this process will enhance their candidate state’s ability to assimilate the knowledge encapsulated by the ‘acquis’. Regardless, of which target motion knowledge seekers fall under their aim is to be able to take advantage of the knowledge processor which permit the recipients, in each case, to manage the costs associated with the search, acquisition, transfer and implementation of the knowledge attained (Daviter, 2007). While all three motions address a specific set of recipients, the knowledge exchanged represents a well‐ integrated and highly flexible intangible asset. The pooling of resources and the reliance on integrated mechanisms for the accumulation, analysis and dissemination of information among recipients allows both center and periphery to manage costs. The flow of knowledge from the periphery to the center assures that the latter’s processes cumulative and accurate knowledge, which will provide the basis for further analysis. By using the local processes in place to collect this knowledge the center does not invest time in searching for the appropriate sources of knowledge nor does it duplicate mechanisms for its acquisition and transfer. In a similar manner, the periphery, as a recipient of knowledge acquisition does not need to duplicate processes and mechanisms to intergrade, analyze, transfer and implement the resulting policies that form the ‘acquis’ of the community. Where the managing of knowledge costs is most evident is the third motion, that addressing knowledge acquisition by candidate states. Despite the volume of knowledge needed by the candidate state to function within an integrated Union, the cost associated with all aspects of knowledge acquisition (i.e., identifying the knowledge sources, the necessary transfer mechanisms, and the technical know‐how) are but a fraction of what they would be, had the candidate state been unable to access the central knowledge processors of the EU, namely its institutions.
2.3 The framework of knowledge costs minimization Based on the EU manner as discussed above we develop a framework for managing knowledge costs with potential applicability to start‐up corporations (Figure 1). Start‐ups are companies that have been developed by an individual company or represent the outcome of a partnership (e.g. strategic alliance, joint venture, mergers or acquisition). The effective participation of start‐ups in the product market requires, among other things, the acquisition of new knowledge. In line with the literature, the acquisition of external knowledge although linked to increased performance is by its nature complicated and followed by knowledge costs, which should be revealed and managed. Our framework distinguishes between four types of knowledge costs, i.e., knowledge search, acquisition, transfer and implementation– which are all defined based on the indicator of time spent by a company to search, acquire, transfer and assimilate new amounts of knowledge that are externally derived. More specifically, search costs include the considerable amount of time spent searching in order to locate the appropriate new knowledge required. Acquisition costs include the considerable time needed for the prompt extraction and assimilation of new specific amounts of knowledge sourced outside organizational boundaries. Transfer costs include the considerable effort to effectively distribute the new knowledge obtained from various sources within organization and knowledge implementation costs include the considerable time
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Evangelia Siachou and Dimitris Apostolidis needed in order for the incoming knowledge to be effectively implemented in organizational routine tasks and activities. Derived from EU’s motions, the framework provided exemplifies three options available to a start‐up corporation in the need of acquiring knowledge outside its boundaries, More specifically, a start‐up corporation may acquire new knowledge when (i) accessing various knowledge repositories as defined in the extant literature, (ii) searching linkages from its external environment and/or (iii) form alliances and/or partnerships with other organizations which posses the required knowledge (knowledge keepers). These relationships are viewed in the framework below (Figure 1) each having an impact on the management of knowledge costs. Start-ups
Knowledge Repositories
Search Costs
Acquisition Costs
External Environment
Transfer Costs Strategic Alliances
Implementation Costs
Knowledge Sources
Knowledge Costs
Figure 1: The framework of knowledge costs minimization with applicability to start‐up corporations As with the motions described as part of the EU knowledge acquisition processes, none of the three options available to a start‐up are mutually exclusive. Depending on its needs, strengths and weaknesses, a start‐up may select to exclusively access the required knowledge through one of them or combine those that best fit its needs. The use of knowledge repositories resembles greatly the third motion used by the EU candidate state to acquire the knowledge necessary to enhance its institutional infrastructure and policy cohesion with respect to those of the EU. As with the candidate states, a start‐up will be able to minimize all of the relevant costs and place itself in a competitive footing compared to those that will rely on themselves to develop the knowledge and mechanisms necessary for their operations. The use of strategic alliances resembles the pooling of resources of the various sources of knowledge available within the EU structure culminating in an integrated body of knowledge that is flexible and application ready. Accessing repositories located in the external environment resembles the continuous flow of knowledge between the center and periphery using existing channels, thus minimizing costs associated with search and acquisition of knowledge but not necessarily with transfer and implementation.
3. Conceptualization and research propositions Since knowledge acquisition is essential for organizations to innovate, employees are often enforced to seek external knowledge sources or access networks in order to obtain the necessary knowledge (Haas and Hansen, 2005). Although organizations believe that such incentive practices may increase the willingness of employees to engage in knowledge exchange processes while at the same time increase the knowledge stock of organizations (Gupta and Govindarajan, 2000), the establishment of incentive practices does not always yield the desired task performance outcomes. The random knowledge search increase the time spent by employees to pinpoint valid and appropriate external repositories of knowledge, thus increasing knowledge costs. Equally, efforts to access existing networks or to look for various CoP outside organization is also found to be a time consuming activity that may impair organizational performance, as, for instance, the estimated product completion time may not be met. Consequently, we could suggest that acquiring knowledge from the external environment does not always yield beneficial outcomes for knowledge seeker organizations.
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Evangelia Siachou and Dimitris Apostolidis Extant literature has also identified possible sources from which an organization can extract the required knowledge. As such technical reports, patent databases, conferences, scientific publications and use of Internet as well as sources, which occur after coordinated efforts on the part of organizations to establish linkages (e.g. University‐industry cooperation, mergers and acquisitions or strategic alliances) are considered as knowledge repositories (Ahuja and Katila, 2001; Caloghirou et al., 2004; Cohen et al., 2002; Soulitaris, 2001). To gain access to such sources, though, the collaboration between organizations must be secured in order for a knowledge transfer process to materialize (Kogut, 1988). Management scholars (e.g., Grant and Baden‐Fuller, 2004; Inkpen and Beamish, 1997; Inkpen, 1998) illustrate the effectiveness of alliances as external sources of knowledge and highlight a direct positive effect of external knowledge acquisition on organizational performance outcomes. In the context of strategic alliances, numerous moderators have also been examined. As such, the significant role of trust that has been developed (or should be) between the partners or the powerful relationships between the parties in an alliance increase the effectiveness of knowledge exchange processes. However, prior literature lacks evidence to support whether or not alliances could function as determinant factor for knowledge seekers in minimizing or effectively managing knowledge costs. Moreover, the majority of organizations which are in great need of acquiring knowledge tend to develop inter‐ organizational relationships which include customers, suppliers, investors and so on which may offer a useful exchange of experiences and knowledge that aid organizations in their future growth. However, these relationships may not be adequate for the prompt acquisition of specific knowledge when needed since they are not built on personal relationships with strong ties between the two interacted parties (Lyles and Schwenk, 1992; von Krogh et al., 1994). Perhaps a permanent collaboration between core employees and stakeholders could facilitate the latter to share the knowledge they possess in an ongoing process. Moreover, long‐term relationships may ensure issues of loyalty and commitment between the two parties in a knowledge exchange relationship. In reviewing the extant literature we support the claim that the acquisition of external knowledge is often linked to beneficial organizational performance outcomes able to effectively manage the newly acquired knowledge. However, the acquisition of knowledge is followed by knowledge costs, which may impair its efficacy. Based on this rationale, and in order to expand on current literature we delineate research propositions regarding the extent to which the three motions of external knowledge acquisition available to start‐up corporations could decrease knowledge costs. In addition, we also hypothesize the impact of knowledge acquisition motions on each of the four distinct types of knowledge costs. Our research propositions are summarized as follows: RP1: Knowledge acquisition from various external sources does not lead to knowledge costs minimization. RP2: Knowledge acquisition from the external environment leads to some extent to knowledge costs minimization. RP3: Knowledge acquisition from alliances or partners leads to knowledge costs minimization.
4. Inferences and concerns Start‐ups directly participate in the product or service market within a short time span with the tendency to enter foreign markets (Christensen, 1997; Lerner and Merges, 1998). To earn their returns, to gain competitive advantage and efficiency of their operations, they are increasingly in need of urgently obtaining and instantly using new knowledge that is sourced outside its boundaries. Thus, there is not only the mere acquisition of knowledge that matters but also its value and accuracy has to be legitimated at the same time, implying that hidden knowledge costs should be appropriately managed. The main outcome of this study is that a decrease in knowledge costs can be achieved mainly through the formation of strategic alliances. This is also supported by the prior work of Darr et al. (1995) who suggest that the productivity of one corporation is a matter of their partners’ success, thus highlighting a consistency in the literature. Without underestimating additional efforts made by knowledge seekers to obtain new knowledge ‐ either from their external environment or from other sources (e.g., conferences, data bases, etc)‐ we support the claim that for the case of start‐up corporations they are not found to be effective. More specifically, it is
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Evangelia Siachou and Dimitris Apostolidis supposed that start‐ups without having prior presence in the sector in which they are planning to operate have not equally developed the necessary powerful relationships with the stakeholders of the sector which might secure them (easy) access to the amounts of knowledge required. It's true that strong ties between units or teams positively affect the outcome of a knowledge acquisition and sharing activity. In the case of permanent collaborations, the new knowledge acquired is subject of mutual exchange between two or more parties. In contrast, temporary (or novel) relationships that a company attempts to form or develop with its stakeholders cannot guarantee by their nature prompt acquisition of knowledge on an ongoing basis. Furthermore, such relationships could also not be characterized as fruitful since the establishment of personal contact with those interested in extracting specific knowledge is a prerequisite for a successful knowledge transfer. Elements of trust, the willingness of those possessing the required knowledge or “win‐win” expectations are also taken into consideration by the parties involved in knowledge acquisition (Davenport and Prusak 1998; DeLong and Fahey 2000; Gupta and Govindarajan 2000; Nevis et al.,1995). Taking also into account that the knowledge acquisition process, even if it is coordinated, is by nature time‐consuming and likely to impair the expected organizational outcomes, appropriate relationships in terms of alliances should be established between the parties involved in the knowledge exchange in order for its inherent difficulties to be obviated. Even if start‐ups seek to acquire new knowledge by making considerable efforts to gain access to various knowledge sources available to the majority of organizations (thus overcoming the accessibility of knowledge repositories) they may be confronted with issues related to the accuracy, validity or implementation of the knowledge to be acquired. The knowledge that is sourced outside an organization ‐either in its tacit or explicit format‐ consists of previously unexploited amounts of knowledge from various external sources (Olivera, 2000). External knowledge cannot be put into valuable use for an organization if it is not implemented within the right context. For this to take place, start‐ups are required to possess experience and cognition similar to that of the new knowledge acquired, which is not always the case for novel business models which are launched for the first time in the local and/or international market place. Start‐ups may lack the ability to implement effectively and efficiently the new knowledge acquired from various sources, even when they have access to the necessary knowledge. In contrast, prior sector experience aids them to recognize the value of incoming knowledge, make the necessary modifications in order for the newly‐obtained knowledge to be implemented successfully where necessary, or, similarly, to institutionalize the utilization of the incoming knowledge. Consequently, knowledge costs minimization, to a great extent, depends on which knowledge motion is adopted by start‐ups to secure and access sources which will provide them with the necessary knowledge. The existing knowledge stock that a start‐up may maintain (mainly based on the personal knowledge possessed by the founders), is not alone adequate to reduce the possible inherent knowledge costs. Perhaps, prior knowledge and experiences may decrease, to an extent, the implementation costs. Although the existing knowledge embedded in various repositories is (or should be) available to almost the majority of corporation through everyone employed at them, its accurate identification and prompt as well as effective implementation within time constraints which secure to start‐ups the first‐mover advantage requires, among other things, direct and ongoing access to knowledge repositories along with the intervention of expert: in our case the partners in an alliance.
5. Limitations and future research The study experienced some limitations in regards to its nature as a conceptual study. Empirical research, both qualitative and quantitative, should follow to confirm the validity of our research propositions, thus generalizing the applicability of the proposed framework. In terms of the research propositions, our study supports a general framework for knowledge costs minimization without taking into consideration specific factors which may differently dominate in various sectors. Therefore, it is not clear whether our propositions for managing knowledge costs could equally support the efforts made by start ups in different sectors to manage knowledge costs. In addition, the linkages provided in the aforementioned framework may be modified, as different region conditions exist in every occasion. Equally, additional studies in different sectors need to be conducted to examine the functionality of the three proposed options of knowledge minimization. Also, each option should be further empirically tested thus contributing in the literature data of large scale analysis. In terms of the literature review, our study is based on a number of articles accessed through specific databases while using pre‐selected key words. Consequently, this may have increased the likelihood of not
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Evangelia Siachou and Dimitris Apostolidis taking into consideration journal articles and published research work in other electronic databases or print sources.
6. Concluding remarks Start‐ups are in great need of effectively managing knowledge costs so as to achieve successful implementation of innovative projects and gain first mover advantage. It is in this light that the EU’s knowledge management motions may provide a framework for the successful management of such costs when implemented by start‐ups. Derived from EU’s motion, the framework provided exemplifies three options available to a start‐up corporation in the need of acquiring knowledge outside its boundaries (i.e., various knowledge repositories, its external environment and/or alliances with knowledge keepers). We estimate that not all motions have the same value in managing knowledge costs. The formation of strategic alliances with organizations which possess the required knowledge seems to offer a significant decrease in all of the four types of knowledge costs. Without underestimating the other two knowledge motions, seeking knowledge in various sources outside a start‐up corporation does not always promise decrease in all types of knowledge costs. Even as, the external environment of start‐ups is estimated as a motion able to offer, to an extent, a decrease in transfer and implementation costs, other antecedents (e.g., trust, nature of relationships, etc) should also be taken into consideration because they directly affect the associated search and acquisition costs. it is not clear whether our propositions for managing knowledge costs could equally support the efforts made by start‐ups in different sectors to manage knowledge costs. As all links in the aforementioned framework may be modified, as different region conditions exist in every occasion additional studies in different sectors need to be conducted to examine the functionality of the three proposed options of knowledge minimization.
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Use and Acceptance of Learning Platforms Within Universities Boyka Simeonova1, Pavel Bogolyubov2 and Evgeny Blagov3 1 School of Management, Royal Holloway, University of London, London, UK 2 Dpt. of Management Learning and Leadership, Lancaster University Management School, UK 3 Dpt. of Information Technologies in Management, Graduate School of Management, St. Petersburg University, St. Petersburg, Russia Boyka.Simeonova.2010@live.rhul.ac.uk p.bogolyubov@lancaster.ac.uk blagove@gsom.pu.ru Abstract: Virtual Learning Environments (VLEs) are learning platforms within universities aiming to enhance students’ learning. In order to determine the success of VLE adoption in universities it is essential to identify the factors which influence the students’ acceptance and use of VLE systems and potentially to develop a theoretical model which can predict the influence of these factors on the students' learning activities. We are adopting the Unified Theory of Acceptance and Use of Technology (UTAUT) model developed by Venkatesh et al. (2003) to identify and test the underlying factors influencing VLE acceptance and use. UTAUT is a relatively new and untested model especially within cross‐cultural settings in the context of Higher Education (Straub, 2009). The adoption of UTAUT to explore the use and acceptance of technology – and particularly VLEs – in educational settings is somehow limited. We posit that this model is appropriate for this context and we position our research to fill these gaps. We are testing the model in three business schools in universities in two different countries. We are aiming to test the model within UK and Russian educational settings using factor analysis by deploying Venkatesh et al.’s (2003) questionnaire among students. If UTAUT has been used in various settings, its adoption appears to lack rigour in explaining the significance of the identified factors which can shape acceptance and use. The results would help enhancing and hopefully strengthening the theory by testing the factors’ loadings and their impact on VLE acceptance and use within educational settings to enhance knowledge creation, sharing, mapping and collaboration. The results demonstrate the validity of the UTAUT model in different cultural settings as well as the deeper cultural differences in the perceptions of use and acceptance of VLEs which highlight the crucial role of culture on technology adoption. Keywords: UTAUT, technology acceptance, virtual learning environments, higher education
1. Introduction The aim of this study is to test the validity of the original Venkatesh’s UTAUT model in cross‐cultural settings (Venkatesh et al. 2003). Few studies have attempted to test the model in other countries and most of them include US where the model was originally developed and tested (e.g. Venkatesh and Zhang 2010; Im et al. 2011; Nistor et al. 2010, 2011). Venkatesh and Zhang (2010) call for expanding the use of the model by primarily testing its validity and robustness in other cultural contexts. This paper aims to fill this gap and provide insights from two countries: the UK and Russia. This paper advances the recent findings of Blagov and Bogolyubov (2013). They tested the UTAUT model in three Russian organisations from various industries which implemented corporate Web 2.0 platforms. In this study we focus only on one industry – Higher Education – and we investigate the use and acceptance of VLE platforms in three business schools, two in the UK and one in Russia. Straub (2009) points out that UTAUT has not been extensively adopted, particularly not in educational settings to test the acceptance and use of VLEs. Virtual Learning Environments (VLEs) are digital platforms – often Web 2.0‐driven – which aim at enhancing interactions among students and between learners and teachers. These platforms are being given an increasingly prominent role in the teaching and learning process (Pituch and Lee 2006). The use of VLE provides access to a wealth of materials and allows interaction and knowledge sharing between learners and between students and teachers without the limitations of space and time (van Raaij and Schepers 2008). VLEs such as Moodle and Blackboard are tools for knowledge mapping where students are provided with the course contents, lesson plans, podcasts, etc as well as for knowledge sharing and creation by using wikis, forums, resources and repositories embedded in these systems. Eid and Nuhu (2011) identified that the use of IT technologies has a significant positive effect on knowledge sharing in Saudi universities. It is often argued that adoption of VLE technologies within universities would result in greater students’ creativity, problem solving
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Boyka Simeonova, Pavel Bogolyubov and Evgeny Blagov and improve their learning experiences (Wozney et al., 2006). In that respect the most widely adopted learning platforms are WebCT, Blackboard, or Moodle (Vrasidas, 2004). However, VLEs are not utilized and used by students to their full potential. It is recognised that user acceptance is one of the essential prerequisites for the development of knowledge management systems (Vitari et al 2007). It is therefore crucial to identify what determines technology adoption in Higher Education and particularly what shapes the use and acceptance of VLE systems such as Moodle and Blackboard. In the following section we present the UTAUT theoretical model with a discussion on its current state and robustness. The subsequent sections portray our study design, analysis and findings. Finally, we conclude with theoretical and practical remarks and provide future opportunities for research.
2. Theoretical framework UTAUT was developed by Venkatesh et al. (2003) as a unified theory after reviewing eight models – the theory of reasoned action (TRA), the technology acceptance model (TAM), the motivational model (MM), the theory of planned behaviour (TPB), the model of PC utilization (PCU), the innovation diffusion theory (IDT), and the social cognitive theory (SCT). The authors assessed the eight models with regards to their similarities, differences and abilities to explain individuals’ acceptance of new technologies. They compared them empirically in four US organisations and found that the existing models explained between 17% and 53% of the variance in the use and acceptance of technology. Based on these results they proposed a new unified model. The UTAUT model appears to present a fairly complete picture of technology acceptance and use as it includes direct determinants of behavioural intention and use behaviours such as performance expectancy, effort expectancy, social influence, facilitating conditions. They suggested four mediating factors impacting the use and acceptance of IT – gender, age, experience and voluntariness of use. Subsequently, the authors tested the newly founded model on the same four companies and found that the explanatory power of the new UTAUT model is 70% which exceeded sensibly the results obtained by the other eight models. The graphical representation of the UTAUT model is portrayed on Figure 1.
Figure 1: UTAUT Model (Venkatesh et al., 2003) The original model presented above has been cross‐culturally tested by Oshlyansky et al. (2007). They tested the theory with students from diverse faculties in the following nine countries: Czech Republic, Greece, India, Malaysia, New Zealand, Saudi Arabia, South Africa, the United Kingdom, and to the United States. They conducted PCA analysis on the data to explore and validate the factor loadings. Across the countries their analysis resulted in one ‘omnibus’ factor which contained all the constructs except Anxiety. Based on their findings it can be stated that the model is robust and applicable across diverse countries and cultures. However, we argue that this result is dubious and it questions the construct validity of their data as all the constructs load onto a single factor. On the other hand, Al‐Gahtani et al. (2007) investigated the applicability of UTAUT to a single country (Saudi Arabia) and concluded that culture is a significant factor for technology use and acceptance. Venkatesh and Zhang further tested the validity of the model and in a new study (Venkatesh and Zhang 2010) compared two countries: US and China. Their results revealed variations in the relationships across the
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Boyka Simeonova, Pavel Bogolyubov and Evgeny Blagov countries based on gender, age and experience as well as a significant difference on the effect of social influence. They concluded that the model has potential to be extended cross‐culturally. However, considering the scarcity of the empirical evidence, they called for further testing and validating of the theory so that it acquires robustness and generalizability power. In an attempt to answer this call and enhance the theory, Blagov and Bogolyubov (2013) tested the validity of UTAUT in three Russian companies across diverse industries – the software industry, Higher Education and the banking industry. They conducted Principal Component Analysis (PCA) and concluded that the model could be considered valid in the Russian context. However, their analysis revealed significantly different results from Oshlyansky et al. (2007) as their analysis resulted in five different factors as opposed to one ‘omnibus’ factor. As Blagov and Bogolyubov (2013) studied solely one country, we attempt to fill this gap and extend their study by comparing Russia and the UK and focusing on the use and acceptance of VLEs within Universities’ Business and Management Schools. Our research aims are: 1) to cross‐culturally validate the UTAUT model and 2) to test the model within Higher Education. In the following section the methodology adopted for the study is presented.
3. Methodology As evident from the literature review, there have been recent attempts to test and validate the theory cross‐ culturally and it has been contended by the originators of the theory that this is a potential shortcoming of the model that needs to be addressed by scholars. The question this research aims to answer is whether the original model developed in the US can be extended to other countries. This study investigates two countries in particular: the UK and Russia. We first test whether the results are significantly different from the original UTAUT model. Secondly, we compare the results between the UK and the Russian sample to identify any similarities and differences between these two countries. Subsequently, we try to explain any unexpected results and any emerging cultural differences in the use and acceptance of VLE technologies within universities. In order to answer these questions, data was collected from students in three Business and Management Schools; two in the UK and one in Russia. Before leveraging the survey, the construct items were adjusted and reworded to fit the technology in question. In our case we are investigating the use of Moodle and Blackboard as learning platforms, so the questions of the survey were adjusted to fit for purpose. Subsequently the construct “behavioural intention to use” was dropped from the model as we are testing the UTAUT model on already existing technologies as opposed to predicting the use of technology that has not been implemented yet. The English version of the adapted questionnaire is presented in Appendix A. The survey questions were back‐translated by two bilingual (English‐Russian) scholars. This way the authors ensured that the meaning was kept and the Russian version carried the same nuances as the original English version. Prior to leveraging the main survey we conducted a pilot study with 31 students from the Russian business school. The data showed a reliability results greater than 0.70 based on Cronbach’s Alpha measurement. The recommended threshold for acceptable reliability is a value of Cronbach’s Alpha greater than 0.70 (Nunnally, 1978). After the pilot study, the main survey was sent out to the students in the three business/management schools. The study has applied purposeful sampling technique as the study was targeting specifically enrolled students at universities where a VLE platform exists. However, the sample is representative and ‘fair’ as all students had an equal chance to participate in the survey. In total 169 completed questionnaires were returned. Subsequently, we tested the reliability of these 169 responses. The constructs show good reliability as the values of Cronbach’s Alpha vary between 0.754 and 0.936 which exceeds the recommended value of 0.70. The following table presents the reliability results for each construct: Table 1: Main study reliability results UTAUT Construct
Cronbach’s Alpha
Performance expectancy Effort expectancy Attitude toward using technology Social influence Facilitating conditions Self‐efficacy Anxiety
.907 .936 .914 .754 .844 .855 .867
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Boyka Simeonova, Pavel Bogolyubov and Evgeny Blagov We conducted PCA to identify the factor loadings and test the validity of the theory. We conducted the PCA using SPSS 21. Varimax rotation technique was applied. Factor loadings with eigenvalues of 0.1 or more are considered acceptable principal components, and factor loadings with magnitude 0.3 or more are considered statistically significant (Kline 2002). First, a PCA analysis was performed on the whole dataset followed by country‐by‐country PCA analysis. The results are shown in the following section.
4. Analysis and findings The demographic characteristics of the sample are the following: the UK dataset consists of 102 respondents, out of which 36 male, 66 female, and their average age is 21.29 years. The Russian dataset consists of 67 respondents; out of which 34 male, 32 female, and one respondent who didn’t specify her or his gender. The average age of the participants in the Russian dataset is 21.23 years. The results of the PCA after Varimax rotation based on the whole dataset (UK and Russia) are presented in Table 2. Each row corresponds to the respective question in the questionnaire. The statistically significant loadings are marked with the sign “*” in the table. Table 2: UK and Russia results of the PCA after the Varimax rotation
PE1 PE2 PE3 PE4 EE1 EE2 EE3 EE4 ATUT1 ATUT2 ATUT3 ATUT4 SI1 SI2 SI3 SI4 FC1 FC2 FC3 FC4 SE1 SE2 SE3 SE4 Anx1 Anx2 Anx3 Anx4
Rotated Component Matrixa Component 1 2 3 4 5 .597* .246 ‐.157 .550* .135 .742* .109 ‐.222 .314* .212 .756* .150 ‐.133 .298 .212 .650* .086 ‐.139 .442* .244 .413* .705* ‐.166 .112 .182 .251 .760* ‐.174 .247 .212 .260 .840* ‐.238 .146 .102 .234 .838* ‐.177 .235 .046 .624* .328* ‐.163 .412 .014 .846* .175 .011 ‐.031 .059 .814* .286 .058 ‐.059 .125 .834* .291 ‐.020 .042 .132 .388* ‐.011 ‐.150 .256 .195 .063 ‐.058 ‐.113 .090 .195 .246 .215 ‐.073 .757* .127 .167 .253 ‐.055 .787* .136 .094 .445* ‐.017 .537* .108 .055 .607* ‐.028 .460* .099 .231 .367* ‐.148 .212 .221 .328* .459* .052 .041 .113 .107 .573* ‐.075 .078 .455* .207 .263 .035 .184 .770* .180 .171 ‐.096 .100 .850* .179 .085 ‐.181 .093 .852* ‐.190 ‐.042 .657* .111 ‐.214 ‐.060 ‐.104 .872* ‐.146 ‐.036 ‐.039 ‐.133 .897* ‐.058 ‐.058 ‐.029 ‐.252 .839* ‐.115 .009 Extraction Method: Principal Component Analysis. Rotation Method: Varimax with Kaiser Normalization.
6 ‐.072 ‐.007 .106 .076 .073 .080 ‐.013 ‐.045 .109 .223 .286 .168 .642* .722* .225 .119 .474* .388* .448* .604* .168 .196 .190 .122 ‐.176 ‐.036 ‐.013 ‐.049
Note: PE ‐ Performance Expectancy, EE ‐ Effort Expectancy, ATUT ‐ Attitude toward using technology, SI ‐ Social Influence, FC ‐ Facilitating Conditions, SE ‐ Self‐efficacy, Anx – Anxiety Similarly to the results of the Blagov and Bogolyubov’s (2013) study and in contrast to the results of Oshlyansky et al. (2007), our results do not show one ‘omnibus’ factor. Our results show that the construct items load onto six different factors.
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Boyka Simeonova, Pavel Bogolyubov and Evgeny Blagov Factor 1 includes significant loadings of all the items of the constructs PE and ATUT. Three more construct items load on Factor 1: EE1, SI1 and FC4. This factor represents the perceived usefulness and fun of using the learning platform. Occasionally, but to a lesser extent the facilitating conditions and social influence could affect this perception when the students are externally influenced by others to use the system or when they require assistance in using it. Factor 2 comprises statistically significant loadings of all the items of the constructs EE and FC. Two more items load on Factor 2: ATUT1 and SE1. The existence of external support and facilitating conditions results in less effort required from the students to learn how to use the VLE platform and enhances their perception that using the VLE is a good idea. Factor 3 is representative solely of the Anxiety construct. What is interesting is that the whole construct loads with positive sign. This could be interpreted in a way that students with high levels of anxiety towards using the platform may put more effort into using it (Blagov and Bogolyubov, 2013). This could be due to students’ intrinsic motivation to do well in their studies where despite their high levels of frustration in using the platform they invest extra time to interact with it. Other possible explanation could be due to external pressure from peers or colleagues to use the system which could lead to higher levels of frustration and anxiety but still resulting in high interaction levels with the platform. Factor 4 is a collection of items: PE1, PE2, PE4, SI3, SI4, FC2 and FC3. This factor relates to the students’ perception of the value of Moodle/Blackboard for their studies. However as opposed to Factor 1 where it was related to fun, this factor describes the strong external support that needs to be provided to students to help them see it as a useful tool. Factor 5 represents the whole SE construct and it clearly accounts for the self‐efficacy of the platform. Factor 6 is a mixture of the whole FC construct and two SI items (SI1 and SI2). The whole FC factor scores higher loadings on Factor 2 where it is closely related with the ease of use of the VLEs. Factor 6 implies that students are externally motivated to use the system by classmates and other influential people and despite the recognised value of the facilitating conditions, they are using the platform because they are expected to. The mixed UK and Russian data results in 6 Factors. Each construct loads significantly on one or two factors. The only construct which does not load on a single factor is SI. SI’s highest loadings are distributed between two factors Factor 4 and Factor 6. Subsequently, we conducted a country‐by‐country analysis to test the cross‐cultural validity of the model and investigate any similarities and differences between the UK and Russia. Table 3 presents the results of the PCA based on the UK dataset. Table 3: UK results of the PCA after the Varimax rotation PE1 PE2 PE3 PE4 EE1 EE2 EE3 EE4 ATUT1 ATUT2 ATUT3 ATUT4 SI1 SI2 SI3
1 .781* .825* .781* .752* .333* .206 .159 .189 .639* .596* .543* .664* .246 ‐.100 .302*
2 .224 .221 .121 .055 .824* .811* .865* .761* .223 .017 .119 .174 ‐.040 .031 .127
Rotated Component Matrixa Component 3 4 .294 ‐.089 .046 ‐.071 .130 .004 .224 .016 .088 ‐.058 .275 ‐.125 .214 ‐.173 .357* ‐.251 .391* ‐.220 .000 .178 ‐.017 .123 .022 .029 .195 ‐.003 .061 .014 .748* .015
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5 .084 .100 .061 .115 .102 .178 .055 .048 .122 .029 .096 .180 .158 .046 .105
6 ‐.069 .052 .190 .050 .136 ‐.042 .141 .144 .293 .629* .717* .563* .095 ‐.003 ‐.026
7 .033 .105 ‐.030 .104 .116 .079 ‐.080 ‐.139 ‐.045 ‐.020 ‐.029 ‐.074 .766* .833* .200
Boyka Simeonova, Pavel Bogolyubov and Evgeny Blagov
SI4 FC1 FC2 FC3 FC4 SE1 SE2 SE3 SE4 Anx1 Anx2 Anx3 Anx4
.311* .081 .041 .230 ‐.020 .036 .112 .138 .117 ‐.037 ‐.124 .001 .073
Rotated Component Matrixa Component .138 .780* ‐.009 .095 .242 .814* ‐.062 .145 .365* .764* ‐.106 .122 .192 .403* ‐.177 .302* .327* .336* .009 ‐.018 .536* .154 ‐.080 .416* .125 .258 .089 .820* .154 .102 ‐.018 .852* .064 .027 ‐.038 .892* .123 .115 .717* ‐.186 ‐.197 ‐.157 .820* .060 ‐.269 ‐.034 .862* .055 ‐.166 ‐.121 .878* .069 Extraction Method: Principal Component Analysis. Rotation Method: Varimax with Kaiser Normalization.
‐.106 .181 .168 .274 .663* .140 .051 ‐.002 .039 .030 .062 .040 .004
.029 .125 ‐.012 .213 .252 .042 ‐.014 .120 .089 .005 ‐.038 .002 .034
Interestingly, the first obvious difference is that the UK dataset resulted in 7 factors while the combined UK/Russia dataset had only 6 factors. Secondly, the constructs item loadings are more dispersed than in the combined results. This could be due to underlying cultural differences. Similarly to the results in the combined dataset, Factor 1 includes all items of the PE and ATUT construct. There are three more items loading on Factor 1: EE1, SI3 and SI4. This reveals that in the UK Universities there is a strong external support from teachers and the college who enhance the perception of usefulness and fun of using the VLE platform. Factor 2 accounts for the whole EE construct, FC2, FC4 and SE1. This factor shows that if facilitating conditions are present students find the use and the interaction of the system easier. Factor 3 represents the whole FC construct, EE4, ATUT1, SI3 and SI4. It can be interpreted in a way that the formation of positive attitude towards Moodle/Blackboard highly depends on the available facilitating conditions as well as strong support from the teachers and the college. Factor 4 accounts solely for the Anxiety construct and all items have positive loadings as in the Factor 3 of the combined dataset. It could be particularly relevant that in UK Universities higher levels of anxiety could lead to higher efforts for using the system. A possible explanation could be the lower level of uncertainty avoidance in the UK culture according to Hofstede’s cultural dimensions model (Hofstede and Bond 1988). Factor 5 of the UK dataset similarly to Factor 5 of the combined dataset represents the whole SE construct but it has an additional item FC3 thus showing the perceived self‐efficacy of the UK students in relation to the compatibility of the VLE with other systems used by the students. Factor 6 is a mixture of ATUT2, ATUT3, ATUT4, and FC4. Similarly to the Factor 1 it demonstrates positive attitude towards using the VLE platform if there is external help that students can rely on if facing difficulties with the system. Factor 7 consists only of SI1 and SI2 showing the positive influence of the social pressure on the UK students’ effort to use the VLEs. Finally, Table 4 presents the results from the Russian dataset. The first apparent point is that the Russian dataset resulted in 7 factors, same as the UK dataset result. Secondly, the Russian results appear ‘noisier’ which can be interpreted as a result of cultural differences and perceptions in the use and acceptance of VLEs between the Anglo‐Saxon cultures, to which both the UK and the US (which is “native” for the UTAUT model) cultures belong, and the Russian culture. Factor 1 is quite untidy in comparison to the UK Factor 1 and it includes diverse items from many constructs. These are all items of PE and ATUT constructs, EE1, EE2, EE3, SI1, SI3, FC1 and Anx1. The factor could be
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Boyka Simeonova, Pavel Bogolyubov and Evgeny Blagov interpreted in a way that providing facilitating conditions together with support from teachers and a little push from influential people results in students forming positive attitude towards using the VLE platform and being less apprehensive about interacting with it. Table 4: Russia results of the PCA after the Varimax rotation Rotated Component Matrixa
PE1
1 .329*
2 .703*
3 .454*
Component 4 .103
5 .095
6 .096
7 .020
PE2
.607*
.544*
.022
.206
.399*
.096
‐.094
PE3
.551*
.604*
.098
.215
.350*
.184
‐.007
PE4
.357*
.670*
.254
.232
.316*
.072
.161
EE1
.452*
.203
.622*
.155
.282
.167
.018
EE2
.392*
.302*
.770*
.115
.148
.102
.087
EE3
.314*
.260
.754*
.226
.179
.224
‐.060
EE4
.224
.224
.834*
.016
.017
.072
‐.201
ATUT1
.704*
.434
.135
.077
‐.082
.155
.027
ATUT2
.822*
.071
.332*
.279
.131
.046
.106
ATUT3
.805*
.113
.393*
.159
.128
.064
.198
ATUT4
.799*
.160
.284
.197
.028
.256
.083
SI1
.629*
.331*
.061
.289
.068
.337*
.379*
SI2
.231
.122
‐.061
.187
.202
.090
.795*
SI3
.370*
.763*
.159
.105
.123
.170
.020
SI4
‐.088
.797*
.288
.141
.062
.099
.182
FC1
.415*
.206
.114
‐.152
.217
.605*
.315
FC2
.201
.114
.431
‐.113
.097
.649*
.284
FC3
.050
.023
.198
.425*
.084
.731*
.022
FC4
.170
.181
.070
.089
.168
.821*
‐.094
SE1
‐.071
.103
.602*
.048
.312*
.328*
.319*
SE2
.058
.199
.241
.219
.805*
.189
‐.082
SE3
.131
.061
.112
.198
.878*
.098
.178
SE4
.071
.170
.131
.233
.810*
.134
.197
Anx1
‐.430*
‐.007
‐.120
‐.642*
‐.227
‐.273
.164
Anx2
‐.172
‐.169
‐.027
‐.857*
‐.255
‐.025
‐.210
Anx3
‐.201
‐.210
‐.078
‐.799*
‐.294
.073
‐.303*
Anx4
‐.234
‐.360*
‐.277
‐.661*
‐.171
‐.256
.143
Extraction Method: Principal Component Analysis. Rotation Method: Varimax with Kaiser Normalization.
Factor 2 includes the whole PE construct, EE2, SI1, SI3, SI4 and Anx 4. A possible interpretation for this factor could be that Russian students require more social support from the college, the teachers and other influencing people in order for them to overcome the feeling of the system being intimidating and start perceiving the platform as an useful and easy to use tool aiming to enhance their learning. Factor 3 represents the whole EE construct with additional items: PE1, ATUT2, ATUT3 and SE1. This factor relates the perception of the system being useful and fun to the ease of using it.
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Boyka Simeonova, Pavel Bogolyubov and Evgeny Blagov The whole Anxiety construct loads on Factor 4, but interestingly all loadings are negative which is contrary to the UK results. This could mean that students in Russian Universities are less willing to interact with the system if they feel uncomfortable with it and experience higher levels of anxiety. This could be due to students unwilling to make an extra effort if they find the system difficult or not so useful. Factor 5 accounts for the whole SE construct and three items from construct PE: PE2, PE3 and PE4. This could mean that students may not be self‐motivated to use the system unless they find direct usefulness of adopting it, which interestingly seems different from the UK students who demonstrate significant positive loadings for the SE factor without significant linkage with the performance expectancy. A possible interpretation for this could be that Russian students seem to be more practice‐oriented in their motivation to use systems. A viable Hofstede‐based explanation most probably could be related to the uncertainty avoidance factor which would need further testing. Factor 6 incorporates the whole FC construct with only two additional items SI1 and SE1 which makes the results a little controversial, thus requiring further analysis to investigate the variations within the data. Some students find the supporting conditions encouraging which results in them using the system independently while others use it because they are forced by external factors and people despite the availability of facilitating conditions. Factor 7 similarly to the UK results accounts for SI1 and SI2. It has additional loadings from SE1 and Anx3. As with the UK results this factor describes the external pressure to use the system. However, it appears that the students in Russia need this extra social push and reassurance to overcome their hesitation to use Moodle/Blackboard.
5. Conclusion This paper contributes to the theory and practice in several ways. Firstly, we tested the UTAUT model cross‐ culturally in two countries: the UK and Russia. Secondly, we tested the model within educational settings. Thirdly, the study revealed the cultural similarities and differences in the use and adoption of VLEs between the students from UK and Russian universities. From the presented findings we can clearly conclude that the UTAUT model is valid in cross‐cultural settings. Interestingly, the combined UK and Russian dataset fits the model best with least noise and dispersed item loadings. The more in‐depth country‐by‐country analysis shows that there are cultural differences in the use and acceptance of VLEs. The effect of social influence and anxiety is different in the two countries, indicating the culture‐specific nature of the use and acceptance of technology. The students in the UK Universities experience higher levels of anxiety but the effect of this anxiety stimulates them to make an extra effort to use the system in order to do well in their studies. That could be because UK students rely less on external influence and motivation and the value of using the system appears to be well built in the culture of the UK universities. The students in the Russian university experience less anxiety in general, but when anxiety is present this reduces their interaction with the system and prevents them from perceiving it as useful. A reason for this could be the students’ lower intrinsic motivation to interact with the system, or low level of integration of the value of using the system in the culture of this university, or Russian universities in general. Interestingly, the results from the three Russian companies described in Blagov and Bogolyubov (2013) revealed a positive relationship between anxiety levels and effort to use the system, similarly to the results from the UK university presented in this paper. In that respect, the second explanation seems to be more adequate here, that the negative influence of anxiety experienced by students in the Russian university is due to specific traits of the corporate culture of this university, or the universities in Russia in general. This study provides a recommendation to carefully control the level of the students’ anxiety at universities, even more at Russian universities as the negative effect of anxiety on effort could be a result of higher level of uncertainty avoidance in Russian culture (The Hofstede Centre, 2013). Thus, universities in Russia should make greater use of VLE platforms, which are valuable tools for collaboration, sharing materials, opinions and knowledge, communication with peers and group mates, participating in forums and helping others. The peers and teachers need to support the students if they have difficulties using the platform so that students’ anxiety levels do not obstruct them from using the system. A clear vision of the positive features and usefulness of using the VLEs should be conveyed within universities and deeply built in their culture as the result have
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Boyka Simeonova, Pavel Bogolyubov and Evgeny Blagov clearly shown that students in Russian universities follow a more practice‐based approach in using the system, meaning that they are motivated to use the system only if they find it directly useful. Conversely, the UK students put more effort into interacting with the system even if the benefits are not directly observable. However, UK students experience much higher anxiety levels. Even though higher levels of students’ anxiety lead to greater effort to use the system, this issue should be addressed as anxiety in general is a distressing factor that can lead to demotivation of users even in the cultures with lower levels of uncertainty avoidance. Thus, the UK universities should provide greater support and enhance students’ awareness of possibility to get support if facing difficulties in using the VLEs. Further research should expand the study to include more countries and test UTAUT as well as the use and acceptance of VLEs. Subsequently, further investigation is needed to conduct deeper analysis of the most controversial factors of social influence and anxiety. Anxiety specifically loads positively on the UK and the UK/Russia datasets and negatively on the Russian dataset. It is worth exploring further what causes such anxiety levels in different contexts. As for the theoretical scope and practical methodology of future research, analysing the influence of cultural differences using Hofstede’s cultural dimensions model seems potentially fruitful and it would be worth extending the original Venkatesh et al. (2003) questionnaire to incorporate items on culture.
Appendix A The UTAUT questionnaire leveraged to both countries is presented below: Performance Expectancy: How useful do you think Moodle/Blackboard is? PE1 I would find Moodle/Blackboard useful for my studies PE2 Using Moodle/Blackboard enables me to accomplish tasks more quickly PE3 Using Moodle/Blackboard increases my productivity PE4 If I use Moodle/Blackboard, I will increase my chances of successfully completing the course Effort Expectancy: How much effort does it take? EE1 My interaction with Moodle/Blackboard would be clear and understandable EE2 It would be easy for me to become skilful at using Moodle/Blackboard EE3 I would find Moodle/Blackboard easy to use EE4 Learning to operate Moodle/Blackboard is easy for me Attitude toward using technology: Is it enjoyable? ATUT1 Using Moodle/Blackboard is a good idea ATUT2 Moodle/Blackboard makes work more interesting ATUT3 Working with Moodle/Blackboard is fun ATUT4 I like working with Moodle/Blackboard Social Influence: What do your social surroundings think about Moodle/Blackboard? SI1 People who influence my behaviour think I should use Moodle/Blackboard SI2 I use it because most of my classmates do SI3 The teachers are supporting the use of Moodle/Blackboard SI4 In general, the University supports the use of Moodle/Blackboard Facilitating Conditions: Do you have everything you need to use Moodle/Blackboard? FC1 I have the resources necessary to use Moodle/Blackboard FC2 I have the knowledge necessary to use Moodle/Blackboard FC3 Moodle/Blackboard is compatible with other systems I use FC4 A specific person (or group) is available for assistance with Moodle/Blackboard difficulties Self‐efficacy: I could complete a job or task using Moodle/Blackboard… SE1 If there was no one around to tell me what to do as I go SE2 If I could call someone for help if I got stuck SE3 If I had a lot of time to complete the job for which the software was provided SE4 If I had just the built‐in help facility for assistance
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Boyka Simeonova, Pavel Bogolyubov and Evgeny Blagov Anxiety Are there any concerns? Anx1 I feel apprehensive about using Moodle/Blackboard Anx2 It scares me to think that I could lose a lot of information using Moodle/Blackboard by hitting the wrong key Anx3 I hesitate to use Moodle/Blackboard for fear of making mistakes Anx4 Moodle/Blackboard is somewhat intimidating to me
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The Relationship between Knowledge Management and Employees' Empowerment in Justice Administration of Tehran Province Faezeh Sohrabi1, Alireza Chenari1, Fattah Nazem1, Mohamad Farahzadi2 and Masoumeh Bahmanabadi3, 1 Department of Education, Roudehen Branch, Islamic Azad University, Roudehen, Iran 2 Department of Management, Firozkoh Branch, Islamic Azad University, Firozkoh, Iran 3 Department of Education, Roudehen Branch, Islamic Azad University, Roudehen, Iran faezehsoh@yahoo.com a.chenari@yahoo.com nazem@riau.ac.ir Mohamad.farahzadi@yahoo.com masoumeh.bahmanabadi@yahoo.com
Abstract: The main goal of this research is to analyze the relationship between knowledge management and empowerment in Justice Administration of Tehran Province. The research population consisted of all the staff at Justice Administration of Tehran Province. 351 were selected as members of the sample using Krejci-Morgan Table. The evaluation tools were Sallis and Jones's (2002) Knowledge Management and Spreitzer's (1995) empowerment scale. The Cronbach alpha coefficient in the selected sample of Justice Administration of Tehran Province was 0.97 for the knowledge management and 0.91 for the empowerment. The results of analysis of variance indicated that there is a relationship between knowledge management and the staff empowerment in Justice Administration of Tehran Province. There is a meaningful relationship between dimensions of knowledge management with empowerment that are leadership and management, teamwork and learning communities, collaborative knowledge, knowledge creating, digital sophistication, visions and missions, strategy, organizational culture, intellectual capital, learning organization. There is no meaningful relationship between other dimensions with empowerment including knowledge sharing, knowledge creating, visions and missions, strategy, intellectual capital, and learning organization Keywords: Knowledge management, empowerment, justice administration, Spreitzer , Sallis and Jones
1. Introduction The organization's managers encounter with several challenges in such a stressful business environment today. Technological changes and innovation in product, on the one hand, and complexity of organization management, on the other hand, are the only factors in focusing on main capitals, i.e. manpower. Accordingly, the importance of employees is increasingly known. On this basis, the managers sought the mechanisms to empower the individuals. Empowerment is a method to create contribution among the staff through engagement and granting them the responsibility. By this way, the staff would be encouraged to decide about their tasks themselves and be more skillful in doing them (Mirzaei Daryani, 2008). Empowerment makes a balance between the power of manager and freedom in the staff's action. According to many researches, the organizations need the staff's empowerment process because it increases the ability in decision-making, more self-competency and job commitment and better performance (Hausband & Short, 1994, as cited in Arabian, 2004). Knowledge management means any attempts to recognize hidden capital in individuals' mind and change it to organizational capital in such a way that many people engaging in organizational decision-making can benefit from it (Prusake & Davenport, 2005). In fact, the knowledge is a main resource in economic and industrial development in modern economy; other traditional factors of production such as land, labor force and capital are secondly important. Regarding the implicit importance of each variable, analyzing the relationship between empowerment variables and knowledge management is of the special importance. This analysis makes the managers pay more attention to the staff and try more in empowering them which is the best way for organization progression and higher performance because the Justice Administration is among the fundamental bodies in every country. This is because of its role to guarantee the social and individual rights, realizing peace, justice and safety. Therefore, it seems vital to consider the human resource potentials and to actualize those
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Faezeh Sohrabi, Alireza Chenari, Fattah Nazem et al potentials and invaluable talents which can itself leads to the individual's development and adaptation with the objectives of the organizations. One of the most important ways to achieve organizational goals is to manage these valuable resources well and effectively. To do so, some factors affecting the staff empowerment are information, knowledge, and job skill (Bowen & Lawler, 1992). Individuals' empowering would, accordingly, be possible by gathering information and managing it. Finally, it is possible to improve organizational performance by knowledge management and making the organization have a more brilliant performance (Weick, 1993). There are few scientific studies to analyze the concept of knowledge management on the staff empowerment although many authors and researchers have dealt with the key role of knowledge management and empowering the manpower. Therefore, in this paper we seek to analyze the question which reads: is there any relationship between knowledge management and the staff empowerment. To provide the answer, first we considered the theoretical principles in empowerment and knowledge management and then we analyzed the relationship between these two. Although organizational theorists defined empowerment differently, there are two general definitions in the literature:
2. Empowerment as power sharing Many theorists considered the concept of empowerment synonymous with the staff contribution at work. Staff contribution is a process in which the power would be shared among the individuals (Lawler, 1994). According to the theorists, empowerment may be defined as granting more organizational power to the staff (Raymond, 2003; Pearce, Sims, Cox, Ball, Schnell, Smith, and Trevino, 2003).
3. Empowerment as a motivational and cognitive concept In this approach, the empowerment is defined as motivational factor. According to the Conger and Kanungo's model (1989), empowerment is a conceptual motivational model including two feelings of self-competence and self-effectiveness. In Thomas and Velthouse model (1990), empowerment is defined as four dimensions of competence, self-determination, meaningfulness, and impact. Spreitzer (1992) defined empowerment as a conceptual motivational model including competence, self-determination, meaningfulness, and impact. These dimensions provide a personal orientation towards the role of employee in the organization. Mishra (1992) then added "trust" to the dimensions above and totally formed five psychological dimensions of empowerment. According to Thomas and Velthouse, the high levels of four dimensions of empowerment combined with each other to create high levels of energy, innovation, flexibility and stable job behaviors. Since the motivational cognitive approach does not have the limitations of power sharing approach and, instead, has a few distinctive merits, it can be the basis of the theoretical framework of the present study. Among the different motivational cognitive approach models, the Mishra model is more complete and includes all other models so it is the basis of this research. The empowerment dimensions are explained based on the above model as follows:
3.1 A) Self-determination Self-determination is the degree of influence that an individual has on how to perform the job. In other words, it is the self-determination of person in starting and continuing job behaviors and processes and making decision on methods, activities and attempts necessary to perform the job. The empowered employees feel responsibility and ownership in their tasks (Rapoprt, Swift & Hess, 1984; Zimerman, 1990).
3.2 B) Competence By empowering, the individuals feel competence i.e. they feel being capable or expert enough to perform successfully. The empowered individuals feel as qualified ones who are sure enough to work efficiently (Amichai et al., 2008, p. 39). They grow and learn to overcome the new challenges through the feeling of personal superiority (Spreitzer & Doneson, 2005, p. 38).
3.3 C) Impact This means the limit in which the individuals are able to control and use the strategic, bureaucratic or operational consequences in their tasks (Sacedakt, 2006).
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Faezeh Sohrabi, Alireza Chenari, Fattah Nazem et al
3.4 D) Meaningfulness Job meaningfulness is an opportunity for the individuals to feel that they seek important and valuable job goals (Garsliet, al., 2004, p. 12).
3.5 E) Trust in other colleagues The empowered individuals are trustful towards their colleagues. They are sure that they are behaved fairly and equally and are certain that equity is their final activity result, even if they are of the subordinates (Gomez, 2001). Knowledge management is a new valuable achievement aligning with other business and competitive strategies. That is why the organizations decide to execute the programs in knowledge management to benefit from its potential advantages. Improving impact and efficiency in organizations needs providing various conditions – human resource is the most important of all. The empowered manpower is the most effective factor in carrying out the duties and achieving the organizational goals; undoubtedly, this is the root of knowledge management. Therefore, it is not only important for the large organizations and companies to execute the knowledge management, but also it is vital for such an organization like the Department of Justice. Justice administration is an organization that needs competent and knowledgeable employees; since it has a direct contact with the society, it needs efficient methods in providing services to improve quality, to guarantee the social individual rights, realization of peace, justice and safety. These are all possible in shadow of using modern methods in information management and specifying suitable time for knowledge management. Belanet believed that the knowledge management is a process by which the organizations use their gathered information. Malhotra defines the knowledge management as a process by which the organizations can learn (internalizing the knowledge), code the knowledge (externalizing the knowledge), distribute and transfer knowledge. Carl Wig (2002) believed that knowledge management means creating necessary processes to recognize the data, information and knowledge needed for the organization that is gained through the internal and external environment and changing them to decisions and actions of organization and the individuals (Abtahi & Salavati, 1385, pp. 33-34). Danport and Prusack (1998) described the knowledge management as a process of gathering, distributing and using knowledge resources in all organization effectively. Based on the Bourox definition from knowledge management, affirmed by the US Center for Quality and Productivity Improvement, it is concerned with some techniques and processes to create, determine, occupy, organize and manage vital skills, information and knowledge to empower the individuals well to achieve organization mission. Managers, serving the role of knowledge management and information, can gain knowledge and transform the implicit knowledge of the employees to the clear contact massages; this implicit knowledge rooted in personal experiences and beliefs of the individuals. This would be possible through providing opportunities for group activities and changing the implicit knowledge to the explicit one (Jaffarimoghadam, 1382, p.11). By knowledge management in this research, we mean the adaptability of a given organization, according to the view presented by Sallis and Jones (2002), with the components of knowledge management which include leadership and management, teamwork and learning communities, collaborative knowledge, creating knowledge, digital sophistication, visions and missions, strategy, organizational culture, intellectual capital, and learning organization. Vision refers to the perspectives in a knowledge-based organization and sharing it with other shareholders and mission is the creator of knowledge and delineates to the practical strategies.
Strategy: it refers to developing modeled scenarios and applying it in management.
Organizational Culture: it refers to the various dimensions of culture including creating, focusing, sharing and recognizing organizational culture as a key capability.
Intellectual Capital: it includes recognizing the value of intellectual assets and compiling implicit knowledge of that.
The Learning Organization: it is an organization where learning is constant, the skill is defined to provide new knowledge, the EQ is diagnosed and the outcomes are encouraging creative thought, promoting practical learning for both individual and team.
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Leadership and Management: it refers to the ability to support the organizations' management and creating leaders and knowledge managers with appropriate leadership strategies and developing strategies to train middle managers.
Teamwork and Learning Communities: Under teamwork and learning communities, organization should encourage learning communities and knowledge teams, establish trust, and recognize the need for intellectual autonomy.
Collaborative Knowledge: it means the organization must be able to gather and collaborate new information, record the events of big organizations, and recognize the new competitors.
Knowledge Creating: The organization needs new knowledge and the experts who can change it to the service.
Digital Sophistication: Under digital sophistication, organizations are to develop technology among the staff through a clear technologic architecture and devising common virtual systems among them.
According to Drucker, empowering educated employees affects the organization's development. In Along the same line, in a research named "the impact of knowledge management in empowerment and constant development of human resources" conducted by Memarzadeh, Mozafari, and Bagheri (1387), they evaluated knowledge management hierarchy of descriptive and prescriptive models and different models of empowering human resources and role of knowledge management in empowering human resources; then, they analyzed the role of knowledge management in empowering human resources at ISACO company by individual, group and strategic questionnaires and found out that empowerment and constant development of human resources needs sharing and distributing information and knowledge for the employees in organizations. Bowen & Lawler (1992), in another research, considered empowerment as a strategy in sharing the staff more in power. In empowerment model presented by Bowen & Lawler, information access has an important role in decision-making. They believed that the companies could distribute power, information, knowledge and rewards in the organization by empowerment. Employees' empowerment is of the most important in the contingency model of Bowen and Lawler. They know the following factors effective in empowerment:
Organizational strategy (one of the knowledge management dimensions)
Link with customers
Complicated technology
Environmental changes
Employee's high need for growth
Jaffe and Scott (1992) indicated that a successful empowerment requires a change in individuals' attitude and team relations in organizational structure (of knowledge management' dimensions). They believed that it is impossible to gain the empowerment ignoring these factors. Kappelman and Richards (1996) discussed that empowerment, like other changes, requires obeying increasing and systematic strategy and executing it. In their opinion, teaching's role is undeniable in successful process of empowerment. Another researcher called Burdett (1991) asserts three factors in successful empowerment: 1) training-based management 2) wide-scope jobs 3) creating a learning organization (a dimension of knowledge management). Lashley (1999), focusing on empowerment dimensions, regarded the following factors effective in empowerment:
Management attitude (management awareness in granting authorities),
Task structure,
Organizational culture (a dimension of knowledge management).
He believed that different combinations of these factors would represent various forms of empowerment in organizations. Philamon (2004), in his research, concluded that the following variables affect empowerment:
Satisfy the employees' need,
Relations among the individuals,
Supervisor's, colleagues', and organization's support,
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Faezeh Sohrabi, Alireza Chenari, Fattah Nazem et al ď&#x201A;§
The staff's beliefs,
ď&#x201A;§
Sense of belonging.
He evaluated the staff empowerment in three dimensions of job meaningfulness, competence, and impact.
4. Objectives of the Research The objective of this study is to analyze the relationship between knowledge management and the staff empowerment.
5. Methodology The population included the staff of Justice Administration of Tehran Province. The Krejci-Morgan's Table was used to estimate the sample size. The least sample size was 351 to all of whom questionnaires were distributed. Stratifies and cluster random sampling methods were used to select the members of the sample group.
6. The Research Instruments The research tool was Spreitzer's Empowerment Scales was used. It was developed using empowerment model of Thomas and Volthouse (1999). It included twenty items in Likert scale and measures the empowerment dimensions including competence, self- determination , impact, job meaningfulness, and trust among the colleagues. The Sallis and Jones questionnaire (2002) was also used to measure knowledge management. This one included forty items in Likert scale and measures dimensions of knowledge management including leadership and management, teamwork and learning communities, collaborative knowledge, knowledge creating, digital sophistication, visions and missions, strategy, organizational culture, intellectual capital, learning organization. The Cronbach alpha coefficient in the selected sample of Justice Administration of Tehran Province was 0.97 for the knowledge management and 0.91 for the empowerment. The index showed internal homogeneity.
7. Findings The demographic data of the participants in this study are shown in Tables 1 and 2. Table 1: Demographic data of the staff Gender
Employment
Education
Age
Job Position
Demographic Data Male Female
Frequency 131 97
Percentage 57.5 42.5
(1-8 years) (9-16 years) (17-23 years)
47 97 32
22.3 46 15.2
(24 years and above)
35
16.6
(High School Diploma& Associates) (B.A.) (M.A. & Ph.D.) (26 - 30) (31 - 35) (Older than 36) Judge Clerk Others
44 124 44 16 22 169 73 116 25
20.8 58.5 20.8 7.7 10.6 81.6 34.1 54.2 11.7
Frequency 199 13 18 54 32
Percentage 93.9 6.1 9.3 27.8 16.5
(24 years and above)
90
46.6
(High School Diploma& Associates)
7
3.3
Table 2: Demographic data of the managers Gender
Employment
Education
Demographic Data Male Female (1-8 years) (9-16 years) (17-23 years)
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Faezeh Sohrabi, Alireza Chenari, Fattah Nazem et al (B.A.) (M.A. & Ph.D.) (26 - 30) (31 - 35) (Older than 36) Others
Age
82 120 16 22 169 25
39.2 57.4 7.7 10.6 81.6 11.7
The indices for the measures of central tendency and variability of empowerment variable and its dimensions are shown in Table 3. Table 3: Indices for the measures of central tendency and variability of empowerment and its dimensions Trust among Colleagues Mean Median Mode SD Range Min. Max. Total
Meaningfulness
impact
choice
competence
Empowerment
16.80 17 16 2.249 9 11 20 3914
14.19 15 16 3.268 16. 4 20 3334
13.74 14 16 3.7 16 4 20 3228
16.69 17 17 2.498 11 9 20 3922
74.94 79 80 12.282 53 45 98 17460
13.62 14 16.00 3.314 16 4 20 3200
The analysis of the data on Table 3 indicated that distribution of the participants' scores in empowerment questionnaire and its 6 dimensions tend to be a normal distribution. Table 4: Indices for the measures of central tendency and variability of knowledge management Vision and mission
Digital sophistication
Teamwork and learning communities
Leadership & management
KM
Strategy
Sharing
Organizational culture
9.6 10 4 4.057 16 4 20
13.07 13 10 4.966 20 5 25
9.87 10 4 3.802 16 4 20
8.14 9 9 3.052 12 3 15
10.84 10 9 3.944 16 4 20
7.9 8 9 3.134 12 3 15
8.32 8 8 2.952 12 3 15
11.43 12 12 3.822 16 4 20
18.04 18 18 5.119 24 6 30
111.98 112 89 34.646 164 46 210
Total
3492
2255
3071
2319
1914
2547
1857
1934
2687
4239
26315
Creation
Intellectual capital
14.86 15 6 5.858 24 6 30
Learning organization Mean Median Mode SD Range Min. Max.
The data in Table 4 on the knowledge management and its dimensions show that the variable, on the whole, does not tend to be distributed normally. This is in spite of the fact that few dimensions of knowledge management are distributed normally. Research major question: Is there any relationship between knowledge management and employees' empowerment in Justice Administration of Tehran Province? Table 5: Analysis of Variance Sum of Model
Mean df
Squares 1
Regression Residual Total
8343.643 26652.391 34996/034
F
Sig.
72/316
.000
Square 1 231 232
657
8343/643 115/378
Faezeh Sohrabi, Alireza Chenari, Fattah Nazem et al With the f value of 72 and the level of significance lower than 0.01, the table indicates that the regression model predicts the outcome variable significantly well. Table 6: Coefficients of independent variables Model 1
Unstandardized Coefficients B 55.583 0.173
Constant KM
Std. Error 2.382 0.02
Standardized Coefficient Beta 0.488
t 23.334 8.504
Sig. .000 .000
The correlation index between knowledge management and empowerment is 0.48 with the t value of 0.23. This means that 23% of the variation in empowerment (dependent variable) is explained by knowledge management (independent variable). Is there any relationship between dimensions of knowledge management and empowerment among employees in Justice Administration of Tehran Province? Table 7:Analysis of Variance Model 1 Regression Residual Total
Sum of Squares 13089.465 21906.569 34996.034
df 10 222 232
Mean Square 1308.947 98.678
F 72/316
Sig. .000
With the F value of 13.26 and the level of significance lower than 0.01, the table indicates that the regression model predicts the outcome variable significantly well. Table 8: Coefficients of independent variables Model Constant Leader ship & Management Teamwork and learning communities Sharing Knowledge Knowledge creation Digital sophistication Vision and mission Strategy Organizational culture Intellectual capital Learner organization
Unstandardized Coefficients B 49.050
Std. Error 20.547
0.569
0.203
0.794 0.779 0.512 1.091 - 0.126 -0.355 1.068 -0.858 -0.274
0.328 0.439 0.394 0.311 0.437 0.465 0.305 .0470 0.320
1
Standardized Coefficient Beta
t 19.260
Sig. .000
0.237
2.802
.006
0.248 - 0.188 0.131 0.351 - 0.031 - .110 .0433 - 0.283 -0.130
2.419 - 1.774 1.302 3.508 - 0.287 - 0.762 3.498 -1.827 -0.858
0.016 0.077 0.194 0.001 0.774 0.447 0.001 0.069 0.392
The correlation index between knowledge management dimensions and empowerment is 0.61 with the t value of 0. 37. This means that 37% of the variation in empowerment (dependent variable) is explained by knowledge management (independent variable). The Beta index of leadership and management to predict the dependent variable is 0.23, teamwork and learner communities is 0.24, digital sophistication is 0.35, organizational culture is 0.43. There is no significant relationship between the dimensions of sharing knowledge, knowledge creation, vision and mission, strategy, intellectual capital, learner organization and employees empowerment.
8. Discussion and Conclusion According to the research findings, there is a relationship between knowledge management and the staff empowerment in Justice Administration of Tehran Province. . There is a meaningful relationship between dimensions of knowledge management with empowerment that are leadership and management, teamwork and learning communities, collaborative knowledge, knowledge creating, digital sophistication, visions and missions, strategy, organizational culture, intellectual capital, learning organization. There is no meaningful relationship between other dimensions with empowerment including knowledge sharing, knowledge creating, visions and missions, strategy, intellectual capital, and learning organization. These conclusions align with other researcher's outcomes such as Memarzadeh Tehrani, Mozafari, and Bagheri. They showed that empowerment and constant development of human resources needs information and knowledge sharing and distributing for the staff in organizations. In another study by Bowen and Lawler (1992) they introduced the following dimensions as effective in empowerment: organizational strategy, link with the customers,
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Faezeh Sohrabi, Alireza Chenari, Fattah Nazem et al complicated technology, environmental changes, and employees' high need for growth. Jaffe and Scott (1992) indicated that a successful empowerment requires a change in individuals' attitude and team relations in organizational structure (of knowledge management' dimensions). They believed that it is impossible to gain the empowerment if these factors are ignored. Kappelman and Richards (1996) discussed that empowerment, like other changes, should follow an increasing but gradual strategy to be able to implement it. In their opinion, the role of instruction is undeniable in successful process of empowerment. Another study by Burdett (1991) asserts three factors in successful empowerment: 1) training-based management 2) wide-scope jobs 3) creating a learning organization. Lashley (1999), focusing on empowerment dimensions, regarded the following factors effective in empowerment:
Management attitude (management awareness in granting authorities),
Task structure,
Organizational culture.
He believed that different combinations of these factors would represent various forms of empowerment in organizations. Philamon (2004), in his research, concluded that the following variables affect empowerment:
Satisfy the employees' need,
Relations among the individuals,
Supervisor's, colleagues', and organization's support,
The staff's beliefs,
Sense of belonging.
The modern organizations are under pressure of factors such as increasing universal competition, sudden transformation, need to quality and after-sale services and limited resources. It is proved that an organization would be pioneer in economy, job affairs and competition in case of using expert, creative and highly motivated manpower. In Drucker's view, the growth of organization is completely affected by the staff empowerment. In the empowered organization, the staff collaborates with each other in doing activities. Justice Administration is among the fundamental bodies in every country. This is because of its role to guarantee the social and individual rights, realizing peace, justice and safety. Therefore, it seems vital to consider the human resource potentials and to actualize those potentials and invaluable talents which can itself leads to the individual's development and adaptation with the objectives of the organizations. One of the most important ways to achieve organizational goals is to manage these valuable resources effectively. To do so, some factors affecting the staff empowerment are information, knowledge and job skill (Bowen & Lawler, 1992). Therefore, it is possible to empower the individuals using knowledge and managing it. Knowledge management can improve organizational performance by forcing organization to have more remarkable performance (Weick, 1999). On the other hand, according to the findings of many studies, there is a relationship between knowledge management and empowerment; it is also recommended to provide empowerment among the staff in Justice Administration of Tehran Province by applying efficient mechanisms of knowledge management. Furthermore, it is possible to improve productivity in Justice Administration of Tehran Province by creating practical mechanisms for competence, self-determination, impact, trust among the staff and job meaningfulness.
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Software Agent Societies for Process Management in Knowledge‐ Based Organization Anna Sołtysik‐Piorunkiewicz and Mariusz Żytniewski University of Economics in Katowice, Faculty of Informatics and Communication, Department of Informatics, Katowice, Poland anna.soltysik‐piorunkiewicz@ue.katowice.pl zyto@ue.katowice.pl Abstract: The dynamics of changes in an organization’s environment and the increasing competitiveness of the global knowledge‐based economy compel business companies to use their resources efficiently and effectively. Business management theory offers the process‐centric approach founded on a systemic perspective of the organizational structure. Within management science, the process‐centered approach (which support knowledge productivity and which facilitate innovation) originates in relates to a shift in the way we perceive the organizational structure, i.e. a move away from a vertical, linear, functional perspective toward a horizontal, process‐centric one. Agent technologies may, in this context, constitute an element of the organizational information or knowledge system, where software agents support selected tasks within the framework of a specific business process. On the other hand, in the case of multi‐agent systems, the process‐centered approach comes to be applied in defining the system architecture. The use of software agents will make it easier to integrate distributed devices within business processes in which a human being participates, dynamically specify business processes with the participation of such devices and codify such processes as part of the concept of composite software, and consequently gain a competitive advantage by knowledge‐based organizations thanks to the possibility of gaining new knowledge about processes and entities that take part in them. The use of semantic solutions in this process will aid the representation of knowledge gained in this way and its processing by IT technologies and by a human being. This way of codifying knowledge about dynamic business processes and entities that participate in them makes it possible to use agent societies also for the purpose of managing the knowledge that was obtained in this way. This paper is dedicated to a range of issues concerning the process‐centric approach and software agent technologies to support organizations during the knowledge‐based economy. Keywords: knowledge management, knowledge‐based organization, software agent societies
1. Introduction The current development of a knowledge‐based society makes process management oriented organizations look for solutions that support their day‐to‐day operations connected with the business processes taking place in them and knowledge about them. One of the trends that may facilitate the development of this conception is the use of agent technologies which may influence the enhancement of business processes taking place in knowledge‐based organizations. This paper will present theoretical issues connected with the conception of knowledge‐based organizations with respect to the use of software agent societies as an element supporting processes that occur in them. The ISO 9000:2000 standard interprets a process as a an assembly of correlated and interlinked activities by which inputs are transformed into outputs, while the process approach is described as systemic identification and management of processes applied within an organization, particularly the management of interactions between such processes. The operations of a process‐oriented organization can be supported through the use of information technology tools that allow the design, implementation, monitoring and simulation of business processes (Dayal et al. 2009). Agent technologies may, in this context, constitute an element of the organizational information system where software agents support selected tasks within the framework of a specific business process. On the other hand, in the case of multi‐agent systems, the process‐centered approach comes to be applied in defining the system architecture. Software agents (Zambonelli et al. 2003) “(...) can be conceived in terms of an organized society of individuals in which each agent plays specific roles and interacts with other agents according to protocols determined by the roles of the involved agents.”
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Anna Sołtysik‐Piorunkiewicz and Mariusz Żytniewski Software agents may constitute an immanent element of an organization’s IT systems (raised by, among others (Olszak 2007), supporting a system of knowledge management and business processes . We should remember that business processes refer not only to the organization itself but also to its environment. For that reason, these issues must be considered in a broader context based on the assumptions of a process‐centric approach related to ubiquitous communication. For an organization, ubiquitous communication means, on the one hand, an easier integration of its information system with business partners and, on the other hand, new possibilities of using the knowledge about the environment. This process can be supported by the semantic methods of knowledge representation in the form of ontology. The approach aims to enhance the competitiveness of an enterprise in a rapidly changing environment by reducing its operating costs, improving the quality of its products and/or services, and to streamline its operations by clearly isolating the stages within each process, monitoring these, and by eliminating any potential bottlenecks. The process‐oriented approach supports effectiveness, largely helping an organization achieve its objectives. It has been noted that currently the third wave in the evolution of the process‐oriented approach has begun (Bitkowska 2009). However, such activities require that agent solutions not only constitute an element of an organization’s information systems, but also have their own mechanism for processing and representing the intraorganizational knowledge, becoming an element of knowledge processing systems (Monticolo et al. 2012). Modern knowledge‐based organizations which are oriented not only on the use of solutions designed to support business processes but also on creating, processing and spreading knowledge, may be one of the areas where such solutions are used. The first part of the study will feature issues concerned with the use of software agents to support an organization’s operation from the process‐oriented perspective. In particular, it will provide an overview of models of agent supported organizations. The second part will present the conception of knowledge‐based organizations, with special attention to the specificity of their operation, and the third part will be concerned with the issues of IT solutions used in such organizations. The subsequent part of the paper will present the areas of use of agent solutions in knowledge‐based organizations with special focus on their potential applications.
2. Models of agent supported organizations From the perspective of an organization, the dichotomy of the business processes and knowledge management processes makes it necessary to look for solutions that support the processing of the knowledge possessed by an organization with respect to business processes that take place in it. One of the conceptions of building IT solutions to support an organization’s operations is the theory of software agents which can be viewed in the context of cooperation between individual autonomous units making up a society (Żytniewski 2013), where software agents influence the environment by communicating with it and among each other (Figure 1) . In such structures, there should be certain principles, norms regulating the way the entities act, established by their creator and emerging from the tasks assigned to them. The activity of an organized group of software agents depends in this scope on formal and informal structures of interaction between the agents, which define communication channels, allocation of information for the task being processed, deployment of decision‐makers and the presence of stimuli. In the context of the use of such a multi‐agent structure as an element of social organizations where a society of software agents constitutes a technical solution supporting an organization’s operation, the key research aspect is to define the areas where the agents support, influence the organization. As far as the research (Chang and Harrington 2006) conducted so far is concerned, the main areas where agent‐based solutions influence an organization include information allocation, the presence of authorities, organizational norms and culture, motivating.
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Source: (Wooldridge 2002) Figure 1: Software agent influencing the environment Information allocation in an agent model refers to the way information flows between the organization and its environment, and additionally the influence of the information on the organization, using a software agent, i.e. which agent and where is responsible for the information flow between the organization and the environment. Another aspect of an organization supported by software agents is connected with the responsibility for the decisions made by designated software agents. The participation of agents in a decision‐making process relies on two features: modularity and decentralization. Modularity refers to a system‐centric approach to management where a given process can be performed in several sub‐processes, and these sub‐processes can be performed by appropriate tasks, and as part of these tasks certain operations can be conducted. Thus the problem that arises here is connected with the types of operations, tasks and sub‐processes within a given process, what actions should be taken in a given module and possibilities of potential linkage of similar actions. The aspect of modularity is closely connected with decentralization, i.e. which agent is responsible at a given level for performing a given task. The third aspect of an agent‐supported organization is connected with organizational culture. Referring to Sathe’s definition of organizational culture (Sathe 1985) we can assume that an agent’s behavior depends on the organization’s historical factors which are contained in the organization’s norms and culture (Chang and Harrington 2006). The next aspect discussed in the literature is the sphere of motivation of the entity performing individual tasks within the framework of the processes run. In such a case, human factor can be subjected to various influences which in the case of the use of agent‐based solutions come down to a certain decision imperative of an agent. Supporting an organization’s information systems with agents involves using agents to support activities related to the execution of information flow between the organization’s environment and the organization itself. One of the research strands, presented by Chang and Harrington 2006, points to the similarities and differences in modeling an agent supported organization taking into account the role of agents designed to solve problems in an organization. In the literature of the subject we can also find works which use the theory of software agents as an element supporting the building of organization models. They include, among other things, the model by Kollman, Miller and Page (Kollman, Miller and Page 2000), model by Chang and Harington (2000) and (2003) (Chang and Harrington 2006) which focus on the use of agents in solving similar problems within a given organization, e.g. sale of products in a sales network, manufacture of a specific line of products, or models dedicated to the sphere of financing complementary operations within an organization. They include the model by Rivkin and Siggelkow (Rivkin and Siggelkow 2003) and the model by Siggelkow and Levinthal (Siggelkow and Levinthal 2003). Other organization models mentioned in the literature which use the theory of agents to model an organization’s behaviour are presented in table 1.
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Anna Sołtysik‐Piorunkiewicz and Mariusz Żytniewski These models (table 1) use agent‐based solutions in the process of analysis, adaptation and evolution of organizational structure: model by Ethiraj and Levinthal (2002), model by Carley and Svoboda (1996), address the issues of processing information in an organization: model by Miller (2002), and refer to the issues of agents’ adaptability in the context of an organization’s operation: model by Carley (1992), model by Barr and Saraceno (2002) and (2005). Table 1: Models of agent knowledge‐based organizations against the background of research strands in neoclassical economics
Description
Models
Research strands Solutions to similar and Changes of Management of information processes and different problems in an organization information processing organization al structure different different influence of general organization adaptation units solve operations of software characteristi of and the same an agents in cs of adaptational evolution of problems organization organization information (learning) the are s on the processing agents organization distributed changes of al structure among the the organization’ organization s functional al structure divisions model by model by Model by model by Model by Model by Carley Carley and Kollman, Rivkin and Ethiraj Miller (2002), (1992), Svoboda Miller and Siggelkow and Levinthal Page (2000), (2003), (2002). (1996), model by model by Model by Model by Chang and Siggelkow Barr and Miller Harington and Levinthal Saraceno (2001). (2000). (2003). (2002). model by model Barra Chang and and Harington Saraceno (2003). (2005).
The models of organizations listed here show a great variety of approaches to the use of the conception of software agents appearing in neoclassical economics. But these approaches do not sufficiently address the issues connected with the new conceptions of supporting an organization’s operation, not only in the context of information resources but also modeling software agents in the area of facilitating knowledge management in an organization and business processes performed with their participation.
3. Knowledge‐based organizations The approaches to agent‐based support of an organization that have been mentioned above focus on meeting information needs of an organization and its environment. We are living in a time when information underlies the functioning of a society and economic organizations. Because of a large amount of information that is generated and processed it is necessary to channel such information and prevent informational noise to which a decision‐maker is subjected. This results from the fact that the distinguishing feature of the conception of an information society is access to information and an efficient contact among the entities that exchange it (Zacher 2004). Modern knowledge‐based organizations understand the importance of knowledge in the process of creating a competitive edge. According to the definition, a knowledge‐based organization is an organization „whose structure is subordinated to and focused on creating value added based on an effective use of knowledge” (Grudzewski i Hejduk 2004). At the same time it should be noted that some researchers question the sense of assigning an organization to the category of knowledge‐based organizations based only on the criterion of the delivery of products and services whose main element is knowledge (Zack 2003). Thus, IT solutions focused on the aspect of supporting such organizations should support business processes that take place in them in the
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Anna Sołtysik‐Piorunkiewicz and Mariusz Żytniewski area of creating, processing and sharing a contextual knowledge about them. This results from the fact that knowledge‐based organizations focus not only on business processes but also on knowledge management processes which should be treated in such organizations equivalently. Based on the characteristics presented above we can say that knowledge‐based organizations use the intellectual capital to manufacture products and provide services, but they also consciously manage the intellectual capital and are capable of learning. This is in line with the definition provided in the paper that “a knowledge‐based organization is a social‐economic organization with simplified and flexible structures which is mainly concentrated on knowledge, processes and team work, which intensely uses relations with the environment and creates an organizational culture that favours knowledge management” (Ziemba 2009). They constitute a pillar of a knowledge‐based society as they absorb knowledge from the environment, process knowledge, generate a new one and add it to another environment. Knowledge‐based organizations are ones that adapt their offer and operation to knowledge that comes from the reflection on the way they have operated so far, and consciously manage knowledge resources possessed by them. Such organizations are “capable of innovative and fast adaptations, with the most important thing being the accumulation of intellectual capital and knowledge management (Kromer 2009). Knowledge in an organization requires management. It is the totality of processes that make it possible to create, disseminate and use knowledge for the purpose of pursuing an organization’s objectives (Murray and Meyers 1997). The areas of knowledge management include the use and creation of knowledge, knowledge localization, obtaining knowledge, knowledge retention and transfer. The aim of knowledge management in these areas is to identify valuable and valueless knowledge, store knowledge, disseminate and use knowledge, reduce the risk of knowledge loss and increase the competitive edge. Among the models of knowledge management, the following three are deserving attention: Japanese model of knowledge management according to Nonaka, Takeuchi (Nonaka and Takeuchi 2000) process‐centric model of knowledge management according to Leonard‐Barton (Leonard‐Burton 1995), model of competences development according to Cap Gemini. The Japanese model is the so called Knowledge spiral described by Nonake and Takeuchi (Nonaka and Takeuchi 1995). In the context of the use of IT solutions supporting knowledge‐based organizations, this process may be used to implement IT solutions that require knowledge to be obtained from users in order to codify and disseminate it in an IT system. Then the elements of this approach may find application in the process of preparing the architecture of a system supporting knowledge processing. However, this process should not focus only on the architecture of a system being built. It should also support the discovery of knowledge sources and the obtaining of knowledge, and in further stages such a codification of knowledge that allows the developed structure of the resources of the codified knowledge to be understood by IT systems and users themselves. According to the strategy of knowledge management used by Cap Gemini, the combination of competences and the latest technology leads to an increase in the value of a company. The combination of people’s knowledge and competences with modern IT tools ranges from the analysis of the situation concerning the interior of a process to the selection and implementation of an IT tool which can be a software agent ‐ an example of a solution at the highest levels of management and technology. These issues indicate that organizations focused on knowledge management must be supported by new IT solutions which not only support, as was indicated earlier, information processing, but are also focused on the use of organizational knowledge in these processes. The solutions postulated may be agent technologies and systematic methods of knowledge representation supporting the processes of communication in agent societies.
4. Technological needs of a knowledge based organization The development of IT solutions used by organizations makes solutions such as intranets supporting group work or document management systems prevailing in the context of supporting information needs insufficient to build a competitive edge. The information solutions used today in chats, instant messengers, discussion
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Anna Sołtysik‐Piorunkiewicz and Mariusz Żytniewski lists, social media, wikis or blogs (Rydz 2008) allow the transfer and popularisation of information, but their role is human being oriented storage and processing of knowledge. Modern IT solutions should focus on providing unified and transparent solutions enabling the access to various sources of knowledge and information, on making it possible to connect knowledge sources and places it is used during the execution of business processes and to generate and present the knowledge about the business processes in which users participate. To achieve that, IT solutions that support an organization’s operation should draw from and enrich the intraorganizational knowledge. They should be used to change data and information in knowledge resources (in the case of the knowledge of a certain field) and make it possible to create procedural knowledge, and to codify and write it as an additional resource of organizational knowledge. This will allow the processes performed by information systems to be treated not as a hidden knowledge written in a machine code but as a resource of a process knowledge, processed by various IT systems. In the case of learning organizations (often equated with knowledge‐based organizations), we can distinguish individual, group and organizational learning (Olszak i Ziemba 2008). However, “at today’s stage of development of knowledge‐based economy the above mentioned levels of organizational learning should be supplemented by interorganizational and social learning”. Interorganizational learning, like organizational one, also refers to organizational memory, but it involves several organizations. On the other hand, social learning refers to various informal groups (e.g. experts, practitioners, supporters, hobbyists, etc.) which enter into relationships with an organization and influence it (Ziemba 2009). The effect of studies conducted in the field of knowledge codification are languages of ontology description which enable the representation of knowledge in the form of a formal model describing the semantics of the terms used (Staab i Studer 2009). As a result of their use, IT systems no longer operate only on data and receive a conceptual description of the knowledge resource they use. Such an approach supports the postulated social and interorganizational processes of learning, providing a coherent conceptual meta model of knowledge, and allows an easier integration of the IT solutions indicated within the framework of the business processes performed and the knowledge processed. For that reason, IT solutions should support not only the acquisition, distribution and presentation of knowledge within an organization but also facilitate these activities in the interorganizational context. This is connected with the processes of knowledge accumulation, knowledge management, knowledge sharing and knowledge innovativeness (Xiaocui 2010). As has been pointed out, the current trend in the development of knowledge representation is the use of semantic description in the form of languages of ontology description (Zytniewski 2010). The main strand of such research is based on the issues of semantic Internet, but semantic methods of knowledge representation may support the codification of an organization’s knowledge of a given field and procedural knowledge, supporting its business processes. Currently used solutions designed to support the modeling of an organization’s business processes are connected with a specific integrated system or support only the process of modeling a business process based on a selected form of process representation (Stanek 2010). The use of a semantic representation of such a process, its participants, course, stages and objectives on the one hand allows business analysts to understand it better and on the other hand helps to use, for the purpose of supporting it, new conceptions in the form of software agents for which the semantic representation of knowledge leads to a better performance of the tasks that have been assigned to them.
5. Advantages of software agent societies in knowledge‐based organizations From the point of view of manufacturing organizations (Kieltyka 2012), software agents can support activities related to the manufacturing process, distribution, logistics, e.g. monitoring of manufacturing processes, monitoring of stocks and supply, support of the processes of designing goods, optimization of product manufacturing, participation in auction processes. In the case of service provision‐oriented organizations, agent solutions may support the sphere of contacts with customers and information distribution, knowledge about it, marketing activities, competition monitoring, customer service. The main technological advantages of using software agents in knowledge‐based organizations include:
improvement of a system’s modularity,
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ensuring that a system is easier to be modified,
ensuring a loose connection between the elements of a system and a better scalability,
greater technological independence through the use of open standards,
support of the autonomy of the elements of a system,
greater ease of searching services offered by a system,
improvement of inter‐ and intraoperability
reduction of costs.
From the perspective of knowledge‐based organizations, the advantages of using software agents can be listed against the main tasks of the process of knowledge management. As a result, software agents may constitute an intermediary element between the systems designed to support business processes and IT solutions designed to support knowledge management, by supporting:
Management of an organization’s resources in terms of their allocation, scheduling, monitoring
advanced online interactions with users to solve various decision‐making problems in the case when software agents constitute a component of a user interface,
creation and management of ontologies and knowledge contained in them through the analysis of user actions as part of business processes in which they participate,
the building of an organization’s „collective intelligence” thanks to the participation of each of the organization’s members in the processes taking place in the organization, which influences the quality of the processes being executed, their productivity and competitiveness of the organization,
the processes of automating the creation, obtaining and propagating of knowledge through the use of conversational agents which may gather knowledge about the user during the conversation with him, creating his profile and disseminating a codified knowledge,
acquisition of specific resources of knowledge from the environment,
simulation of how business processes run in organizations based on the codified knowledge contained in IT systems,
knowledge distribution and processing through the use of semantic mechanisms of communications among agents in a given society,
access to knowledge and cooperation between knowledge users by ensuring interactive and multimedia user interfaces or becoming an element of knowledge portals run in organizations.
automation of the processes of knowledge discovery and use, obtaining knowledge from data warehouses, multidimensional structures and using it during the execution of an organization’s business processes
the processes of assessing knowledge and its use, as well as monitoring; informing users about a specific knowledge being available.
The use of groups of software agents supporting various areas of an organization’s operation can on the one hand refer to the tasks required from solutions supporting knowledge management and, on the other hand to innovativeness connected with the use of software agents in the technological context of building of such solutions.
6. Summary To support the operation of a process‐oriented organization, it is possible to use IT solutions designed to design, implement and monitor business processes, as well as simulating their operation. Agent technologies may constitute in this case an element of an organization’s information system where the agents support selected tasks within the framework of a defined business process. The conceptions of the use of agent technologies with respect to their applicability in knowledge‐based organizations that have been discussed in this paper show that such solutions may be used not only as an element of IT system integration or a solution designed to support the execution of business processes but also as an element supporting knowledge management systems in an organization. Thus, such solutions can on
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Anna Sołtysik‐Piorunkiewicz and Mariusz Żytniewski the one hand support the processes being executed and on the other hand they can process the knowledge about them, becoming an element linking business processes with knowledge management systems. For such a use of software agents to be possible it is necessary to use unified solutions allowing the representation of intraorganizational knowledge. The current research strand indicated in the paper are the languages of ontology description which are developed in the context of the theory of semantic Internet, but can also be used in the field of knowledge management in an organization. The main research problems that appear in the context of the agent technologies indicated include methodologies supporting the modeling of software agent societies in the context of their use in knowledge‐ based organization, in particular the creation of methodologies which support the acquisition and codification of tacit knowledge in the form that is semantically interpretable and the creation of solutions that can be duplicated which support agent‐oriented knowledge management where agent solutions will constitute an element of a knowledge management system using the languages of ontology description as an element of a defined intraorganizational knowledge. These issues will constitute further research aspect of the authors.
Acknowledgements The issues presented constitute a preliminary stage of the authors’ research into the aspect of modeling software agent societies in knowledge‐based organizations. The project was financed from the funds of National Science Centre 2011/03/D/HS4/00782.
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Innovation and Sustainability: Two‐Sided Knowledge Management by an Ice‐Cream Producer Inga Stankevice1 and Birute Slaustaite2 1 Department of Strategic Management, Faculty of Social Sciences, Kaunas University of Technology, Kaunas, Lithuania 2 JSC Unilever Lietuva distribucija, Marketing Division, Mazeikiai, Lithuania inga.stankevice@ktu.edu birute.slaustaite@gmail.com Abstract: Knowledge management is inevitably related to decisions about the priority areas it can be applied to. Today, these areas, to a great extent, are conditioned by global trends, such as the increased attention to innovation and corporate social responsibility (CSR). The links between innovation and corporate social responsibility can emerge as a result of the implementation of different types of innovation and the application of different principles of responsibility, thus bringing to enterprises and communities more than a plain summative utility. The synthesis of innovation‐ and CSR‐ activities can foster technological development and contribute to the cherishment of natural and socio‐cultural environment that gives origins to the enterprises' resources. However, the relationship between innovation and corporate social responsibility remains underexplored. Though some links have already been identified, yet it is not clear which dimensions of CSR are related to which types of innovation, and how this two‐sided knowledge converges into one fruitful whole. Therefore, the paper is aimed at the revelation of the relationship between innovation‐ and CSR‐activities in the context of knowledge management. The empirical evidence was based on the case study of an ice‐cream producer, and the data assessment and analysis include field study, analysis of the enterprise’s annual reports, as well as half‐structured interviews with top managers of the structural units of the company. As a result, the dimensions of corporate social responsibility were related with the types of innovation, thus revealing how a creative synthesis of varied knowledge leads to the growth of competitiveness and effectiveness, as well as to the increase in public good for the interested parties. The paper is original in its attempt to reveal the value that is generated as a result of the integration of knowledge on the increasingly popular trends (i.e., innovation and sustainability) by the ice‐cream producer into its activities. Besides, the scholarly contribution in the shape of the revelation of the links between certain dimensions of corporate social responsibility and types of innovation is also relevant. Keywords: corporate social responsibility, ice‐cream producer, innovation, knowledge, knowledge management
1. Background In a broad sense, knowledge management can be regarded to as a process during which different knowledge assets are used in order to generate value. Hence, this case study reveals what kind of value is generated as a result of a two‐sided process of prioritisation, integration and usage of the knowledge assets. Numerous approaches to knowledge management (Nonaka, Takeuchi 1995; Davenport, Prusak 1998; Despres, Chauvel, 2000; Adenfelt, Lagerstrom 2006) suggest that knowledge assets are of intangible nature, thus making it difficult to decide about the priority areas these assets can be applied to and about measures to be used during this process. However, this paper does not attempt to develop one more assessment methodology that helps to prioritize knowledge management related activities, as this has already been done by Bornemann & Sammer (2003). Instead, the paper reveals a case study of an ice‐cream producer whose priorities, as well as those of many other enterprises today, are conditioned by global trends, such as the increased attention to innovation and corporate social responsibility. Hence, the paper is primarily focused on the result of this two‐sided knowledge convergence, whereas the very process of knowledge management is kept in the background, except for sources of knowledge feeding into the process. Nonetheless, the value that is generated by courtesy of the integration of knowledge on innovation and sustainability by the company into its activities is important to the discourse on knowledge management equally to the very process of knowledge management, as the generated value convinces of the strength of leverage of the proper prioritisation, integration and usage of knowledge assets on the company’s performance and its ability to address new challenges posed by the most voiced contemporary trends. Besides, innovation has long been associated with knowledge management, e.g. at the level of business processes in Bornemann & Sammer’s (2003) taxonomy of drivers of knowledge vision. Innovation is understood as a part of knowledge management which supports organisational excellence. However, the
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Inga Stankevice and Birute Slaustaite incorporation of the dimension of sustainability into the duet becomes often a challenge. On the one hand, corporate social responsibility within an enterprise is a factor that hinders its innovativeness for different reasons: distracted attention, cut resources, decreased spectrum of options, etc. (Midttun 2007; Scott 2007; Griseri, Seppala 2010). On the other hand, CSR can also be managed as a tool that, on the contrary, fosters innovation and increases competitiveness. Hence, according to Ubius & Alas (2009), more and more firms engage in CSR‐activities that not only multiply the firms’ efficiency but encourage them to expand and innovate as well. Porter & Kramer (2006) state that CSR is more than plain increased value of enterprises or their generous acts – CSR can also represent new opportunities, or contribute to an increase in competitiveness or innovation activities. Then, according to Asongu (2007), an enterprise that seeks to become a business leader has to concentrate on the creation of innovative technologies that would contribute to the development of CSR principles. Preuss (2011) supports the statements above as well and suggests that implementation and development of innovation should be a key principle of CSR. In addition, he claims that sponsorship for ideas based on CSR is better accessible, and it is generally easier to innovate in the area of CSR, as this kind of innovation is motivated by not solely economic, but also ethical and philanthropic reasons. Therefore, the revelation of the relationship between innovation‐ and CSR‐activities is essential. Preuss (2011) proposes three areas of successful CSR implementation with regard to innovation activity: good and service innovations, emergence of new markets and customer groups, and creation of new business models. Good and service innovations concern the production of environment‐friendly products and their components, design and manufacturing of “green” products (i.e. suitable for recycling, consuming fewer resources in order to promote more economical energy and other resources). An example of the emergence of new markets and customer groups could be the production for customers with special needs, whereas new business models concern, for example, new business models that enable efficient and responsible exploitation of resources (energy, manufacturing materials, etc.), nature preservation and revitalization of brownfield, etc. Francis & Bessant (2005) indicate four modes of the relationship between CSR and innovation: content, process, positioning of CSR, and innovative forms of CSR. In the context of the content, innovations are mostly ecological – products include natural components exclusively, environment‐friendly technologies are used, etc. In the context of process, links between CSR and innovation emerge during business processes in the shape of reusable packaging or secondary material processing. Then, links between the positioning of CSR and innovation emerge when a firm chooses one pattern of the implementation of CSR principles and innovation. For instance, a firm that concentrates on reusable energy sources and implements technological innovations in precisely this area. And finally, an example of an innovative form of CSR is a firm that first amongst all the others establishes an endowment, day‐centres for children in need, etc., and thus extends the understanding of CSR. Hence, the literature points to the existence of varied types of innovation and dimensions of CSR within the relationship between the two factors. Concerning innovation, one would first of all distinguish 1) product innovation, 2) process innovation and 3) organisational innovation (Edquist et al. 2001; Battisti & Stoneman 2010), i.e. pretty traditional types of innovation. Nonetheless, the reference to CSR suggests that a 4) community innovation plays a role as well (e.g. creation of innovative forms of CSR as indicated by Francis & Bessant, 2005). It is also important to note that, more often than not, several types of innovation are implemented by an enterprise at a time. On the other side, the traditional dimensions of CSR, as defined by Carroll (1999) and refined by Griseri & Seppala (2010), are also considered in the explored literature. Economic responsibility (1) includes timely payments to all the interested parties and taking into account needs of special individual groups (e.g. disabled people, youth, or pregnant women). The basis of legal responsibility (2) is a responsible implementation of legal commitments, including safe and healthy working environment, and respect for and protection of human rights. Ethical responsibility (3) requires from enterprises that they meet those customer expectations which are not defined by law or existing moral norms, e.g. integration of the community’s and business interests in a mutually satisfactory way. This dimension of CSR lays foundations of the communication between the enterprise and community. And last but not least, philanthropic responsibility (4) is regarded to as voluntary acts of the enterprise, aimed at the sustainability or improvement of a society’s welfare, e.g. philanthropy and
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Inga Stankevice and Birute Slaustaite voluntary participation in social activities and initiatives, youth involvement in business and professional promotion, transfer of knowledge and practice opportunities. To conclude, though the explored literature reveals some linkages between CSR and innovation, and makes references to certain types of innovation and dimensions of CSR, yet it is not that clear which dimensions of CSR are related to which types of innovation, in which areas of activity these links are present and how viable they are, i.e. how the result of this two‐sided knowledge convergence into one fruitful whole looks like.
2. Methodology in a nutshell JSC Ingman Ledai became the object of the case study, as the company emphasized its innovativeness and enlarged attention to CSR. At the time of data collection in 2012, JSC Ingman Ledai (currently split up into JSC Unilever Lithuania ice‐cream production and JSC Unilever Lithuania distribution) was a recognized market leader in Lithuania with the long‐term successful business experience, bringing together a team of competent professionals engaged in rigorous product quality control, and great attention to new product development and customer satisfaction. JSC Ingman Ledai produced ice‐cream and waffles, and was engaged in their trade. The company realized its production in both the local and international markets. Sales in the domestic market (Lithuania) accounted for 52% in 2011, and 48% of the production were exported to other countries (20.64% ‐ Latvia, 15.36% ‐ Finland, 9.6% ‐ Estonia, 0.96% ‐ Germany, 0.17% ‐ the USA, and 0.96% ‐ other markets). Beyond the management department, the company encompassed four structural departments: the department of ice‐cream production was the largest, and it was followed by logistics, laboratory, and marketing. The research was aimed at the revelation of: 1) innovative activities introduced by JSC Ingman Ledai in each department; 2) CSR‐activities introduced by the company in each department; 3) links between innovation and CSR in each department. The company’s reports of 2009, 2010 and 2011 were analyzed in order to indicate what kinds of innovation and CSR were introduced in each department and for what purposes, as well the respective expenditures. The longitudinal data was used in order to vouchsafe the reliability of the results, i.e. that the links between CSR and innovation are constant and not accidental. Accordingly with the theoretical background, four types of innovation (i.e. product, process, organisational, and community) and four dimensions of CSR (economic, legal, ethical, and philanthropic) were considered. In addition, half‐structured interviews with top managers of the functional departments were carried out and included following core questions: 1) Could you please identify any recent innovative activities introduced in your department? 2) How do these activities contribute to the company's performance? 3) Could you please identify any recent CSR‐aimed activities introduced in your department? 4) How do these activities contribute to the company's performance? 5) Do you think that corporate social responsibility is related to innovation in this department and how? 6) Are there any recent innovations in your department which concern CSR? Finally, all the data were integrated, thus enabling to reveal the relationship between innovation‐ and CSR‐ activities in different areas of activity of the research company.
3. Links between innovation and CSR: Findings In order to assess which dimensions of CSR are related to which types of innovation, and in which areas of activity these links are present and how viable they are, the four departments of the company were analyzed separately. In the production department, ice‐cream mixes are prepared, final products are produced and packaged. In the logistics department, the final products are stored and distributed to the outlets. The laboratory is responsible for controlling the production activities: it tests products by composition, weight, and appearance standards. It is also responsible for the hygiene. Finally, the marketing department prepares the company’s marketing strategies, evaluates profitability of new products, and represents the company’s interests in the area of marketing.
3.1 Value generated by the two‐sided KM: production Based on the company’s annual reports (AR) 2009‐2011 and the interview with the department’s top manager, table 1 was produced.
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Inga Stankevice and Birute Slaustaite In 2009, harmless detergents were introduced into the process of cleaning the equipment. These detergents self‐eliminate completely from washable surfaces and leave no trace. Hence, this activity is featured by community innovation: the harmless detergents treasure environment and health of the employees. The detergents helped the company to gain the consumers’ trust, as this action attracted lots of positive reviews in a local newspaper. In addition, this activity is associated with philanthropic responsibility, as it is the company’s way of a voluntary contribution to the public welfare. During the same year, the department began also to produce ice‐cream fillings with palm oil, which, as an integral part of an ice‐cream, was not common to the market. Hence, consumers could enjoy a healthier product. Table 1: Expenditure on innovation and/or CSR in the production department in 2009‐2012 Activity
Expenditure, Eur
Introduction of harmless detergents
3578
Palm oil in ice‐cream fillings
4218
Interview, AR 2010
Change of ice‐cream design
3917
Interview, AR 2011
Installation of milk freezer
5691
Source Interview, AR 2009
Interview
Types of innovation process; community product; community product; community product; process; community
Dimensions of CSR philanthropic philanthropic philanthropic legal; ethical
Fly‐by‐wire control system aimed at ice‐ cream mixes production Timely salaries, exceeding the minimum
n/a
process
‐
n/a
economic
Production for consumers with special needs
n/a
‐ product; community
philanthropic
In 2010, the department launched the production of newly‐designed ice‐cream – it became lower and wider. This activity can be described as product innovation because the product had been modified in terms of its shape. Moreover, this innovation is focused on community needs and represented the company’s effort to reduce environmental pollution and conserve natural resources via cutting the consumption of paper down. Due to the change of the form of ice‐cream, the ice packs became considerably smaller, less paper was used during the packaging process, and, consequently, less paper was recycled by the consumers. In the background, the company’s target was also to reduce costs. Then, in 2011, a new milk freezer was installed. This action can be attributed to product and process innovation, as it not only helps to improve the final product (i.e. ice‐cream), but also change the production process. The installation of the milk freezer is a community innovation as well – the company took into account user preferences and, thanks to the freezer, enabled the usage of natural components during the process of production of ice‐cream. On the other hand, the action is related to CSR. As the continuous improvement of the quality of ice‐cream meets all legal requirements, the legal dimension of CSR should be mentioned. However, the action involves also the ethical dimension of CSR, as it is based on ethical principles, such as, for instance, fairness to consumers. The fly‐by‐wire control system was also installed in the department. It is a process innovation because it did not alter the composition of the products, but each stage of the production process was shortened. However, the installation of the system cannot be attributed to CSR, because it does not represent any of its traits and was exclusively aimed at the company’s efficiency. Similarly, timely salaries represent economic responsibility, but they have nothing to do with innovation. The top manager of the department has also mentioned the release of ice‐cream for consumers with special needs. Sugar‐free ice‐cream was launched for diabetics, consumers who are on diet or simply do not want to consume sugar. Another innovative product ‐ ice‐cream with reduced lactose for lactose‐intolerant consumers – was also introduced to the market. Production for consumers with special needs can be attributed to the philanthropic CSR, as the company contributes to the welfare of the community by addressing the specific needs of the consumers and proposing them equivalent product options. To sum up, in the production department the link between community innovation coupled with product and/or process innovation, and philanthropic CSR is most vivid. This resulted from the specifics of activity of
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Inga Stankevice and Birute Slaustaite the department – the production of new, environmentally and socially safe ice‐cream, and modification of the production process or final product in order to meet the society's expectations, and, when possible, reduce costs by courtesy of the same action.
3.2 Value generated by the two‐sided KM: Logistics Based on the company’s annual reports 2009‐2011 and the interview with the department’s top manager, table 2 was produced. Table 2: Expenditure on innovation and/or CSR in the logistics department in 2009‐2012 Source Interview, AR 2009 Interview, AR 2010 Interview, AR 2011 Interview
Activity
Expenditure, Eur
Types of innovation
An automatic cold‐keeping system in vehicles
6240
process
“Strech” wrap for packaging
1004
process; community
philanthropic
Fully automated cold store exploitation
70957
process
philanthropic
6470
product; community
legal; philanthropic
n/a
process
‐
n/a
‐
philanthropic
“BRC Global Standard FOOD” quality management system Accounting program for the fully automated cold store Execution of support projects
Dimensions of CSR legal; ethical
In 2009, the department began to use “Strech” wrap in order to wrap boxes of ice‐cream which were passed up onto palettes. The wrap is environment‐friendly: it is thinner than the other wraps and can be re‐processed. Moreover, the use of “Strech” was a new packaging technique that was completely new to the company’s target markets. Hence, the wrap was a process innovation and community innovation of a philanthropic nature. During the same year, the company has also installed an automatic cold‐keeping system in vehicles, thus ensuring the proper quality of ice‐cream in between packaging and coming onto the markets. However, the latter process innovation cannot be regarded to as philanthropic because a proper quality of production is, first, a company’s legal responsibility, and second, a natural expectation of the society, i.e. ethical CSR. In 2010, logistics department got access to a fully automated cold store. It was a process innovation, as the place of the gelation of ice‐cream was new, but the products’ composition had not been altered. The exploitation of the store is associated with philanthropic responsibility because the store contributed to healthier working conditions of the employees. Since then, they have not had to work at considerably low temperatures ‐ in the store, all the work is carried out by robots. The store includes also a new system that is able to capture continuously the trade balance and expiry terms. Yet this process innovation is not related to CSR, since the essence of this improvement was to facilitate the company's internal accounting, and it was not aimed at public good. In 2011, the logistics department installed the “BRC Global Standard FOOD” quality management system. This is a product innovation because it is aimed at improving product quality. On the other hand, the system is also a community innovation, as the company voluntarily adapted to ever higher standards to meet and exceed user requirements and expectations. Accordingly, this innovation is attributable to the philanthropic and legal dimensions of CSR. And last but not least, the logistics department has actively been involved in support projects. The department is responsible for the distribution of ice‐cream to orphans, the sick, other people in need or simply those who participate in different projects aimed at nature preservation or social inclusion, etc. Though these activities are not innovative in market, but they represent a purely philanthropic responsibility. To summarize, in the logistics department the link between process innovation and philanthropic CSR is the most visible. This means that the department tends to embark process innovations that fulfil not only obligatory, but also highly‐optional requirements concerning food quality.
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Inga Stankevice and Birute Slaustaite
3.3 Value generated by the two‐sided KM: Laboratory Based on the company’s annual reports 2009‐2011 and the interview with the laboratory’s top manager, table 3 was produced. Table 3: Expenditure on innovation and/or CSR in the laboratory in 2009‐2012 Source
Activity
Expenditure, Eur
Types of innovation
Interview, AR 2009 Interview, AR 2010
Automated weighing‐machine for ice‐ cream
360
process
Harmful vapour collector
1635
process
Interview
Ensuring the occupational safety
n/a
‐
Dimensions of CSR legal; ethical legal; ethical legal; philanthropic
In 2009, the laboratory acquired an automated weighing‐machine. This device can accurately indicate the composition and measure the mass of ice‐cream, so this is the innovation of the production process. The installation of the weighing‐machine includes features of ethical and legal dimensions of CSR, as the company strives to be honest with its customers and place on the market products that exactly match the composition. In 2010, the laboratory acquired equipment that collects harmful vapour. This acquirement was a process innovation that refined routine work techniques of the laboratory. On the one hand, both quality of ice‐cream and waffles, and healthy working conditions are legal requirements. On the other hand, it is an ethical responsibility of a company to constantly introduce even better equipment that, accordingly with the expectations of the society, raises product quality and contributes to the increase in environmental safety. Since 2012, the laboratory has paid particular attention to safety rules. The idea is that safety at the workplace is a priority. The company not only meets the necessary security requirements, but also takes care of the employees on its own initiative through the continuous increase in occupational safety. Hence, in addition to legal safety standards, the laboratory’s management is particularly concerned with its employees and their well‐being at work, thus contributing to the development of philanthropic CSR. To conclude, the laboratory’s activities enable the synergy between process innovation, and legal and ethical responsibility. Hence, similarly to the production and logistics departments, the laboratory seeks not only to obey the law, but to do what is right, just and fair. However, the laboratory does not contribute resources to the society to as great extent as the production and logistics departments do.
3.4 Value generated by the two‐sided KM: Marketing Based on the company’s annual reports 2009‐2011 and the interview with the department’s top manager, table 4 was produced. Table 4: Expenditure on innovation and/or CSR in the marketing department in 2009‐2012 Source
Interview
Activity
Expenditure, Eur
Types of innovation
Dimensions of CSR
New product creation and development group
n/a
organisational
economic
Execution of support projects
n/a
‐
philanthropic; economic
In 2012, a new product creation and development group was formed. The group is responsible for identification of market needs and proposition of products that satisfy and/or exceed them. The formation of the group was a typical organisational innovation, aimed at increase in profit. Hence, this innovation is related to the economic dimension of CSR. In addition to the economic responsibility, the marketing department, together with the logistics one, is responsible for the execution of different support projects. The marketing department is engaged in this activity by contacting persons or organisations who seek support. As the engagement in support projects is completely voluntary and contributes resources to the society, it represents the philanthropic dimension of CSR.
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Inga Stankevice and Birute Slaustaite Hence, in the marketing department the relationship between organisational innovation and economic responsibility was found out. Besides, it is also marketing department that is responsible for that the company’s philanthropic activities make profit or simply contribute to the good company’s fame, which in the long‐term can bring the increased revenue as well.
4. Discussion The analysis of JSC Ingman Ledai revealed that the links between innovation and CSR vary. A prevailing type of innovation in a department depends highly on the area of activity the department is involved in. Hence, in the production department, where ice‐cream mixes are prepared, and final products are produced and packaged, product and process innovations are the most commonplace. Yet they are often coupled with community innovation, thus making reference to the philanthropic responsibility of the company. The link between process‐community innovation and philanthropic CSR is also strong in the logistics department that is responsible for the storage and distribution of production. Thus, knowledge for innovation comes primarily from routine processes and products the departments deal with. Nonetheless, the new ideas are often based on new trends that penetrate markets and societies, e.g. environmental responsibility, individualization, social needs, etc. However, the company seeks to even exceed the expectations of the employees, consumers and society by courtesy of the reduction of costs and increased efficiency, and this is probably an explanation of its strong leadership positions. The situation is slightly different in the laboratory. The laboratory is a controlling unit of the company: it tests products by composition, weight, and appearance standards, and is also responsible for the occupational safety of the employees. Similarly to the production and logistics departments, the laboratory seeks to not only obey the law, but to do what is right, just and fair. However, the laboratory is not involved in the processes of production and distribution directly, and is less affected by external conditions. Hence, its innovations are related to legal and ethical dimensions of CSR more than the philanthropic one, and they contribute directly to the internal sources (i.e. employees) more than the external ones. The relationship between innovation and CSR is unique in the marketing department as well, and this is also conditioned by the specific area of activity of the department. Here, the link between organisational innovation and economic responsibility was identified. The marketing department is a kind of back‐office in the company that takes responsibility for that the company’s philanthropic activities make profit or simply contribute to the good company’s fame, which in the long‐term promises the increased revenues as well. Hence, the more a structural unit of an enterprise is involved into an immediate production and distribution, the more product and process innovations are implemented, the more knowledge for innovative activities is bucketed from external sources, such as consumers and society, and, consequently, the more philanthropic responsibility is viable within the innovations. On the other hand, some knowledge for innovation can also be collected from internal sources (e.g. employees), and innovations in this case are most often related to legal and ethical responsibility. Finally, organisational innovation is based on both external and internal sources, as those responsible for organisational refinement rely on information communicated by other departments. Organisational innovation is more visibly than the other types of innovation related to the economic side of CSR. It lets to integrate the two‐sided knowledge in a way that contributes to the well‐being of the society, consumers and employees on the one side, and to the reduction of costs, and increase in profitability and efficiency of the company on the other side.
References Adenfelt, M. and Lagerstrom, K. (2006) “Knowledge Development and Sharing in Multinational Corporations: The Case of a Centre of Excellence and a Transnational team”, International Business Review, Vol 15, No. 4, pp 381‐400. Asongu, J. J. (2007) “Innovation as an Argument for Corporate Social Responsibility”, Journal of Business and Public Policy, Vol 1, No. 3, pp 1‐21. Battisti, G. and Stoneman, P. (2010) “How Innovative Are UK Firms? Evidence from the Fourth UK Community Innovation Survey on Synergies between Technological and Organizational Innovations”, British Journal of Management, Vol 21, No. 1, pp 187‐206. Bornemann M. and Sammer M. (2003) “Assessment Methodology to Prioritize Knowledge Management Related Activities to Support Organizational Excellence“, Measuring Business Excellence, Vol 7, No. 2, pp 21‐28. Carroll, A. B. (1999) “Corporate social responsibility – evolution of a definitional construct”, Business & Society, Vol 38, No. 3, pp 268‐295. Davenport, T. And Prusak, L. (1998) Working Knowledge: How Organizations Manage What They Know, Harvard Business School Press, Boston.
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Inga Stankevice and Birute Slaustaite Despres, Ch. and Chauvel, D. (2000) Knowledge Horizons: The Present and the Promise of Knowledge Management, Butterworth‐Heinemann, Woburn. Edquist, Ch., Hommen, L. and McKelvey, M. (2001) Innovation and Employment: Process versus Product Innovation, Edward Elgar Publishing, Cheltenham. Francis, D. and Bessant, J. (2005) “Targeting Innovation and Implications for Capability Development”, Technovation, Vol 25, No. 3, pp 171‐183. Griseri, P. And Seppala, N. (2010) Business Ethics and Corporate Social Responsibility, Cengage Learning EMEA, Andover, Singapore. Midttun, A. (2007) “Corporate Responsibility from a Resource and Knowledge Perspective. Towards a Dynamic Reinterpretation of C(S)R: Are Corporate Responsibility and Innovation Comparative or Contradictory?”, Corporate Governance: The International Journal of Business in Society, Vol 7, No. 4. pp 401‐413. Nonaka, I. and Takeuchi, H. (1995) The Knowledge Creating Company: How Japanese Companies Create the Dynamics of Innovation, Oxford University Press, New York, London. Porter, M. E. and Kramer, M. R. (2006) “The Link Between Competitive Advantage and Corporate Social Responsibility“, Harvard Business Review, Vol 4, No. 15, pp 1‐15. Preuss L. (2011) “Innovative CSR. A Framework for Anchoring Corporate Social Responsibility in the Innovation Literature”, The Journal of Corporate Citizenship, Vol 42, Summer, pp 17‐33. Scott, S. (2007) “Corporate Social Responsibility and the Fetter of Profitability”, Social Responsibility Journal, Vol 3, No. 4, pp 31‐39. Ubius U and Alas, R. (2009) “Connections between Corporate Social Responsibility and Innovation Climate”, EBS Review, Vol 5, No. 15, pp 58‐71.
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Business Innovative Environment as a Prerequisite for a Long‐run Competitive Advantage Marta Christina Suciu and Cristina Andreea Florea Academy of Economic Studies Bucharest, Romania Christina.suciu@economie.ase.ro Cristina.andreeaf@gmail.com Abstract: The purpose of this paper is to highlight the importance of innovation in businesses. It tries to illustrate the way companies could invest in lifelong learning focusing on the entrepreneurship education in order to support creativity and innovation as prerequisite for a short‐run increase in their profits and mostly for a long‐run sustainable competitive advantage. In the first part of the paper, we will focus on companies. In some companies the manager is thinking how creativity could help. One person may generate 10 ideas; conversely, 5 persons may generate 50 ideas. From those 50 ideas the manager can use at least 3 of them to increase the profit. This is how intellectual capital and creativity should be enhanced. Furthermore, it will influence the consumers buying renewed products in a more profitable way. The second part of the paper, we will highlight the importance of taking into account some best practices cases. Nowadays, with Internet banking, Euro Automatic Teller Machine (ATM) and credit cards, the final user has been granted options for the monetary transactions. People like consumers want their needs to be satisfied and this is one of the reasons why they have grown accustomed to demanding more and more from their banking services providers. In the last part of the paper, we will discuss about the creativity models and ways in which these models might influence the workers and the leaders to come up with new ideas, be creative and generate larger profit. From a methodological point of view we applied both the quantitative method (surveys) and the qualitative one (in terms of focus‐groups & interviews). Keywords: business innovation, competitive advantage, innovation
1. Introduction Nowadays, many companies must catch up global development through improving the quality of the products. Banks represent a distinct segment, which invests in technology in order to enhance the range of value‐added products and services. The implementation of the Internet banking service and mobile banking solution has changed dramatically the consumer’s perspective regarding banks. The Knowledge‐based organization understands the importance of education and workers’ skills for maintaining a sustainable and competitive business. We will explain the correlation between innovation, creativity, technology and bank products within the financial development framework.
2. Brief literature review Innovation is one of the most important engines of a sustainable development. Team buildings, creative meetings, private inquiries with employees, all contribute to assure a sustainable competitive advantage. Innovation is present everywhere regardless if we talk about investment in people or creating a new business line, branding or rebranding, finding new source of earning money or decreasing the costs. Tom Peters (2010: 227) said that in a crowd market, branding is an essential element used to come forth. On one hand, innovation and creativity need also an anticipatory thinking that asks for analyzing the way ideas are implemented, while on the other hand, the retroactive thinking is trying to explain the effects of one particular idea. Creative solutions are sought now more than ever. Karsten Nooack (2012) explains that creative thinking could be stimulated by using associative methods, analogies, illustrative methods or systematic procedures. Other authors, such as Chesbrough (2003), present innovation as a new paradigm for enterprises which can be used as a sustainable long‐term advantage, based on internal and external sources of innovation. Small companies can be innovative if they use intensively the human capital as an internal source of innovation. The employees can be stimulated to create through incentives, challenges, quality circles, brainstorming and other methods. Another important role regarding the development and competitiveness is played by meetings inside the company focusing on discussions about the six hats procedures, access point, random stimulation, analogies, dominant’s idea, design and branding.
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Marta Christina Suciu and Cristina Andreea Florea ING Bank developed a program named “Go Smart” as an internal competition started in 2008, open to all ING Bank staff. Its goal was to involve the employees in identifying current business problems, malfunctions or unexploited opportunities, and to come up with out‐of‐the‐box, original and practical business solutions. The lateral thinking is very important in business development. Edward de Bono (2010) considers that lateral thinking focuses on discovering ways to reach the main objective and to choose the best way to reach it. The managers can improve the work activity so as to become more competitive. The “ICEC” is an ING innovation centre that supports businesses by working along with their employees in order to find new and innovative ideas about how to improve the ING customer experience. When a business’ unit comes to the ICEC for support in developing innovative ways to enchant their customers, the visit always begins with a Customer Journey. A Customer Journey shows how technology can be used to delight ING’s customers in the present. Marketing department requires tends to favour client satisfaction rather than profit maximizing (Aaker, 2001). The first step for big companies is to start an inquiring report to analyze the targeted sector. Under the pressure of reducing costs and remaining competitive, more companies are attracted to the efficiency of e‐commerce. Related to the users and Internet providers, companies have noticed an increase of the importance of Internet, in the development of the business environment. As Gallo (2011) said, Steve Jobs was mindful with creative domains and hired young creative people to manufacture dreams. The ING Strategy for long‐term is “building the Bank of the future". This starts with a focus on the customer by restoring trust through transparency and fair pricing, excellent services and solutions, by improving efficiency as a competitive imperative edge under the regulatory cost pressure. Ian McDonald Wood (Rosca, 2006) said that network economy creates a significant added value through digital technologies, human and organizational connections. Now, due to the technological changes in bank’s services, IT systems are used at a large scale for the clients’ benefit. This means that banks services could benefit from improved technologies and use of new distribution channels, by using technology to maintain the competitive advantage.
3. Innovation trend in ING Bank Since ING Bank appeared on the market, it has received many awards: in 2003 it received a prize for interactive vocal phone call services for clients; in 2004 it was offered a bounty for “Self’Bank”. From 2005, ING Bank set out a purpose on long term to transform the Romanian subsidiary from a niche bank into a universal bank. At the end of 2010, ING Bank reached 1 million clients. Also, ING Bank was the 3rd provider for VISA Electron International Card with chip (EMV), with 1 million sold cards and the most used cards on POS transactions. The main opportunities for ING Bank in 2012 were: the highest level of emotional loyalty on the market, higher availability of ING to invest in better products. Several potential competitive advantages were identified: best client service, best services, best Internet banking, transparent communications and being easy to understand. At the end of 2012, ING Bank finished its run with the following awards: “Best European Bank of the Year”; “Best Global Structured Commodity Finance Bank”; “Best Bank in the Netherlands”; “Best C&EE Loans Mandated Arranger”, “Russia and CIS Loans Mandated, Arranger” and “Russia and CIS Loans Bookrunner”. That revealed ING Bank as a competitive bank with a very good brand tracking. ING Bank is one of the most important players in banking products. It is also a leader in the insurance market. In July 2004, ING Romania launched “Self’Bank”, a platform used by clients for banking services (money transfer, cash deposit, cash withdrawal, balance enquiry, credit top‐up, and utility bill payment). Its advantage was to provide full and permanent access for clients (24h/7days), without wasting time queuing, while maintaining low costs. After “Self’Bank” release, ING provided “Home’Bank”, a financial service available at any time and any place for online transactions. Since then, ING has reaffirmed a permanent engagement for Romania in order to respond to their client’s needs.
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Marta Christina Suciu and Cristina Andreea Florea The PR campaign dedicated to “E‐Banking” platform was distinguished with “Silver Award for Excellence” within PR Award 2012. The new website gives details in a user friendly and creative way for the Internet banking. It is meant for all clients who search information in order to simplify their life and save time while conducting their financial transactions. During the ten years of activity, ING has proved to be an open bank, building the reputation that promotes quality and innovation. Initially, it was electronic‐banking, followed by „Internet Banking”, and recently in Romania „Mobile Banking” has been introduced. All the three services offer almost the same facilities to the clients. The diferences between them relate to the freedom of movement and the communication chanel with the bank. Their benefits are: simplicity, comodity, security and lower prices. The costumers were not familiarized with this technology and for that, the bank pre‐ announcied them regarding the upcoming product to inform about its benefits. This had an important role because it educated clients. By working together for the same purpose in the Romanian market, banks attract customers with new products, more competitive, based on innovative IT solutions. With this aim, ING Bank attended in November 2012 the International Conference Operations & IT 2012 where CEOs from around the world gathered for the first time for such an event. The break‐out sessions focused on: sharing and building best practices that support customers and tackle security challenges; stimulation of innovative ideas and how to create an environment that inspires innovative entrepreneurship; increasing efficiency and operational excellence through the use of Shared Services. ING came up with a new project – Home’Bank for smart phones. “Mobile Banking” was launched at the beginning of 2012 using internal resources by a dedicated Romanian team. In October 2012, ING deployed a new technology – “Contactless for MasterCard and Visa”. With this card clients can easily pay up to 100 RON or 20 EUR without using a PIN Code. Payments over this limit will be made by using a PIN Code. This new product innovation uses a different technology in order to improve product benefits (Maarse, 2012). Considering innovation as a prerequisite, we tried to analyze and explain the correlation between innovation and its determinants based on ING Bank case study we conducted.
4. Our research study The objective of our study is to prove that innovation in products leads to a higher profit rate in the short‐run and creates sustainable competitive advantages on the long run. The objective is to measure the Internet Banking utilities and to improve its characteristics in order to create a competitive product. Seitz and Sticke (1999) in their paper „Internet Banking” demonstrate that companies who provide financial services use the Internet as a distribution channel. Online transactions are elements of efficiency, mostly because they tend to save time. The qualitative stage of our study uses focus groups and interviews. These discussions were conducted in Bucharest in February‐March 2013. The quantitative stage of our study is based on surveys. The sample includes respondents between 20 and 60 years (Figure 1), with higher education and income levels as showed in Figure 2. All the respondents use frequently at least one card and sometimes shop online. We have used quantitative methods for evaluating the relevant aspects of research. The main purpose of our research is to evaluate some market segments and how consumers rank products on the market. We applied the Likert scale for quantitative measuring. When answering to a questionnaire based on the Likert scale, respondents mentioned their level of agreement or disagreement. The scale has 10 levels: beginning with very dissatisfied and ending with extremely satisfied. The respondents had to give a mark for a series of banking products. This is why a symmetric agree‐disagree scale for a series of statements was used. Thus, the
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Marta Christina Suciu and Cristina Andreea Florea range captures the intensity of their feelings for a given item so the answers will be more specific and will not lack any piece of information.
Figure 1: The sample structure with respect to age
Figure 2: The sample structure with respect to the income level In the qualitative study, we started from the following aspects: defining the characteristics of the market, identifying emotional and behavioural traits that influenced the consumer’s decisions, trying to understand people’s needs, discovering relevant behaviour patterns in buying decisions. Assuming a bank designs a new online payment method. In order to create this product, bank representatives search a group of potential clients (people who usually make online payments) and discuss with them about their experience on Internet shopping; what they like or dislike related to these services. Then, based on their feedback, the bank managers debate all the possibilities to sell this new product. Beginning with these, in our measurement we focused on interviews with clients to identify their preferences on bank products and also in relation with other banks. We used a Utility Test in order to analyse the possibility to enhance products’ utility and to develop new technologies. The Utility Test is an important instrument of qualitative on‐line research because its objective is to discover the inconsistencies between expectations and necessities of users in order to solve their problems. Moreover, companies have to invest in their brands because youth are sensitive to this high degree of loyalty. They will also advise others to do so. Graham Brown (2011) considers that these are often youth with passions – sometimes extreme. Looking at the surveys, we used a regression function to analyze the dependence between innovation and its elements. We also used a correlation function to estimate the correlation index. Innovation is highly related to the flexibility of Internet Banking (with 9.55 score), simplicity in card use (with 9.66 score) and the possibility to
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Marta Christina Suciu and Cristina Andreea Florea make payments at the ATM (with 9.6 score). We have observed that consumers are sensitive to flexibility and simplicity in regard to these products. Approx 56% of respondents intend to use accounts for savings, deposits or investments. In doing so, they tend to intensify the Internet Banking usage, especially because all these operations can be made electronically. Consumers’ focus has changed from a superficial evaluation of banks (based on their notoriety and size) to a qualitative one (based on both emotional benefits such as easy to work with, transparent, trustworthy and rational product features). The interview meetings showed that clients prefer a safe bank even if it charges bigger fees. Our research illustrated that clients between 20 and 35 years are more open to new technology than others and use their cards more intensively. The next group of clients with ages between 36 and 60 years are more conservative and they use bank products in order to receive their salary or to create deposits. Thus it is clear that teenagers from 18 to 25 years must be targeted, because, in many ways they are integrated in social and financial life. Deutsche Bank analysts observed that the most users of e‐banking are young people. Companies must reach their devotion in order to steady the relation Business‐Client. This focus on companies to young people improves the products through technology. Nevertheless, products and services with bigger utility are regularly preferred and the utility/usefulness perceived accurately is usually the expression of a process of conception and execution, which took into consideration the needs of the target market (Gould, 1985). Afterwards they develop or create new technology to respond to the teenagers’ wishes. Teenagers represent an important sector of Romania’s population. They have different reasons for using a particular product in favour of the others, whereas the adults tend to keep their preferences. That is why large companies have made studies in order to analyze the teenagers in Romania so that they cater to their needs. Moreover, even Graham Brown (2011) confirmed that real innovation does not come from the product development departments or people with <innovation> in their job title, but from numerous social interactions and conversations with the market. Everybody wants to understand what makes good innovation, but very few people have asked why it happens and explored its social dimension. Young adults, from 18 to 30 years, do not have enough time to go to the bank, to speak with financial consultants about new products. This is happening because they work, study and they do not have time for this. Also, they are always connected to their friends, through all kind of technology and social networks; companies must keep up to their demands. Today’s youth are the most likely to carry their bank in their pocket. A growing number of older adults are likely to join them in the coming year according to Mountain View, Calif. From these surveys, it can be observed that about 73% of respondents are already using Internet’Banking. This fact influences the capability of the market to absorb new technology. For long term it educates the consumers and increases the efficiency of these services. In fact, the Internet’Banking solution is correlated to the saving accounts, deposits and personal loans. Clients must be sure that they have control over their money and that they could use it anytime. Online checking of the account and payment for utility bills are the favourite transactions performed via the Internet banking service. Morken (2012) noticed that regardless of age, each customer expects to connect with their financial institution in their own way. Referring to the clients’ satisfaction, about 90.9% of respondents are very satisfied with the easy way to make payments through Internet’Banking. At the same time, the possibility to make deposits at an ATM is a very important factor to clients’ satisfaction, stated 87.3% of respondents. Also, the possibility to make payments on the Internet is very appreciated in number of 93.2% of respondents. About 73% of respondents are already using Internet’Banking. Moreover, about 90% of respondents are pleased with the flexibility of Internet’Banking services. The advantages of using Internet’Baking rather than branches are the followings: time saving, comfortable payment solutions anywhere, anytime, modern payment solutions, full control over account, and safe payment solutions. Moreover, consumers were asked about possible improvements and the most frequent
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Marta Christina Suciu and Cristina Andreea Florea answers were: higher security, real time update for personal accounts transaction, more information about Internet’Banking, becoming easier to use, easier to authenticate and to acquire other products by Internet’Banking (as loans, credit cards), communicate directly with the bank. Because some clients feel unpleasant about the Internet’Banking token, ING Bank implemented a new way to spend less time on connecting to Internet’Banking. It is a new application for smartphones that allows client to log in one single time and since then they can stay online permanently, without proceeding log in again. This revealed that Internet is a very useful instrument in money transactions. Starting from this point of view, bankers should invest in this technology to increase the satisfaction of respondents, because in all three cases the difference between satisfied and very satisfied is about 50%. However, regarding the Internet’Banking flexibility, the situation is different. About 60% of respondents are very satisfied with it, and this may be due to the services and products that clients could use through it. In line with these, it is the assistance of Call Centre which shows that 25% of respondents are unsatisfied. Some factors that influence the percentage of the Call Centre satisfaction are: a small number of employees, few actions to resolve the customers’ problems, very low empathy for the clients, long time until answering and not a permanently open line for calling. The improvement in these elements could increase the percentage of clients’ satisfaction with more than 10%. Over all transactions performed through Internet banking, about 71% are checking online for accounts or transactions, 30% are paying utility bills, 29% are making RON transfers while 20% are making EURO banking transfers, 14% are recharging mobile phone cards, 9% are using it for loans administration. Observing these percentages, banks must invest in increasing transactions, because less than 50% of participants are using Internet’Banking for this purpose. Internet’Banking is perceived more as a way of being permanently informed about accounts’ situation rather than a channel for payments and transactions. The last year ascertained an increase of Internet users especially in product acquisitions via Internet. For checking accounts there are other services like Mobile Banking or Push Alert, which respond to this kind of necessities. Only 10% of clients are using Push Alert. Since the costumers are not familiarized with these instruments, the bank decided to inform all clients by email about it. In 2012, the electronic services market developed; thus it stimulated the on‐line payments in order to commercialize various services or products. Until 2011, Internet payments were not very secure, but in order to respond to all clients’ wishes, banks adopted 3D Secure and Verified Visa for transaction’s security. Therefore, the following year, these transactions have grown to a higher level, more transparent, easier and more secure. Last years, the virtual space developed very fast, producing new possibilities of informing and communicating between people. The business world won a lot of clients because it was easier to promote and sell products. The digital technology has changed the way of building and expanding a business. E‐commerce became an extensive field, offering a lot of possibilities not in the least expensive. This fast expansion of e‐commerce has created important opportunity for banking systems. More than 60% of clients chose a bank against another for commodity because it was closer to their house. For today’s consumer it is very important to access their money whenever they want, including Internet options (Wilson, 1996). The ATM locator is one of the latest improvements. More than 40% of respondents are now using this service to find an ATM, while 20% are calling the Call Center to find it. This service is also provided by Internet’Banking Services. On the other hand, reliability is outrunning innovation by 10%. We can observe that confidence is a very important basic factor; it is a must‐have element, while innovation is a matter of bank’s choose, what clients receive from a bank in terms of quality and satisfaction. Related to innovation, about 65% of respondents choose a bank because of Internet payments and cash in ATM. Furthermore, about 62% of clients are using Internet’Banking to pay bills, while 22% pay bills at suppliers. By studying this behaviour pattern, managers can attract some clients from the last category by using marketing and campaigns. As well, the bank could promote the Direct Debit possibilities for bills,
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Marta Christina Suciu and Cristina Andreea Florea spending less time on roads and avoiding crowds. About 16% of respondents are now using Direct Debit service. Only 6% of clients are paying bills scanning by mobile phone ‐ this is the newest trend in mobile payments, and also the easiest. For the next question – What is the easiest way to pay the bills? –, the customers answered as it follows: on the first place Internet’Banking, on the second place Call Centre and on third place, scanning the bills. Regarding payments, about 50% of respondents said that the easiest way to pay the urban transportation fare is with a Contactless Card, without introducing the PIN code. ING Bank has already provided this product so it expects to increase this product usage. Also, an important number of respondents said that the POS is the easiest and safest way to buy tickets. So, we could predict that the number of people, who will use these two possibilities to pay tickets, will increase in the near future. Companies should educate the consumers, by using PR activities to promote the benefits of the new product or technology. This affects the company’s product portfolio and the long‐term competitiveness. The banks, which understand that technology is the main channel to ensure development and attract clients, will be more competitive on the market. In Figure 3, we compare the competition between banks and their offers. Referring to Internet’Banking solutions, ING Bank stands ahead of other banks with 49% of this service attached to basic products.
Source: based on our research Figure 3: Distribution of Internet Banking, savings account and deposit on the competitor banks Starting from this overview, ING Bank focused on this young sector of clients and on adults who receive a salary or who want to save money. To reach a new level in the top of the most wanted banks from Romania, ING Bank focused on developing the products that can address the client’s requests. Accordingly, ING Bank began to invest in enhancing Home’Banking and promoted the debit card among its clients. Thus they sold a new package formed by a debit card and Home’Bank. Clients want the ATM to provide all the services they need, instead of the office consultants, as ONE STOP SHOP. The Self’Bank is an ING Office zone, where clients can access all bank services 24/7 without any expenditures and wasting precious time at the bank. In Self’Bank clients can make withdrawals and deposits, can purchase new products and open accounts and can make all kinds of payments. The surveys reveal that 62% of respondents want to spend less time at banks offices. Overall, ING Bank has an augment of the Gross Profit, which evolved from 84 Mil. RON, in 2009, to 232 Mil. RON, at the end of 2012.
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Marta Christina Suciu and Cristina Andreea Florea This study helps different companies to redefine their developing strategy and think out this process for investing in new technologies.
5. Conclusions Our findings showed that companies, especially banks, need innovation as a prerequisite for a long‐term development. As you could see, the majority of innovations come in the form of new technologies in order to enhance the product’s value. These variables as Self’Bank, ATM with cash in, Internet’Banking, Mobile’Banking, product’s quality, and secured payments have a very strong influence on financial market. Moreover, these correlations determine bank efficiency. Now more than ever, we need modern technique to stimulate creative thinking and also adequate environments in order to lead to innovation. Firstly, the necessity of innovation in financial markets was measured by the utility test of Internet’Banking. It was observed that this instrument was extremely important for consumers, banks and the indexed in gross profit. ING Home’Bank was recognised as the best electronic banking product, on a bench marketing study over online banking. The necessity of innovation has been justified on the change of the consumer’s preferences. In both cases, we observed the correlation between innovation and other issues about intellectual capital, creativity, intangible asset, and technology. More and more companies realized that if youth are correctly understood, they could become the most efficient instrument of a company. They are innovation agents because they are living on shuffle mode and never stop, and for that they are involved in the value chain from the product conception stage till the end stage, even the sales phase. But in order to enter their lives, companies must be flexible and take risks. The research study analyzes all responses of interviewees, with the nexus on innovation. The possibility to make deposits at the ATM, the Self’Bank services, the easy way to make payments, payments being made via Internet, can be summed up in one word, innovation. This is the reason why consumers choose bank such as ING Bank. The expansion of these areas using intellectual capital and innovation ensure long‐term competitive advantages.
References Aaker, David A.; V. Kumar; Day, George (2001) Marketing Research, John Wiley & Sons, Inc., New York. Brown, G.; Dhaliwal, J.; Benjamin, F. 2011, Youth Marketing Handbook, available online at http://www.slideshare.net/mobileyouth/the‐youth‐marketing‐handbook‐download. Burns, Alvin; Burns, Ronald (2008) Basic Marketing Research, Second Ed., Pearson Education, New Jersey. Chesbrough, H. W. (2003) Open Innovation: Researching a New Paradigm, Oxford: Oxford University Press. De Bono, E. (2010) Lateral Thinking / Gândirea laterală, Curtea Veche, Bucharest, pp. 39‐219. Dingli, S.M. (2002) Creative Thinking. An Indispensable Asset for a Successful Future, Malta University Press, Malta, pp. 8‐ 157. Dingli, S.M. (2007) Creative Thinking. Designing Future Possibilities, Malta University Press, Malta, pp. 59‐138. Gallo C. (2011) Steve Jobs‐ Innovation secrets / Steve Jobs – Secretele inovației, Curtea Veche, Bucharest, pp. 47‐184. Gould J.D Lewis (1985) Designing for Usability, Key principles and what designers think, Communications of the ACM. Maarse, J.H; Bogers, M. (2012) An Integrative Model for Technology‐Driven Innovation and External Technology Commercialization. Open Innovation at Firms and Public, Hershey, PA: IGI Chapter 4, pp. 59‐78, available online at http://www.ssrn.com. Marous, J. (2011), Consumers Are Increasingly Using Multiple Devices to Support Banking Needs, available at http://www.banking2020.com/category/data‐and‐research/banking2020. Noack, K (2002) Creative techniques / Tehnici de Creativitate, All Educational, Bucharest, pp. 40‐115. Peters, T. (2010) Innovation circle / Cercul inovatiei, Publica, Bucharest, pp. 191‐399. Rosca, I.Gh. (2006) Knowledge society / Societatea cunoaşterii, Economica, Bucharest, pp. 95‐105. Power, J.D. (2012) Canadian Retail Banking Customer Satisfaction Study, Press Release, available at http://www.jdpower.com/content/press‐release/u3sOSZa/2012‐canadian‐retail‐banking‐costumer‐satisfaction‐ study.htm. World Bank, 2013. Knowledge Economy Project available at http://www.worldbank.org/projects/P088165/knowledge‐ economy‐project?lang=en.
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The Creative Society, Urban Revitalisation in the Creative Economy and Society: The Romanian Case Marta‐Christina Suciu and Mina Fanea‐Ivanovici Bucharest Academy of Economic Studies, Department of Economics and Economic Policy, Bucharest, Romania christina.suciu@economie.ase.ro mina.ivanovici@economie.ase.ro Abstract: Over the past decade we have been the witnesses of great changes in our society – both nationally and worldwide. The economic crisis has been present in most of the countries – in Europe and across the world as well, there has been great concern about security and fight against terrorism in an attempt to make places safer, the indebtedness degree has reached unprecedented figures both among consumers and among nations and many other such shattering events have recently taken place. Despite all these changes and general turmoil, there has been a constant evolution and development of several industries. This is the case of the creative and innovative industries, which did not suffer from the merciless effects of the economic crisis, but managed to cope with financially tough times and yield profits and welfare for the places where such businesses are run. In this context, such activities were some of the few engines running in order to re‐create the previously favourable economic conditions. One outcome of the creative and innovative industries was the advent of a new sub‐society – the creative society. Much has been written about and debated upon the creative class so far, but little is there said of the new creative society, which brings about new forms of creative expression along with urban revitalisation, creativity‐driven gentrification and dislocation. It is the creative and cultural activities in certain regions that spurred such demographic changes attributable to the creative population. It is commonly agreed that creative and cultural activities have a definitely positive effect on economies, especially in developed countries. However, there has been recent concern that such activities may eventually lead to certain adverse effects, which question the extent to which potential growth and development can be explained by the activity of the creative people and of the creative society. The paper starts with a brief literature review including the main schools of thought in terms of growth; it then advocates for the development of the creative sector as a prerequisite for urban regeneration. We then discuss the concepts of gentrification and dislocation in the same context of the creative and cultural activity. Shortly, the paper aims to explain and underline the main demographic changes due to the advent of the creative society and, on the other hand, to identify and discuss the main menaces faced by creative societies stemming from the very nature of activity. The paper treats the Romanian case of urban vitality in particular. Keywords: creative society, urban regeneration, gentrification, urban revitalisation, Romanian urban vitality
1. Brief literature review At international level, in the current economic context, there is a growing connection between culture largo sensu and prosperity, between creativity and development, between cultural activities and urban regeneration, but there are, subsidiarily, social and demographic implications of the creative & cultural economy, which have become more and more obvious. After all, during the past years we have been the witnesses of the advent of a new class, the creative class, but also of events that have huge long‐term impact, such as the Internet bubble, the 9/11 attacks and the economic crisis that started in 2008 (Florida 2002) and which, in many regions across the world, are still present and are getting more serious. According to the neoclassical theory, growth has been seen as being determined by the accumulation of physical and human capital, while according to the endogenous growth theory, growth has been seen as a process linked to the features of the place, as it is the case for innovation, knowledge and human capital. Neoclassical theories rely on capital accumulation, as in the case of Solow (1956) and Swan (1956) and technology has been seen as exogenous (Barro, 1997), and therefore not included in models. Technology has been brought into the models via the inclusion of R&D theories, as in the case of Romer (1990), Barro and Sala‐ i‐Martin (1995). All these theories state that economic growth can be explained via the stock of physical capital, human capital, and innovation. While much more attention has been given to their analysis at national level, the regional and local dimension should not be neglected. In the case of innovation, the interaction among economic agents and the exchange of ideas require social capital.
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Marta‐Christina Suciu and Mina Fanea‐Ivanovici According to neoclassical theories, growth in the long term is based on the continuous technological progress in the form of new goods, markets or processes (Aghion and Howitt, 1998) and it can be mathematically expressed as a function of capital accumulation under the assumption of perfect competition and diminishing returns (Solow, 1956; Swan, 1956). The R&D theories were introduced and imperfect competition was factored into the model (Romer, 1990). Despite the economic and political events that took place and shattered at an unprecedented pace the whole society during this period, the forces of the creative class grew continually stronger so that right now one can speak of the existence of a new social class called the creative society (Florida 2012). According to the Creative Economy Report 2010, the creative industries have been more resilient during the economic crisis than all other traditional manufacturing industries. The previously mentioned report reveals that from 2002 to 2008, exports of cultural and creative products and services more than doubled in the United States of America. This entire trend took place while there was registered a 12% drop in the foreign trade in the year 2008 right at the beginning of the world economic crisis. It is the existence of such important political and social events that should have pulled down any other bubbles in the social and economic landscape. However, the creative class stood this test and grew stronger, thus building the foundations of the creative society. According to Allen Scott (Scott 2000), [C]ities have always played a privileged role as centers of cultural and economic activity. From their earliest origins, cities have exhibited a conspicuous capacity both to generate culture in the form of art, ideas, styles and attitudes, and to induce high levels of economic innovation and growth, though not always or necessarily simultaneously. As we enter the twenty‐first century, a very marked convergence between the spheres of cultural and economic development seems to be occurring. This is also one of the distinguishing characteristics of contemporary urbanisation processes in general. Places in general (and cities in particular) are closely or even symbiotically connected to what we generally call culture. Culture has the tendency to distinguish itself by the place in which it is generated, which makes cities or regions distinguish themselves among the others by the activities that generate symbolic products and services. In Europe, economists talk a lot more about the closer relation between urban and rural spaces, while in the past, urban and rural areas were seen in permanent competition. In this context, authorities’ plans include the preservations of green areas around urban areas and preventing the phenomenon that merges small urban areas into bigger ones (Wheway 2011). Thus, Hadjimichalis (Hadjimichalis 2003) points to the fact that the new urban middle classes consume and use both urban and rural space, living, on the one hand, in towns and owning, on the other hand, a second dwelling in rural areas or living in rural areas and working in urban areas. The cultural or symbolic economy influences the contemporary urban landscape. Its new structure is due to the mostly indirect interaction, facilitated by modern communication means, while physical geographical borders as well as organisational borders become more fluid and flexible due to the said communication means. The creative cultural sector has undergone a change of vision from non‐profit fields of activity, which were frequently subsidised of financed by the local or central budget to a strong focus on profitability, marketability and market share. Such an evolution reflects the adaptability of creative cultural fields to the urban space, which is in a continuous changing process itself, in an attempt to survive and produce value added; great companies financed by the state budget tend to be replaced by small‐sized competitive firms having well‐determined profitability objectives that can be backed and belong to social networks. Eventually, culture is not the appanage of the executive power, but the fruit of imagination, creativity, spirituality and individual effort as an
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Marta‐Christina Suciu and Mina Fanea‐Ivanovici exponent of a society, at a certain time moment (Stern, Seifert 2007). Around such networks cultural clusters are born, about which Evans (Evans 2004) claims to have three arguments: economic, social and cultural. Table 1: Rationale for cultural clusters Economic rationale for cultural clusters ‐ industrial regions ‐ management of work space ‐ production chains – ex media, television ‐ production networks ‐ technological transfer – ex Silicon Valley
Social rationale for cultural clusters ‐ neighbourhood revitalisation ‐ urban villages ‐ community arts ‐ urban regeneration ‐ collective identity ‐ artistic and social inclusion ‐ social networks
Cultural rationale for cultural clusters ‐ artistic regions ‐ artistic studios and galleries ‐ new media ‐ ethnic arts ‐ local cultural strategies ‐ art schools and artistic education ‐ cultural agents ‐ creative capital
Source: Evans 2004. Cultural industry quarters: From pre‐industrial to post‐industrial production. In David Bell and Mark Jayne, eds., City of quarters: Urban villages in the contemporary city. Aldershot, Hants, England; Burlington, VT: Ashgate. According to Grams and Warr (Grams, Warr 2003) artistic activities develop urban areas in three directions:
They offer access to resources (by attracting clients‐consumers of cultural products and services – be them local or foreign from that region; by using urban facilities and abandoned/underused spaces; by creating new relations; by supplying new resources that can be used by residents too; by educational value added offered to the young community members; by enhancing qualifications and access to various equipment; by enhancing the access of the young population to the development of technical and entrepreneurial skills);
They help solve problems (by formulating local problems and offering the opportunity of having an intercultural dialogue; by increasing the safety and opportunities to build new skills; by using the creative capacity of inhabitants for the purpose of solving problems; by increasing cooperation and collaboration; by getting young people involved in civic actions);
They contribute to the development of social networks (by developing leadership and decision‐making skills and abilities; by building cultural identities for people coming from other places and settling in the urban area; by supporting the democratic process; by developing peaceful relations; by surpassing cultural boundaries in dialogue and communication; by increasing the level of civilisation of that region; by creating a spirit of belongingness to that place; by creating new opportunities for the citizens in general; by building bridges among social classes).
Despite the obvious benefits of cultural and creative economic development, this can impact the society in a negative manner, and such manners evolve under the form of two processes: gentrification and dislocation.
2. The processes of gentrification and dislocation Coined by Ruth Glass (Glass 1964) in the year 1964, the concept of gentrification denotes the penetration of the middle class in towns or neighbourhoods that had been previously been inhabited by lower social classes. This concept highlights strong class inequalities and injustice and is often associated with the concept of displacement. In this context, it is highlighted a new type of gentrification, hereinafter referred to as urban revitalisation in order to avoid the negative connotations mentioned by Marx and Engels. Urban revitalisation involves more than a simple migration of the population to certain urban areas to some other urban areas. The penetration in the urban area and the development of the creative class have deep implications from a social, economic and cultural point of view, by the advent of IT hubs, artistic centres, tourism programmes etc., according to Edwards (in Imrie et al 2007), including urban areas that had previously been deprived of the influences of new technologies and culture. Stern and Seifert (Stern, Seifert 2007) add up a new inconvenient to cultural revitalisation, that of increased economic inequality. This is actually the concept of “the winner takes it all”, according to which people with best developed abilities and skills get the highest market share or the highest proportion of income in a certain field of activity.
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Marta‐Christina Suciu and Mina Fanea‐Ivanovici Although this situation may look like a natural outcome of competition taking place in that field of creative cultural activity, the fact that the number of jobs increased significantly in this area seems to turn the market into a lottery with one or few winners, generating strong inequalities in a continually growing and expanding community. The same aspect was remarked by Richard Florida (Florida 2005), who considers that this is a dangerous dynamics that societies are developing. Although Richard Florida has been the promoter and supporter of the creative industries in order to stimulate economic growth, social disparities determine us to reconsider all of the above, especially when negative effects start to occur more and more obviously.
3. Urban vitality in Romania Urban vitality has been studied in Romania as well. In 2010, a report on this matter was published by the Centre for Research and Consultancy for Culture, entitled “Cultural Vitality of Cities in Romania 2010” (Centre for Research and Consultancy for Culture 2010b). The aim of the study was to analyse the cultural potential at local level in the main big cities in Romania (46 county capital cities with a population of over 50,000 inhabitants) and it used data offered by the National Institute of Statistics, Ministry of Finance, Trade Register etc. Using a set of six categories measured for several cities in Romania (infrastructure of the cultural sector, specialised human resources, budget expenditures for culture, cultural activities‐participation; creative economy and non‐profit sector), a ranking was established. The capital city Bucharest was not included in this ranking. An urban vitality index was computed for each city. The index was computed as a weighted value using the number of inhabitants. The said raking is described below: Table 2: Urban vitality index for the best performing cities in Romania Romanian City (excluding the capital city, Bucharest) 1. Cluj‐Napoca
Urban vitality index (descending) 1.09
2. Sibiu 3. Sfântu Gheorghe 4. Timişoara
0.88 0.86 0.84
5. Alba Iulia 6. Iaşi 7. Bistrița 8. Târgovişte
0.57 0.56 0.52 0.47
9. Miercurea‐Ciuc 10. Târgu Mureş 11. Constanța
0.44 0.36 0.34
12. Oradea 13. Craiova 14. Piatra Neamț
0.33 0.27 0.25
15. Braşov
0.17
Source: Centre for Research and Consultancy for Culture, Cultural Vitality of Cities in Romania 2010, 2010, Bucharest The concern for cultural activities in urban areas in Romania is proven by the various empirical studies whose main purpose is to estimate the dimensions of creative‐cultural production activities, but also those of cultural consumption. Thus, in the year 2010, the Cultural Consumption Barometer 2010 was drafted (Cultural Vitality of Cities in Romania 2010 2010a). The chapters of this study were: domestic consumption, public consumption, changes in the cultural consumption between 2005‐2010, consumption preferences and profiles of non‐ consumers of activities related to high culture (first part) and cultural practices of the population in Romania, analysis of dynamics, tastes and acquisition of written culture (mainly books) in the second part of the study. As concerns the contribution to the Romanian cultural and creative sector to the Romanian national economy, the Creative Economy Report 2010 reveals that the 2003 turnover, all industries included, was 2,205 million
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Marta‐Christina Suciu and Mina Fanea‐Ivanovici euros, while the value added to the national GDP (all industries and sectors included) was 1.40%. These values place Romania 20th in a total of 30 countries (EU plus Iceland, Norway and Liechtenstein) as concerns both the first indicator (one country having similar turnover being Slovakia) and the second indicator (counties having similar weights being: Hungary, Poland, Portugal, and Bulgaria. The same report lists several main trends on the Romanian market for cultural products and services:
Romania has become an attractive location for making movies, videos and ads;
the local film industry has been affected by the reduction of the number of cinemas from more than 4000 to less than 100 in just 20 years, leading to a fall in audience to 1/45;
there has been an increase in the production of long, medium and short films and TV serials;
an expanding advertising industry;
the development of programmes meant to stimulate crafts and artisans’ activities;
Also it should be noted that Romania’s exports of creative goods between 2002 and 2008 grew by 7.13%, th which places Romania on the 19 position in EU 27. As far as exports of advertising and related services are nd concerned, Romania ranks 2 with a growth rate of 72.57% for the above‐mentioned period in EU 27, following just after the growth registered by the Czech Republic, of 79.63%. Romania ranks 2nd as well after the same country as concerns the growth rate of exports of Research and Development Services, with a growth rate of 62.29%.
4. Conclusions The paper brings into discussion the role of innovation in the context of economic growth and regional development and it provides a short review of the literature in this respect. The case of Romania is discussed in the matter of urban vitality generated by the creative sector. Although empirical, this analysis is meant to discuss the place occupied by the creative sector in the Romanian economic landscape in order to assess growth oppotunities and design economic policies in the field. Despite not all Romanian industries and sectors are well represented in the world ranking, such figures show there is huge potential to be exploited for the Romanian cultural and creative economy to thrive, for the creative class and society to develop and for the global trade to benefit from the Romanian tradition and experience. In conclusion, our opinion is that the Romanian society is due to undergo major positive changes if sustainable effort is made in the direction of the creative and cultural activities and places too will thrive and attract foreign and local tourists and act as an economic growth and development determinant.
5. Reference Aghion P., Howitt P. (1998), Endogenous Growth Theory, the MIT Cambridge. Barro R. (1997) Determinants of Economic Growth, MIT Press. Barro R., X. Sala‐i‐Martin (1995) Economic Growth, Ney York, McGrew Hill. Buesa, Mikel.(2010) “The determinants of regional innovation in Europe: A combined factorial and regression knowledge production function approach”, Research Policy (0048‐7333), Vol.39, Iss.6; p.722. Centre for Research and Consultancy for Culture (2010a) Cultural Consumption Barometer 2010, Bucharest Centre for Research and Consultancy for Culture (2010b) Cultural Vitality of Cities in Romania 2010, Bucharest Drivera, C.; Oughtonb, C. (2008) “Dynamic models of regional innovation: Explorations with British time‐series data”, Cambridge Journal of Regions, Economy and Society, Vol.1,Iss.2;p.205. Imrie, R. and Raco, M., (2007) Urban Renaissance? New Labour, community and urban policy, The Policy Press, Bristol Florida, R. (2005) The Flight of the Creative Class: The New Global Competition for Talent, HarperCollins, New York Florida, R. (2002) The Rise of the Creative Class, and How it’s Transforming Work, Leisure, Community and Everyday, Life Basic Books, New York Florida, R. (2012) The Rise of the Creative Class Revisited, Basic Books, New York Glass R. (1964). London: aspects of change, MacGibbon & Kee, London Grams, D. and Michael W. (2003) Leveraging assets: How small budget arts activities benefit neighborhoods, Richard H. Driehaus Foundation and The John D. and Catherine T. MacArthur Foundation, Chicago Hadjimichalis, C. (2003) “Imagining Rurality in the New Europe and Dilemmas for Spatial Policy”, European Planning Studies 11, pp. 103‐113. Rodríguez‐Pose, A., Crescenzi R. (2008) “Research and development, spillovers, innovation systems, and the genesis of regional growth in Europe”, Regional studies, 42 (1). pp. 51‐67. ISSN 0034‐3404. Romer P. (1990) “Endogenous Technical Change”, Journal of Political Economy, vol. 99, pp. 72‐102.
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Marta‐Christina Suciu and Mina Fanea‐Ivanovici Solow R. (1956) “A Contribution to the Theory of Economic Growth”, Quarterly Journal of Economics, vol. 70, pp. 65‐94. Scott, A. J., (2000) The cultural economy of cities: Essays on the geography of image‐producing industries Sage Publications, London Stern, M. J. and Seifert S. C. (2007) Culture and Urban Revitalization: A Harvest Document, Social Impact of the Arts Project, School of Social Policy & Practice, University of Pennsylvania, Pennsylvania Swan T (1956) “Economic Growth and Capital Accumulation”, Economic Record, vol. 32, pp. 343‐61. UNCTAD (2010) Creative Economy Report 2010. Creative Economy: A Feasible Development Option Wheway, C. J. (2011) The Transformation of English Market Towns: Gentrification, PhD Thesis, University of Leicester, Leicester
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Strategic Innovation – Access Path Towards a New Paradigm of an Academic Career Management Marta‐Christina Suciu1, Irina Dumitrescu2 and Andrei Dumitrescu2 1 Academy of Economic Studies, Bucharest, Romania 2 Petroleum‐Gas University of Ploiesti, Ploiesti, Romania suciu31@gmail.com irinadumitrescuupg@yahoo.com andrei.upg@gmail.com Abstract: This paper highlights the present vulnerability of the teacher’s status, which has comprised the Romanian academic environment, affected by a material – but also legitimacy – crisis. The importance of Universities for knowledge creation and distribution, minimized due to a tendentious “political arithmetic”, needs a notable resuscitation, which cannot be generated if we are not aware of the fact that – in a knowledge based society – education needs teachers not only in the hypostasis of lackeys of the system, but also in the quality of decision‐makers, counsellors, critic and lucid consciences. The Petroleum‐Gas University of Ploiesti (PGUP) has been the ground selected for the case study achieved. The investigation instrument used has been the questionnaire, applied to a representative number of academics, its structuring following edifying target‐zones, such as: opportunities of investing in the own education, aspirations and needs system, organizational culture and perception of changes. Multiple choice closed questions alternate with interrogations characterized by scaled answers, their analysis implying a pragmatic, multidisciplinary reference model, with a synchronic development, based on interpretative‐constructivist methodological inter‐conditioning. The validation of an obvious statutory injuring of the academic profession requires a strategic‐managerial reorientation of innovative type, triggering a proactive behaviour of the employees by exiting the status‐quo and confronting the provocations. Therefore, the aggregation of a new combination of resources imposes itself as a need which could create sustainable opportunities to the employees, the key of success in applying such strategy being the maintenance of the innovation spirit always awake. Some strategic management directions are thus outlined and intended for improving the negative perception of the teaching staff from PGUP regarding the opacity of the institution’s management or the controversial reform of the Romanian educational system. Consequently, the transition from the traditional management based on conformation and control towards an innovative‐creative institutional “philosophy” is needed. The redefinition of the personal effectiveness style by urging the academic staff towards self‐reflexivity, the creation of a supportive culture and getting closer the interaction model “management – employees” to the participative management pattern – are elements that contribute to the crystallization of an innovative organization, capable of “empowering” the employees by cultivating the “confidence capital” and turning the institutional policy towards humanism. Keywords: knowledge management, creativity culture, control philosophy, career management, proactive behaviour
1. Introduction The present vulnerability of the academic staff status, which has comprised the Romanian universities, affected by a material – but also legitimacy – crisis, has been the starting point of our research, aimed at evidencing innovative strategies for academic career management in such context. The concrete elements are rendered by focusing our investigation on both causes and effects prejudicing the career perspectives in a limited context, generated by the institutional space of the Petroleum‐Gas University of Ploiesti (PGUP). The problems investigated constitute a pressing necessity due to the flagrant affectation of the accessibility and proximity of the career development at the level of the academics – a socio‐professional category drastically affected by the present economical, social and political background. The case study performed has been therefore dedicated to finding answers to interrogation marks regarding: the social dynamic of needs creation; the way in which the aims–means relationship affects the individual value system; the part played by the perception effects – the frustration and satisfaction are not universal invariants of human nature, but relative data, amplified by the uniqueness of social subjectivity. The subject of our investigations did not enjoy much attention in other studies available in recent literature. From the similar works, we mention the analysis by Oakes (2002) of the academic career management from a marketing perspective, and an essay by Köster (2002) dealing with the influence of rapid changes upon career management. Bexley et al (2013), based on a large scale survey of Australian academics, exploring their motivations and career plans, have noticed a prevalent intent to leave the higher education sector for other works or for overseas universities, as a result of the unplanned diversification of academic roles. Musselin
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Marta‐Christina Suciu, Irina Dumitrescu and Andrei Dumitrescu (2013) analysed the changes in the relationships between academics and universities that have been profoundly affected by the recent reforms undergone by the European higher education systems and she tried to understand and interpret these changes using four different approaches. Other studies refer to the advantages of a proactive behaviour: Chiaburu et al (2006) evidenced a positive relationship between proactive personality and career self‐management behaviour, mediated by career resilience. The benefits of a proactive employee – in terms of stronger trust relationship with colleagues and increased creativity – are pointed out by Gong et al (2012).
2. Research methodology The multifaceted approach of the stimulating or inhibiting factors acting within the educational process implies an inter‐disciplinary, pragmatic reference model with a synchronic development, based on interpretative‐ constructivist methodological inter‐conditioning. The information has been collected by means of the direct research method, using the questionnaire as investigation instrument; it has been applied to a significant number of the members of the academic staff from our University. Based on the size of the PGUP academic personnel (330 persons) and considering a significance threshold of five percent, the sample dimension necessary for our research has been calculated. The simplified formula proposed by Bacali (2002, p. 34) has been used and the size found has been 178 subjects. The selection of the subjects has been made by stratified sampling, as a function of the academic rank, age and length of service within PGUP. Consequently, a representative fraction from each sample‐segment has been investigated. The data have been collected in the period February – March 2011. A number of 190 questionnaires have been distributed, 147 valid forms being returned. The questionnaire structuring implied the focalisation of our interest on significant target‐zones such as: opportunities of investing in the individual’s own education, aspirations and needs system, possibility of following and accomplishing the personal projects, organizational culture and perception of changes within the organisation. An ample interpretative analysis of the five multiple choice closed questions has been performed, based on the graphical representation of the answers distribution (shown in Figures 1‐5). The two questions with scaled answers have implied a statistical processing of the experimental data, followed by an analysis from the psycho‐managerial perspective. The statistical analysis results are summarised in Tables 1 and 2, indicating the mean values, M, and the standard deviations, SD, of the items studied. Although the analysis was limited as it has fructified the subjective perceptions of a rather reduced number of respondents, its valorisation is relevant as it points out the peculiar motivational specificity of the academic staff.
3. Results and discussions The goals and projects followed by each academic staff member operate as landmarks, with a precise intentionality in crystallizing his professional trajectory. They are however permanently contaminated by the constraints or opportunities of the working ambiance, and their fulfilment probability reflects itself in the concept called locus of control of each person.
3.1 Multiple choice questions According to Figure 1, the following distribution of the questioned academics is ascertained, from the perspective of the assignment of the events marking his career development: 16.3% (answering option A) are affected by internal causal forces or dispositional factors; 40.1% (options C and D) are influenced by external or situational factors; the majority (41.5% ‐ option B) declare the intervention of a combination of the two categories of factors. These observations lead to the statement of a numerical superiority of the subjects for which destiny, chance, the power of the others create the framework of external control, to the detriment of the respondents which possess an internal locus of control, establishing a doubtless causal relationship between behaviour and reward. The explanation of professional success and failure experiences thus differentiated interpretations, corresponding to the approached dominant, and delineates distinct demeanour patterns, in virtue of the mechanism of behavioural strengthening.
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Marta‐Christina Suciu, Irina Dumitrescu and Andrei Dumitrescu 45.00
41.51
Responses frequency (%)
40.00 35.00 30.00 25.00 20.00
20.06
20.06
16.33
15.00 10.00 2.04
5.00 0.00 A
B
C
D
E
Answering option
Figure 1: Responses distribution for Question 1: To what extent is it possible to follow and satisfy your professional objectives within your institution? A ‐ professional achievement exclusively depends on the individual competence, determination and tenacity; B ‐ professional achievement is based on values criteria, but corroborated with the chance of application; C ‐ individual objectives are permanently jeopardized by the constraints of the social environment; D ‐ promotion is often based on subjective criteria, uncorrelated with the values; E ‐ I do not have objectives Consequently, we state that, within PGUP, the decrease of work instrumentality and of the role of the individual effort in outlining the destiny experiences an aggravating acceleration tendency, by perceiving an advanced intrusion of socio‐political, external events. Such fact contributes to the development of a demotivating attitude due to conjunctural changes (blocking jobs, supplementing with new requirements the advancement conditions etc.). Even if singular, the aspect of tough demotivation induced by the choice for the last option cannot be neglected, because it expresses the significance loss of the activity performed, by a subject transposing injured motivation in detachment. This situation, even if isolated, raises question marks regarding the managerial policy of the institution, wishing to have conformist, submissive, good executants, but not also motivated employees. Finally, we underline the prevalence of the external locus of control in the professional field of the respondents, which marks unfavourably the possibility of developing an adequate career management. The next interrogation illustrates the measure in which harmonization of the individual goals with the ones of organisational type acts as restraining or stimulating element at the level of the possibilities of the academic career management. Observing Figure 2, we note a major detachment of the consensus of the respondents, corresponding to the option granting priority to the institution’s objectives (63.3%), fact forcing the teaching staff to adhere to the value system proposed at a central level. Supplemented by a worrying percentage (16.3%) of the academics perceiving a profound discordance between the two types of goals and by a quasi‐ absolute lack of options for the alternative that attests the maximum preoccupation for people, we draw the conclusion of perceptions making a priority from the major concern for performances of the institution’s management, authority‐compliant. This state of facts is due to the statutory identity of PGUP, a „state‐owned institution” depending upon the decisions of the Education Ministry, the main paymaster of the University budget. It is thus facilitated – even if, theoretically, the university autonomy exists – the inversion of priorities, with motivational valences, at the level of the academics that suffer the precarious salaries, the difficult advancement possibilities which would allow self‐achievement, but, mostly, their treatment as simple instruments in the race for research, capable of supplementing the financing.
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Marta‐Christina Suciu, Irina Dumitrescu and Andrei Dumitrescu 70.00
63.27
Responses frequency (%)
60.00 50.00 40.00 30.00 16.33
20.00
16.33
4.08
10.00 0.00 A
B
C
D
Answering option
Figure 2: Responses distribution for Question 2: How is the compatibility between the personal goals and the ones of the institution achieved? A ‐ there is a profound discordance between the two goals; B ‐ the individual goals are identifiable with the organisational ones; C ‐ the organisation is used to serve the own finalities of the employees; D ‐ the goals of the institution have priority All these obstacles are transposed in a collective sentiment of limitation of the individual’s accessibility regarding his career perspective. Consequently, given that „the expectancies create social reality and, even if unauthentic, [these] end by becoming true” (Gavreliuc, 2006, p. 151), the necessity of a supplementary preoccupation at the organisational level imposes itself, by implementing a strategic line of managing through motivations, concretized by means of both a motivating organisation of the teaching staff activities and an integration of individual motivations in the human resources management system. The following question investigates the attitudinal orientation of the subjects, corresponding to the personal modality of perceiving the bipolar structure individual – organisational environment. The behavioural modelling presents, within the respondents, as generating sources, three distinct elements: compliance, identification and internalization. From Figure 3, we note thus the preferential orientation (40.8%) of the academics to cultivate a compliance „philosophy”, based on the possibility of recompense and sanction, fact “strengthening” the assumption previously demonstrated – the perception of an authority‐compliant managerial pattern. Such thing proves the fact that, beyond the “affective commitment”, an instrumental transaction of the type “involvement against reward” manifests itself. Nevertheless, the alternative of an alienating‐type involvement must not be ignored, in which the individuals perceive themselves as being incapable to control organisational experiences. This observation is sustained by the quasi‐uniform distribution of the orientations toward identification (26.5%), corresponding to the relevance of the group for the socio‐professional identity of the subject, respectively internalization (18.4%), based on the individual attachment to the university culture, instituted as personal demeanour standard. We also remark a reduced fraction (14.3%) of respondents belonging to the category of the “deviants” from the norms, potential resource for innovation and testing. The interpretative implications of these answers demonstrate the uncertainty state of the teaching staff regarding their career evolution, which inevitably implies assuming conformation. The results summarised in Figure 4 draw the attention towards the interpretation of the perspective from which the academics perceive the university as a privileged access to self‐identity. The central tendency of the respondents, with a 86.7% overwhelming weight, intercepted through the interest of the institution for involving the social actors, can be correlated with the interpretation of the results from Figure 2. Equally, the
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Marta‐Christina Suciu, Irina Dumitrescu and Andrei Dumitrescu findings setting a managerial pattern based on performance and conformation of the employees are again retrieved, by promoting three keywords: being responsible, mobilization, and participation. 45.00
40.82
Responses frequency (%)
40.00 35.00 26.53
30.00 25.00
18.37
20.00
14.29
15.00 10.00 5.00 0.00 A
B
C
D
Answering option
Figure 3: Responses distribution for Question 3 (the foundation of the type of attitude regarding the personal participation at the organisation’s “life”): A ‐ conformation; B ‐ identification; C ‐ internalization; D ‐ conformation refusal
Satisfied by the activity
4.08
86.73
Implicated in the activity
Motivated to work
0.00
9.18
20.00
40.00
60.00
80.00
100.00
Responses frequency (%)
Figure 4: Responses distribution for Question 4: The real interest of your institution is to have employees... In this context, a major involvement could be facilitated by developing – at the individual level – of a motivation–identification mechanism, that would assume a consensual agreement between the personal and organisational goals. The empirical reality disproves however such a desire, proclaiming the divergence of the objectives and the primacy of the institutional ones. We consider that the severe prejudicing – indicated by reduced percentage values: 9.2%, respectively 4.1% – of the cause (motivating impulse) respectively the effect of the involvement act (maximization of satisfaction) leads to the deprivation of work significance. Such infirmity can determine the respondent to act as a robot, without any attraction towards his work. The permanence and continuity sentiment which should be felt at the individual level, in order to support the responsible management of the evolution trajectory, is thus injured. The last multiple choice question aims at the respondents’ perception regarding the impact due to the implementation of changes dictated by the socio‐economic context within PGUP. We remark – analysing
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Marta‐Christina Suciu, Irina Dumitrescu and Andrei Dumitrescu Figure 5 – the obvious disapproval (51.7%) of the academics, corresponding to the interception of the principal implications that changes comport as chaos generating sources. We consider that the significant resistance to changes is based upon two important aspects: the diminution of the respondents’ satisfaction and the negative perception of personal risk. When the university “life” is perturbed by profound changes, the accessibility and proximity of the career development is jeopardized. 60.00
51.69
Responses frequency (%)
50.00 39.45 40.00
30.00
20.00 6.12
10.00
2.73
0.00 A
B
C
D
Answering option
Figure 5: Responses distribution for Question 5: What is your opinion about the implementation of the changes imposed by the social, economical and political context within your organization? A ‐ most changes are more than necessary; B ‐ they create chaos, leading to demotivation; C ‐ I am reticent to changes, as they outrun the mentalities restructuring; D ‐ I approve changes, but part of them are inefficient We ascertain a vitiated tendency, even if normal, of behavioural orientation: the inability of the organisation to sustain the possibility of self‐development generates a limitation of the involvement in professional projects, especially of the subjects characterised by strong tendencies of self‐achievement. It is also interesting to observe the 91.2% total of the options betraying either an active and declared resistance, previously discussed (51.7%), or a passive and hidden alternative (39.5%), dismantling – using the adversative conjunction – the apparent adhesion to changes. The other answering options are relevant by their extremely limited frequency: 6.1% of the respondents declare themselves authentic supporters of changes and 2.7% openly recognise their reticence to changes, caused by the outrunning of the mentalities restructuring. The quasi‐unanimous distancing of the subjects from the problems related to changes, leads to a painful conclusive remark: abruptly implemented, changes annihilate any prediction regarding the academic career development.
3.2 Questions with scaled answers The questionnaire offered two interrogations with scaled answers, classified based on the criteria of the importance for the respondent of the main characteristics that imprints his career. An exploratory analysis of the dominants situated on the axis desires – aspirations delineates behaviours specific to the respondent personality. Following Table 1, we note a visible outclassing of the traditional aspirations by new tendencies, with intrinsic orientation, from which we remark the resolute detaching of the need for an activity offering professional satisfaction.
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Marta‐Christina Suciu, Irina Dumitrescu and Andrei Dumitrescu Table 1: Answering options for question 6 (hierarchy of the aspirations) Position Item M SD 1. An activity offering professional satisfaction 8.0 2.341 2. Rewarding the good quality teaching activity 6.32 2.580 3. Value recognition in front of the own conscience 6.06 2.746 4. Workplace safety 5.72 2.887 5. Satisfactory working conditions 5.62 2.763 6. Granting autonomy in fulfilling the prescribed responsibilities 5.28 2.429 7. Value recognition by the others 5.11 2.324 8. Raised salary 5.06 2.952 9. Transparent organisation and management of the institution 4.23 2.768 10. The need for an innovative authority 3.60 2.787 (from the formal and hierarchic model)
The following consensual tendency (rewarding the good quality teaching activity), succeeded by the value recognition in front of the own conscience, represent the foundation for the consolidation of the professional identity feeling, attracting performances in the career. The next options refer to classical aspirations (workplace safety and satisfactory working conditions), whose deterioration could produce a profound lack of satisfaction, but without constituting sui‐generis motivators. The capacity of the respondents to confer a durable significance to the experienced events is directly conditioned by granting autonomy and individual trust in fulfilling the responsibilities. The insignificant positioning of the money stimulation between the preferential orientations attests the fact that the most motivated employees are the ones for which the intrinsic motivation is fundamental. We thus conclude that, albeit invested with virtues of social symbol, the salary cannot constitute a panacea when choosing a career. The reduced level of the aspirations of the academics, corresponding to the characteristics of the organisational environment, can be sustained by a detached attitude of the teaching staff towards their own institution. This fact is also demonstrated by the previous analyses (Figures 2 and 4), which show the coagulation of an university culture, under whose influence its teachers, students and administrators do not largely find themselves. Important resource of any institution, the organisational culture is the subject of the last question. The hierarchy of the availabilities of the PGUP life “philosophy” – summarized in Table 2 – underlines the consensual majority of the options corresponding to a major preoccupation to attract external funds. This fact attests the interest of the University to reduce the dependence upon the money allocated by the Education Ministry and to dispose of own resources. However, the real preoccupation for a good quality teaching activity is undermined, fact injuring the content itself of the academic mission. Looking from three perspectives at the structure of the concept of organisational culture (culture of the university, of the academic profession and, respectively, the ideologies of the academic curricula), we note the accentuated prevailing of the culture of the academic institution. The practical valence of this concept equally outmatches the axiological component, by making a priority from the adaptation need of the University to the conjunctural data of the external environment. The respondents’ attention is thus focused upon some principal elements: assuming responsibilities, respectively the leaders’ tenacity. Corroborated with the following directions – turning the opportunities to profit and knowing the dynamics of the external environment – these options reflect a sui‐generis entrepreneurial vision perceived at the level of the afferent institutional culture. The distancing of the teaching staff from the own organisation emerges by under‐classifying some characteristics that facilitate the fulfilment of the need for achievement. We are referring to axiological criteria, such as: encouraging initiatives and promoting professionalism, which, prejudiced by the prevalence of the practical dominant, generate a decrease of the involvement in activity. The modest values of the last characteristics – assuming risk and tenacity in favour of changes – attest a resilience which appears when the changes imposed within the university become uncomfortable, fact denoting an insufficient preparation of the organisational culture to sustain substantial changes. This injures the transformation of changes in opportunities for career management.
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Marta‐Christina Suciu, Irina Dumitrescu and Andrei Dumitrescu Table 2: Answering options for Question 7 (availabilities of the organisational culture promoted by the institution) Position Item M SD 1. Major preoccupation to attract external funds for investments 6.43 2.949 2. Assuming responsibilities 5.76 2.213 3. Tenacity of the leaders 5.48 2.345 4. Turning the opportunities to profit 5.30 2.649 5. Knowing the dynamics of the external environment 5.0 2.309 6. Encouraging initiatives 4.72 2.335 7. Promoting professionalism 4.30 2.764 8. Assuming risk 4.22 2.356 9. Tenacity in favour of innovative changes 3.78 2.347
4. Delineation of a strategic line for an innovative career management Based on the manifest necessity of the academics to dispose of a larger autonomy, generating creativity and innovation at the level of both educational and research activities, we propose as a solution to conceive an organisational framework aimed at encouraging co‐participation, rather than the ascendant or descendant incursions within the hierarchy. Because the organisational conformism specific to PGUP (based on excessive traditional bureaucracy with the tendency to extend rationalisation to the tiniest predictable details) comports a negative impact, demotivating the individuals, we consider as an adequate remedial the instauration of a “creative bureaucracy” (Zlate, 2008, p. 216) – focused on the development of a substitute authority that presents as main marks personalization and innovation. In this way, the institutional hierarchy is maintained, but its rigidity – based on order and formalism – is reduced. We think that such organisation format presents two major advantages: it aims at a desirable evolution of the organisation and of the individual; it manifests “self‐destructive” tendencies in the sense of cultivating the desire to develop the abilities of each individual till the point in which his competence does not necessitate the presence of the authority. Thus, while the individual develops himself, his objectives become more compatible with the ones of the organisation – imperative fact within PGUP, according to our analysis. The visualization of the results regarding the problems of changes in our institution (Figure 5) makes us raise the legitimate question: What is the real motivation for which people – endowed with such intellectual slenderness and mobility of spirit as the academics – get to manifest circumspection, even fear and rejection, towards the society’s dynamic? Changes presuppose innovation. But abrupt innovation, focused only on situation and not on the agreement between situation and protagonists, transforms man in a prisoner of the situation, ignoring thus the role of the personality. For this reason, we consider that the declared opacity of a major part of the academic staff towards the implementation of changes in universities presumes in fact as basis a reticence towards the modality in which what is new penetrates the educational environment. This reversion is alimented by the interpretation of some decisions that do not convince or motivate, but penalise or frustrate the ones upon which they have a direct impact. We do not deny the predisposition of the human mind to react faster to hazards than to problems or opportunities, but we consider the excessive presentation by the management of some situations and conjunctural changes in terms of “dangers” and “defence” induces inevitably stress and anxiety, leading last but not least to hasty decisions sanctioning actually still the organisation members. This attitude of perpetual adaptation in order to ensure survival as a state‐owned educational institution could be overcame by means of a proactive approach presenting the same situation in terms of problem–solution or opportunity–gain. The major degree of emotional implication generated by the increased attention that should be granted to the decisions taken under risk or uncertainty conditions presents thus negative valences. Consequently, we believe that this way of action could be substituted successfully by a fine cognitive analysis of the decisional process involving both each individual and the organizational environment as such, so that the supposed “motivational costs” lead to a more efficient activity, not only to its chaotic haste. The university staff will therefore acquire faster a comprehensive state of mind which will activate all their essential values in view of promoting a participative behaviour based on the implication and integration of the staff members in the decisions‐making process. If the implication is subjective, being intentional and reflexive, the integration is relational and behavioural, both leading to the real (not only formal) exertion of the right to expression of the employees. It is thus generated a manifest and not only latent participation (constituted of intentions, analyses or reflections that will not be transposed in acts) representing the basis of a participative management of the
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Marta‐Christina Suciu, Irina Dumitrescu and Andrei Dumitrescu consultative type. We are referring to an insightful participation, based mainly on an intrinsic motivation, increasing the degree of satisfaction and of relatively independent, creative implication of the academics in their activity. It is interesting to observe that usually, when talking about social influence, we are talking, in fact, about conformism. Based on the in‐field data collected, we have though noticed that this conformism comports negative valences for the academic personnel, thus requiring imperiously the presence of innovation. The paradox of these suppositions, explaining the need for innovation by the very existence of the social conformation, is nourished by the conception according to which innovation can be “regarded as a kind of deviance and the innovators as a subcategory of the general category of deviants” (Moscovici, 2011, p. 60). In such conditions, the conclusion is that adopting an innovative initiative is enjoying success and is not subjected to social sanction only when the movement is performed from up to down, in the present case from the university management to the academic staff. We thus consider that one of the responsibilities of the leader’s role is to implement innovative strategies and to experiment new methods applicable to a sustainable evolution of the academic career. The fact that an innovative behaviour attracts obvious risks for any individual whose status is well‐defined within the society cannot be denied. We can thus explain why the individuals reaching positions in the leadership manifest an increase in the displayed circumspection and stability, often practising innovation only as an answer to internal pressures from their subordinates. The strategic line proposed unfortunately comports as limitation the intrusion of the governmental policy, blocking authentic university autonomy and stimulating mass conformation. Strategic innovation cannot be achieved as long as the university culture is impregnated by the political influence.
5. Conclusions This paper aimed at promoting a coherent process of incremental innovation and changes as a permanent basis of the educational environment, instead of sporadic response and adaptation reactions to the contextual dynamic. The proposed innovative strategies thus facilitate the access towards a new paradigm of an academic career management. “Feeling” the situations, the proactive analysis of the future and the behavioural flexibility, the development of the own “opening” in view of predicting the career evolution and elaborating the personal efficacy style, the construction of a vision in the conditions of re‐inventing time – all constitute premises and modalities of an efficient management of the professional trajectory, in the hypostasis of both manager and simple employee. We believe the implementation of these strategies is equivalent to the reversion to humanism of the organisational policy in the academic environment. Finally, we plead for restructuring the relationship between the “authors” and the “actors” of the organisation – using the terminology of Pierre Goquelin. We favour equality – not in capacities but in rights – between the “co‐authors” and “co‐actors” of the university, that could constitute the “debut of a reciprocal reconnaissance and regard” (Zlate, 2008, p. 379). The development of a participative management, able to operate in terms of synergy and not of pressure or conflict, could “sweeten” the following optimistic‐tragic statement of Goquelin: “the people and the organisation are condemned to stay together” (Zlate, 2008, p. 380). In these conditions, our opinion is that the real motor of changes is internal, leading to the re‐equilibration of the human personality. As a future research line, we intend to develop, based on the results of the questionnaire, a linear regression model describing the variation of the satisfaction of the academics towards their organisation as a function of their opening to changes and innovation.
References Bacali, L. ed. (2002) Manual de inginerie economica – Marketing, Editura Dacia, Cluj‐Napoca. Bexley, E., Arkoudis, S. and James, R. (2013) “The motivations, values and future plans of Australian academics”, Higher Education, Vol. 65, No. 3, March, pp. 385‐400. Chiaburu, D.S., Baker, V.L. and Pitariu, A.H. (2006) “Beyond being proactive: what (else) matters for career self‐ management behaviors?”, Career Development International, Vol. 11, No. 7, pp. 619‐632. Dumitrescu, I. (2013) Perspectiva optimist‐tragica asupra motivatiei muncii in Romania, Editura Universitatii Petrol‐Gaze, Ploiesti. Gavreliuc, A. (2006) De la relațiile interpersonale la comunicarea socială, Polirom, Iasi.
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Marta‐Christina Suciu, Irina Dumitrescu and Andrei Dumitrescu Gong, Y., Cheung, S.Y., Wang, M. and Huang, J.C. (2012) “Unfolding the Proactive Process for Creativity: Integration of the Employee Proactivity, Information Exchange, and Psychological Safety Perspectives”, Journal of Management, Vol. 38, No. 5, September, pp. 1611‐1633. Köster, M. (2002) Career Management in times of rapid change, GRIN Verlag, Munchen. Moscovici, S. (2011) Influenta sociala si schimbare sociala, Polirom, Iasi. Musselin, C. (2013) “Redefinition of the relationships between academics and their university”, Higher Education, Vol. 65, No. 1, January, pp. 25‐37. Oakes, G. (2003) “Academic Career Management: The Higher Learning in the Age of Marketing”, International Journal of Politics, Culture and Society, Vol. 16, No. 4, pp. 599‐611. Zlate, M. (2008) Tratat de psihologie organizational‐manageriala, Vol. II, Polirom, Iasi.
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Success Factors in Knowledge Sharing Behaviour Among Student Bloggers Nor Intan Saniah Sulaiman1, Mazlan Mohd Sappri1, Mohd Syazwan Abdullah1 and Nazean Jomhari2 1 Universiti Utara Malaysia, Sintok,Malaysia 2 Universiti Malaya, Kuala Lumpur, Malaysia norintan@uum.edu.my lolan@uum.edu.my syazwan@uum.edu.my nazean@um.my Abstract: This paper discusses on the success factors of knowledge sharing behaviour among Malaysian undergraduate student’s bloggers. Every university in the country has their own method in delivering the required knowledge to their undergraduate students, however occasionally there would still be unfulfilled requirement of students and this had not received any attention. The aim of this study is to identify the success factors for effective knowledge sharing behaviour among Malaysian undergraduate students. The study sample are student leaders in a student community representing Malaysian undergraduate students. The data collection involves investigating weblogs using content analysis approach. By analysing the data collected, the study has identified the success factors using relevant theories. The main theory used in this study is Knowledge Sharing Behaviour theory which has been adapted from four main theories. The model which identifies the success factors in knowledge sharing (KS) methods among Malaysian undergraduate students is the main contribution of this study. This research has successfully identified how Malaysian undergraduate students are using weblogs applications and other social media for knowledge sharing behaviour. Keywords: success factors, knowledge sharing behaviour, knowledge sharing behaviour theory, Malaysian undergraduate students, weblogs
1. Introduction This study investigates how Malaysian undergraduate student bloggers assess and share the information so that it becomes knowledge to enhance their lives. It is conducted to identify the criterion in knowledge sharing behaviour among undergraduate bloggers in their soft skills development. However, there are obstacles in knowledge sharing behaviour that can occur either at an organisation level, group level or individual level (Jain et al., 2007). Culture is one of the main obstacles. In addition, other obstacles in knowledge sharing include lack of communication and social networking skills (Riege, 2005), lack of time (Rosen et al., 2007) and lack of trust (Riege, 2005). Furthermore, many situations occur where individuals will not share their personal knowledge on certain topics. This situation can be attributed to various factors including physical, technological, psychological, personality and cultural issues (Riege, 2005; Yuen and Majid, 2007). Furthermore, lack of motivation or rewards (Davenport, 1997) is another factor as people are reluctant to share without incentives. Another obstacle in knowledge sharing is the ‘power of knowledge mentality’ (Davenport, 1997; Chaudry, 2005). People normally do not like to share their best ideas because it reduces their credibility in the organisation and their ability to move ahead. Yuen and Majid (2007) study has shown that undergraduates should realise the importance of skills in communication and social networking. In addition to the barriers in knowledge sharing behaviour, nowadays the Malaysian Ministry of Education does not have any specific policies to ensure that the Malaysian undergraduate students share their knowledge when studying in the campus. Currently, knowledge sharing behaviour scenarios are determined by Malaysian undergraduate students themselves or supported by the university facilities. Therefore, there are no mechanism or study on how to identify what criterion influences undergraduates tendency to share their experiences, information and even the knowledge they have gained with their friends, families or the communities. Therefore, the aim of this study is to identify the criterion for effective knowledge sharing behaviour among students’ bloggers in Malaysian university context. There is no specific research done to identify the criterion for effective knowledge sharing behaviour among students’ bloggers. In addition, the study also aims to look at
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Nor Intan Saniah Sulaiman et al. this subject from the student development perspective, specifically for improving their employability rate after graduation. The paper is structured as follows: Section 1 introduces the paper, while Section 2 discusses about knowledge sharing behaviour. Section 3 present the related theories related to knowledge sharing behaviour, while Section 4 highlights the success factor identification approach used in the study. Then, Section 5 discusses the classification of findings and justification, while Section 6 concludes the paper and presents future work direction.
2. Knowledge sharing behaviour (KSB) Davenport and Prusak (1998) believed that knowledge transfer consist of two things which are transmission and absorption; otherwise, the knowledge will not be transferred. Knowledge sharing is also important for determining the success of organisations (Davenport and Prusak, 1998) due to the contribution of knowledge consumption. This leads to the aim of this research, which was to investigate the success factors of knowledge sharing behaviour. According to Jain et al. (2007), there is a lack of solid theory on knowledge sharing. Moreover, Davenport and Prusak (1998) argued that knowledge transfer is only involved within two actions: first, when knowledge is transmitted to a potential recipient and second when it is absorbed by a person or a group. Otherwise, knowledge transfer has not occurred. Furthermore, knowledge sharing is also a critical success factors for knowledge management (Davenport and Prusak, 1998) because it has an important role in knowledge dissemination. However, knowledge sharing only allows people to share their opinions and experiences quickly for effective project, which means that people gain the experiences from others in finding solutions to problems (Ramirez, 2007). Without the sharing experience, knowledge sharing behaviour would not exist. Meanwhile, Roberts (2000) states that knowledge transfers will only happen if knowledge is diffused from the individual to others. It can be disseminated through the ‘process of socialisation, education and learning’. This statement is supported by Davenport and Prusak (1998), who also mention the limitations of the definitions of knowledge sharing or knowledge transfer, as they do not specify whether the knowledge is transferred from one individual to another or from individuals to groups (Zheng, 2005). Knowledge sharing is also applicable in situations where people are willing to share a common purpose and share their experiences purposely to exchange ideas and information (Storey and Barnett, 2000). This means knowledge sharing can be known as a process of exchange where resources are given by one party and received by another (Jain et al., 2007). Knowledge sharing should be defined as: ‘disclosure of existing to others ‐ thus creating ‘new knowledge’’, Boyd et al. (2007). It happens voluntarily through a reciprocal situation and via social interaction. Knowledge transfer tends to apply existing knowledge from one context to another, and it can happen voluntarily or involuntarily, non‐reciprocally and via training or social interaction. Meanwhile knowledge exchange means imparting of knowledge for something in return, and it happens involuntarily through reciprocal situations and also via contract. This definition is suitable in this research context. The definition of knowledge sharing behaviour for this research context is related to how students share their knowledge during their campus life including acquiring, learning, disseminating and sharing information and knowledge, and transferring tacit knowledge into explicit knowledge, and vice versa. Therefore, students are completely involved in all of the socialisation, externalisation, combination and internalisation processes of the Nonaka’s SECI or knowledge conversion model.
3. Theories of knowledge sharing behaviour The research model was adapted from the Theory of Planned Behaviour, Social Cognitive Theory, Social Capital Theory and Social Exchange Theory, which is later termed as the Knowledge Sharing Behaviour Theory. Firstly, Theory of Planned Behaviour was used as it suggests that individual behaviour is motivated by the eagerness of the individuals’ action (behaviour intention). Actions that an individual wants to carry out play a role in their approach towards behaviour. Then, subjective norm surrounding behaviour is based on the individual's perception and can perform the behaviour under control. Meanwhile, attitude towards act or behaviour describes the individual's positive or negative belief in their behaviour. This is decided through one's judgment of their own beliefs regarding the results arising from their behaviour and their evaluation of how these results make them feel. The overall attitude can be assessed as the sum of the individual consequence attraction behaviour assessments. Subjective norm means the way of behaving or doing things that most people would
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Nor Intan Saniah Sulaiman et al. agree is the correct way. Meanwhile, perceived behaviour control that refers to a person’s difficulty or easiness in performing behaviour. This theory tries to convince the control people have over their behaviour as untruthful from behaviours that are easily performed. The Theory of Planned Behaviour shown in Figure 1 is an extension of Theory of Reasoned Action (TRA) founded by (Fishbein and Ajzen, 1975), and was improved again by Ajzen (1991). Theory Reasoned Action has four general concepts which are: behavioural attitudes, subjective norm, intention to use and actual use.
Figure 1: The adapted Model of the theory of planned behaviour (TPB) (Ajzen,1991) Secondly, the Social Cognitive Theory was adapted since it can provide a model for ensuring the concept of understanding, predicting and human behaviour changes. This theory looks at human behaviour as the connection between personal factors, behaviour and the environment (Bandura, 1977; Bandura, 1986). A person’s judgement and performance play an important role in determining the relationship between a person and their behaviour. Furthermore, human behaviour and cognitive competencies are developed to deliver the relationships between the person and the environment. The relationship is also influenced by the social and structure environment. The third relationship between the environment and behaviour is person’s behaviour shaping of aspects of their environment, or in other words behaviour being influenced by the environment.
Figure 2: The model of social cognitive theory (ScogT) (Bandura,1986) The Social Cognitive Theory (Bandura, 1986; Bandura, 1997) shown in Figure 2 is widely accepted in information system study with proven validity. This theory was chosen because the individual will act based on personal cognition within the social environment. In the expected contribution of this paper, we are trying to look either the two determinants for this theory is reliable or not, which are self‐efficacy and outcome expectation. In this model, there are three elements ‐ personal factors, environment influence and behaviour ‐ which act as interactive determinants. It also influences directionally (Wodd and Bandura, in Hsu et al., 2007). For this study, we are looking forward for the contribution of sharing that the personal, behaviour and environment may influence individuals' knowledge sharing behaviour. Thirdly, Social Capital Theory is integrated into Social Cognitive Theory and also Theory of Planned Behaviour, for this research model. Figure 3 shows the integration between Social Capital Theory and Social Cognitive Theory, which have the same elements that suit the data categorisation. There are four suitable themes from Social Capital Theory adapted to Social Cognitive Theory: Generalised Norm, Togetherness, Everyday Sociability and Volunteerism. Social and Structure, Person’s Behaviour, Performance and Judgement are from Social Cognitive Theory elements. Fourthly, Social Exchange Theory is combined with Theory of Planned Behaviour and Social Cognitive Theory. The main assumption from Social Exchange Theory is the equilibrium balance for friendship relations through weblogs and Facebook, the cost of which is equal to its benefits. All of these assumptions depend on person’s behaviour (from Social Cognitive Theory) and behaviour (from Theory of Planned Behaviour).
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Figure 3: Integration model between social capital theory (SCapT) and Social Cognitive Theory (ScogT)
4. Success factors identification approach This paper discusses how we have investigated the success factors of knowledge sharing behaviour among Malaysian undergraduate students. The Malaysian student community is sampled from undergraduate students of Universiti Utara Malaysia. The respondents are taken from the selected community in this university. The team has identified fifteen bloggers to analyse their weblogs. The reason for this selection is that most of the research team members presently reside in the northern region of Malaysia. Thus, it makes it easier to understand the conditions and lifestyles of students in northern of Malaysia. Furthermore, accessing information is easier when compared to obtaining information from Malaysian student communities in other places. In addition, the researchers find it easier to understand the lifestyles of students in Universiti Utara Malaysia and their living conditions as this university is located in north of Malaysia. This work was conducted solely on undergraduate’s students. The reasons for choosing undergraduate students over postgraduates are: (1) the time limitation among postgraduates, as most of them have their own families; (2) postgraduate students enthusiasm in updating weblogs or being involved in Web 2.0 technology applications are also lower than undergraduates; (3) some of them think that the messages they are trying to deliver in weblogs could be misinterpreted by readers; and (4) some of them claimed they have limited time to access internet, need privacy, no priority and do not have time or busy to update. In addition, some of them are more comfortable with social networking such as Facebook, but not weblogs at all. They felt Facebook is more convenient and can provide quicker response and comments to socialise rather than weblogs. Based on the fifteen selected subjects who are bloggers from this community, the researchers have identified the categorisation that can be considered for the purpose of the analysis stage. For the main data collection, the researchers has observed and selected at least twenty entries within one year from both of the communities. From the findings, the researcher determines the types of behaviours from the Knowledge Sharing Behaviour theory. Generally, the main idea of this theory that can be applied to this category is where the subject (student) is referring to the individual and where their life is analysed through their personal weblogs. It also shows the determination through the environment factors, the attitude factors and the personal factors.
5. Analysis of findings The fifteen bloggers are coded A1 to A15. The analysis was done by reading the selected entries from these fifteen bloggers. The researchers selected all the entries for one year for the blogger who had blogging for more than one year. If not, the team ensures that there are at least twenty entries from the bloggers to do analysis on. Content analysis was done using Hermeneutic Theory (Berger and Luckman, in Wong, 2005) to
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Nor Intan Saniah Sulaiman et al. identify the suitable elements of theory for every identified entry. It requires the researcher to understand the flow of the explanation for the entries before identifying the elements for integrated theories. The first category is about personal feelings; for example, for subject A1 the behaviour can be seen from her personal words about her experience in class, in the college, and her feelings through academic days. The behaviour of the subject during the campus can be detected through the personal words, final examination mode, advice from her lecturer, and wishes as it was the last semester of her studies. Meanwhile, subject A2, the subject tries to show more on revealing the behaviour on the feelings in the personal behaviour. However A3 is more varied in terms of behaviourism sharing. A4, A5, A6, A9, A13 tend to share in their feelings, self‐ management and how to reboot the motivation after feeling down. However, for subjects A7, A8, A10, A11, A12, A14 and A15, there are common attempts to share their other behaviour beside the subject itself and also their past experience during childhood or the future of their life. It also can be seen some entries are presented through narrative methods. The highest number of elements is subjective norm where this element is very reliable with this classification. The reason for personal perspectives varies, depending on the individual perspective. Furthermore, the subjective norm is about the human norm itself, so this is the other reason why subjective norm also becomes the main stands of human behaviour. Then, it is followed by the behaviour, where all the actions or performance by the subjects is identified from the analysis stage. The point to behave or in this theory called attitude towards act or behaviour are the third highest of entries in this classification. This happened probably due to subjects always trying to justify the reasons for the action or behaviour itself. The same goes for perceived behaviour control where the difficulties or the strength in doing something are the lowest entries for this personal classification. For the entertainment classification, the elements from Theory of Planned Behaviour are mostly from the attitude towards act or behaviour and the behaviour itself. This is because this classification is about entertainment; it means the function of entertainment itself in determining the behaviour of the student. Mainly, the elements of attitude towards act or behaviour come from song lyrics, the song itself, advertisements or others, for example, song lyrics from Yuna (Yuna is an upcoming artist in Malaysia). In addition, the behaviour refers to the action in entertainment like watching movies, watching concert, watching songs on Youtube, and favouritism to singer or actress. For this classification (vacation), the most reliable elements are behaviour. The reason for this is the vacation itself, actions to take a holiday; it also means the subject must act before sharing the experience. Besides that, there are five types of behaviour intention, also selected when the vacation planning. It shows that sometimes, before the vacation is executed, planning is important. Subject A3 did mention the opinion on single travelling, and that can be considered as behaviour intention. All in all, from this finding, the subjects are willing to share the pictures showing the places visited. It can be guidance for others if they want to go to the same place. It can be seen as a trend that the subjects normally will spend their semester break for vacation rather than weekend time which is usually packed with academic workloads within the semester. Furthermore, sometimes it can revitalise their motivation after the tiring experience of study, finishing assignments or sitting for the semester final examination. For this classification (academic), the general academics themselves can be specific to the academic trips, the academic events and academic examination. Generally, only the element attitude towards act or behaviour is unreliable for this classification. The other four elements are reliable and the trends are balanced either for behaviour, behaviour intention, subjective norm and also perceived behaviour control. The behaviour element usually reflects how the subjects behave in their academic life either in study, in academic trips, academic events or preparation to face examination. The behaviour intention element is more about planning to do such as spirit, eagerness, learning style or examination preparation. Subjective norm refers to the opinion of the subjects in any academic issue. Meanwhile, perceived behaviour control is concerned with the difficulties in behaving or the need for support in carrying out an action. Based on this theory, adaptation to the sub‐elements with the basics of the theory which is the personal, the environment and the behaviour itself are performed. It seems that human belief, through personal to environment, is dominant for this classification rather than the other sub‐elements. The reason for human belief is dominant is depended on whatever behaviour performed must be based on human belief and the entries related to motivation are most suitable with the human belief. Furthermore, judgments through personal behaviour have almost eleven entries that are identified as the judgment. This occurs as the subject
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Nor Intan Saniah Sulaiman et al. has to decide whether it is good or not to follow the information that had been given. In a student’s life, they have competencies, so some entries can be identified as cognitive competencies through personal to environment. The environment in this context refers to the subjects’ friends, campus, family and also their student life itself. The eight entries for this sub‐elements is about the life challenges, future career life, fighters in the life, grab 5 things before come another 5 things, value of career, benefits of Islam months, poem on life and also rewards in life. Another interesting sub‐element is a person’s behaviour through the behaviour and environment. The nine entries are identified and are related to Theory of Planned Behaviour. The nine entries have more to do with the behaviour of the subjects themselves, like how to get motivation in general issues, change the lifestyle, sadness, ‘4 things to ensure the behaviourism’, people aware from 'sleep', the feelings of experiencing beautiful scenery and also to express real feeling. The human belief can be viewed through personal and environment for this classification is only reliable with the entry about the quote from former United States of America’s President, Theodore Roosevelt, entitled ‘it is not the critic who counts’. It can also identify as a judgement when the visitors judge whether the quotes can be accepted or not. The sub‐element of performance through personal to behaviour in this entry refers to information about malaysiakini.com, a website which is now free to visitors as no payment is required to obtain the information (Malaysiakini.com is an online news portal about Malaysian politics). Festive here refers to the festive events that are celebrated by the subjects and how they explain these in their weblog entries. It includes Eid ul Fitr and Eidul Adha. The entries are in various styles, such as personal words, information sharing, dedications, announcements and also photo sharing. Additionally, human belief as sub‐ element is also identified in this classification which is the beauty of way of life, opinion about Ramadan and fasting activity. Only two sub‐elements are adapted which are human belief and social and structures. Both of these sub‐ elements are through personal and environment elements. This classification is about official events, which means that the entries are reliable with the social and structures and human belief. Thirteen entries related to social and structures are more related with the official activities either organised by the society or non‐society. Meanwhile the other four entries concerning human belief are more related with the subject's opinions after attending the official events in the subject’s campus life. Social classification, consist of social activity and society work. Social activity covers the sub‐elements of person’s behaviour, performance and also human belief. For the sub‐elements of person’s behaviour and human belief, it also passes through to the same elements, which are personal and environment. In addition, the sub‐elements performance and judgement are through the same elements which are personal and behaviour. This means that this classification is related to the three elements for this theory. The person’s behaviour entries in this situation are more towards referring to personal experiences, some narrative story and also some general opinion about the behaviour itself. For judgements in this classification is adapted with the opinion of the subjects with the issue. The society activities only have three sub‐elements. The highest numbers are the social and structures, with almost seven entries identified. Then the judgements for the activities are five entries, and the person’s behaviour is only four entries. Most of the social and structures are related to the society’s activities. Meanwhile for the judgements, are related with the own activities by the subjects and also by the society joined by the subjects. Lastly, for person’s behaviour is the mix in the types of the behaviour by the subject itself. Besides that, the both personal and environment are related because this classification is about friendship. The most common sub‐elements are identified as human belief and person’s behaviour. As this classification is about friendship, logically human belief and person’s behaviour dominate the subjects' entries. Human belief is mostly contributed to by poems, quotes, songs, dedications, opinions and also narrative stories. Person’s behaviour is mostly identified by actions through experiences or narrative stories, feelings and also quotations to friends. Mapping of Findings
The mappings in Figure 4 and Figure 5 showing behaviour‐personal contribute the highest entries from the weblogs of identified student webloggers. It is not only limited to the behaviour, but all the elements in the Theory of Planed Behaviour consists from Personal and contributes the highest one.
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Figure 4: Idea mapping within the number of entries from perspective of theory of planned behaviour under knowledge sharing behaviour theory From Figure 4, the findings have identified from all the elements in the Theory of Planned Behaviour. It reveals the three elements ‐ attitude towards act or behaviour, subjective norm and also perceived behaviour control ‐ which consist of the personal classification. It is uncover the subject tendency to give the reason of the act or behaviour is been performed. Then the subjects of research also give the norms as human beings from the personal and academics classification. The same goes for the elements of perceived behaviour control. From the findings also, behaviour intention applies to personal, vacation and entertainments only. However, when looking at the behaviour, all the classifications are adapted to it. It may be proven by the respondent’s weblogs that people can read the behaviour of the individuals. Figure 4 also shows that subjective norm contributes for subjects in the Personal classification. As known, behaviour is one of the independent determinants of intention in this theory. In other words, it can also be a predictor to perform the behaviour, where the subjects perceive the social pressure in order to perform the behaviour or not. According to Ajzen (1991), the more levels of subjective norms there are in the individuals, the intention to perform the behaviour is stronger. However it varies across the behaviours and situations and depends on the type of behaviour itself (Ajzen, 1991). In addition, Figure 4 also shows from this classification, entertainment, is represented by attitude toward act or behaviour itself. It is also represented by subjective norm, behaviour intention and behaviour. However in this study, attitude toward act or behaviour proved to be the most used by the subjects in this classification. The behaviour of the subjects was performed, and then the experiences from that, were shared within their friends and family. It also indicates based Vacation classification, where the behaviour is determined to be travelling. Back from the vacation, the subjects will share their experience with the visitors of their weblogs. Besides the behaviour itself, behaviour intention was identified in this classification. However, behaviour is the most regular contribution as this classification itself is the behaviour, in other words it is travelling. This differs from the Academic classification; it shows that besides the behaviour itself which has the most entries, it also includes subjective norm, perceived behaviour control and also behaviour intention. However in this study, subjective norm is the most identified. It comes from the variety of behaviours in the study, such as doing the assignments; attend the examination, dieting, attending the academic exhibition, acting for further study abroad and others. Community From Figure 5 we can see the sub‐element of Social Cognitive Theory is chosen based on frequency of the knowledge in the motivation classification. The number of frequency of human belief becomes the highest number is stated in Figure 5. However, the other more detailed reason is because this human belief may also contribute to the community in the subject’s environment too. Furthermore, from Figure 5, Person‐ Environment is the highest frequency (45+22+7=74 related entries). That is the reason the sub‐element of person’s behaviour is related with the behaviour to environment. Environment here refers to community. It means friends are included within the community. It uncovers the fact that the community can determine the behaviour of the individuals. This classification may prove that the community can contribute to social structure by sorting out any official events. It means that, any events really need the structure in handling the events. These entries are about the opinions, experiences or words expressed by the subjects. This subject is
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Nor Intan Saniah Sulaiman et al. referring to what happened with the events that they had attended and shared in their personal weblogs. It also shows that the community really plays a big role to the subjects. For social activity classification, even though the person’s behaviour is the main contribution, it still depends on the community. Society works need the cooperation within the community to ensure that the works are a success. So the social and structures contributes to the running of the activities within the community. The friendship classification may contribute to the success of knowledge sharing. From the list of knowledge that has been identified, it seems that the subjects are very open in sharing their feelings, experiences, stories and anything regarding any issues within their friends. Friendship most probably comes from the community. Logically, friendship bonding will develop in the community bonding itself. This means that the friendship also plays a big role in the success factors for knowledge sharing among the students.
Figure 5: Idea mapping from the social cognitive theory based on entries from subjects determined by the sub‐ elements in the elements in the social cognitive theory Web 2.0 Technology Facebook can also be a critical tool in knowledge sharing behaviour besides the personal weblogs among Malaysian student in Universiti Utara Malaysia. All of these characteristics reveals from Social Exchange Theory assumption under knowledge sharing behaviour integrated theory. The applied assumption is applicable for these characteristics is the cost is the assumed same with the reward in human relationship. In other words, it means that what you give is what you get in the applied assumption for this Social Exchange Theory. Furthermore, from this findings support the research findings from Liang et al. (2008) in information technology as significant role specific to two reliable factors; social interaction and trust. The trust factors including two types which are cognition based trust and affect‐based trust (Mc Allister in Liang et al. (2008). Cognition based trust is about a rational evaluation for an individual’s ability to carry out the obligations. Meanwhile affect‐based trust is related with an emotional attachment comes from mutual care and exist between individuals (Mc Allister in Liang et al. (2008)). From the second characteristics of the information it has been uncovered that the respondents have the intention to share their personal weblogs through Facebook. For the majority of subjects the number of friends they have, also high and it proves that knowledge sharing behaviour exists among students. The willingness to put their name on personal weblogs shows that they have high Behaviour Intention to share any information through their personal weblogs. Besides that, the majority of them are also active in putting statuses on their account. The status can be about them, or about current affairs, academic issues or any other issues that can be relevant as students. For picture sharing, some of them seem to be very open to share their personal picture albums. Furthermore, the pictures are tagged by
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Nor Intan Saniah Sulaiman et al. their friends in quite high numbers. The numbers of profile pictures reveal the frequency the subject has for their profile pictures. The profile picture is their introduction picture that can be seen before anybody even adds them as a friend. Only three of the eleven subjects have video columns in their account and two out of eleven have tagged in the video column. Some of them are active in their video posts in their personal weblogs rather than their Facebook account. Generally all of them have experience in sharing links. All of them also provide rich personal information in the information column.
6. Conclusion This paper has discussed about knowledge sharing behaviour amongst undergraduate students in Malaysia based on the sample of undergraduate students from Universiti Utara Malaysia. Generally the main success factors are identified through personal, community, and Web 2.0 technology. It shows that these factors are critical and identical to nurture the sharing culture through individual approach. This is followed by the support through environment (community) and facilitated by the Web 2.0 technology. Personal factor has been proven by the TPB perspective, which comes along with other sub‐factors such as entertainment, academic matters and vacation experiences. In addition, community factor was proven by the integration of SCapT and SCogT, which highly contributes to judgement of community, person’s behaviour itself and also human belief of community. The Web 2.0 technology factor was proven by SET, which was identified from social interaction through bloggers itself and also from respective Facebook. These show the trend how the current younger generation are eager to share their personal experiences through the Web 2.0 technology facilities and support from their community. Therefore, it is strongly recommended that the respective ministries should encourage and nurture the younger generations to share their opinions through Web 2.0 technology by providing more incentives and supporting aids in order to nurture this important knowledge sharing behaviour.
7. The way forward This paper has significant contribution for determination of adapted model for critical success factor in knowledge sharing behaviour among Malaysian undergraduate students. For future work, this research has high potential to validate through multi‐criteria decision making to ensure the validity and the reliability. This method seems reliable due within involving multi‐criteria in determine the most significant factor in the research context. The multi‐criteria which are suitable for this research context can be from the positive attitudes which can contribute to the nurture the knowledge sharing behaviour among the selected community itself. From this paper, proving the personal is the main determinants in cultivate the trend in knowledge sharing behaviour with the support by the community and the Web 2.0 technology itself.
References Ajzen, I. (1991). The Theory of Planned Behaviour. Organizational Behavior and Human Decision Processes, 50 (2), 179‐21. Bandura,A.(1977). Self Efficacy: Toward a Unifying Theory of Behavioral Change. Psychology Review, 84(2), 191‐ 215Bandura, A. (1986). Social foundations of thought and action: A Social cognitive theory. Prentice‐Hall: Englewodd Cliffs, NJ. Bandura,A. (1997). Self‐efficacy:The exercise of control. New York:W.H.Freeman. Boyd, J. Ragsdell, G. & Oppenheim, C. (2007). Knowledge Transfer Mechanism:A Case Study from Manufacturing. 8th European Conference on Knowledge Management. Spain,Barcelona, Academic Conference Management. Calantone, R. J., Cavusgil, S. T. & Zhao, Y. (2002). Learning Orientation, Firm Innovation Capability and Firm Perfomance. Industrial Marketing Management, 31(6), 515‐524. Chaudry, A. S. (2005). Knowledge Sharing Practices in Asian Institutions: A Multi‐Cultural Perspective from Singapore. World Library and Information Congress: 71th IFLA General Conference and Council, Oslo, 1‐8. Davenport, T. H. (1997). Information Ecology. Oxford: Oxford University Press. Davenport, T. & Prusak, L.(1998). Working Knowledge: How Organizations Manage They Know. Boston: Harvard Business School Press. Hsu, M.‐H., Ju,T.L,, Yen, C.‐H. & Chang C.‐M. (2007). Knowledge Sharing Behavior in Virtual Communities: The Relationship between Trust, Self‐Efficacy and Outcome Expectations. International Journal Human‐Computer Studies, 65(2),153‐ 169 Jain, K. K, Manjit, S. S. & Gurvinder, K.S. (2007). Knowledge Sharing among Academic Staff: A Case Study of Business Schools in Klang Valley, Malaysia. Journal of the Advancement of Science and Arts, 2(Science and Technology), 23‐29. Liang,T‐P.,Liu,C‐C.,Wu,C‐H.(2008). Can Social Exchange Theory Explain Individual Knowledge Sharing Behavior? A Meta Analysis. International Conference on Informations System 2008 Proceedings, Retrieved from 28 June 2009 http://www.whiceb.com/download/whiceb2008/semimar/Ting‐Peng%20Liang.pdf Ramirez, A. (2007). To Blog or Not to Blog: Understanding and Overcoming the Challenge of Knowledge Sharing. Journal of Knowledge Management Practice, 8(1).
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Nor Intan Saniah Sulaiman et al. Riege, A. (2005).Three‐Dozen Knowledge‐Sharing Barriers Managers Must Consider. Journal of Knowledge Management, 9(3),18‐35. Roberts,J.(2000).From Know‐how to Show‐How?Questioning the Role of Information and Communication Technologies in Knowledge Transfer. Technology Analysis & Strategic Management,12(4).429‐443. Rosen, B, Satcie, F. & Richard, B. (2007).Overcoming Barriers to Knowledge Sharing in Virtual Teams. Organizational Dynamics, 36(3), 259‐273. Storey, J. & Barnett, E. (2000). Knowledge Management Initiatives: Learning From Failure. Journal of Knowledge Management, 4(2), 145‐156. Sun, H.C. (2003). Conceptual Clarifications for ‘Organizational Learning’, ‘Learning Organization’ and ‘A Learning Organization’. Knowledge Management and HRD. Human Resource Development International, 6(2), 153‐166. Wong, A.(2005). Theories Used in IS Research: Hermeneutic Theory. Retrieved 19 November 2009 from http://www.istheory.yorku.ca/hermeunetics .html Yuen, T. J.& Majid, M.S. (2007).Knowledge‐Sharing Patterns of Undergraduate Students in Singapore. Library Review, 56(6),485‐494 Zheng, W. (2005). A Conceptualisation of the Relationship between Organisational Culture and Knowledge Management. Journal of Information and Knowledge Management, 4(2), 113‐124.
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Innovation, Knowledge and Incompetence: The Case of the Eurozone Macroeconomic Policies Eduardo Tomé Universidade Lusiada, Famalicão eduardo.tome@clix.pt
Abstract: This paper aims at shedding a new and different light over a very big problem that actually is being felt by the European Society and by the world at large: namely the difficulties the EU is having, since adopting the EURO in 2002, with its own management of macroeconomic policies, and with its own forecasts over growth. We follow Sveiby (2012) analysis as a methodology. We compare the predictions before the Euro and with the Euro and analyze the impact the Euro entrance add in the correctness of experts predictions. This methodology puts in evidence a complete different perspective over a very important economy issue. Again, following Sveiby (2012) we say that experts should take into account that macroeconomic restrictions posed by the Eurozone regulations deeply affect the economy of the more divergent Eurozone Member States and that that effect has not been rightly accounted. The miscalculation generates mistakes in prediction of policy impacts. Those mistakes have major negative effects in the life of ordinary citizens. We sincerely don’t know (and believe it is not our fault, sincerely) of any study that studies the problem of the current Macroeconomic crisis in the EU as a problem of incompetence. Keywords: Innovation, Competence, KM, Eurozone
1. Introduction At the time of writing this paper (Spring 2013) the European Union (EU) is facing massive economic problems. Those problems are related to the difficulty of generating growth in the EU and also with the realization that some EU regions (namely the Southern part, ie Greece, Cyprus, Spain, Portugal and plus Ireland) are facing massive recession and a bleak future. In order to solve the problems related to those lagging regions the EU built Support and Rescue Programs” (SRP), for each one of the five countries with the exception of Spain. Those SRPs are meant to solve the massive debt problems the four countries face. Quite crucially they imply a set of macroeconomic and microeconomic policies that should be put in place. Finally, those programs usually forecast a period of recession followed by a recovery; that recovery means that the country was “healed” from the “economic disease” and that its future is now safe. It is worth mentioning that those SRPs are a new form of the Stabilization Programs (SPs) which have been for long advocated by the International Monetary Fund (IMF). However, the problem that exists now, and that we want to address in this study is the following: in the last decade, the forecasts made on the EU, and particularly for the 5 Southern Countries have gone terribly wrong. We want to see what happens when we analyze these mistakes can be analyzed through the lenses of the “Theory of Excess of Innovation” defined by Sveiby 2012. Our hypothesis therefore is the following: the Eurozone meant a change in environment that implied that experts became incompetent to predict the evolution macroeconomic of the EU member states. In order to test the hypothesis in the first section we expose the main concepts, namely innovation and macroeconomic stabilization; in the second section we expose the aforementioned Sveiby 2012 model; in the third section we analyze the EU macroeconomic evolution using the light of the Sveiby 2012 model and comparing with the case of the financial crisis used by Sveiby in 2012. In the fourth and final section we present the paper’s conclusions, linking them with the limitations of the study, its practical implications and the suggestions for further research.
2. Concepts 2.1 Innovation Innovation is not a new concept in the Economic and Management analysis. In fact, all the big Economist of th th th the 18 , 19 and early 20 centuries had some thoughts about the topic; and since Taylor almost all the management gurus of the early stages of management science also dealt with the subject and underlined its importance. But the first big thinker that put Innovation at the forefront of economic prosperity and development was Schumpeter, 1911. And for what matters in this paper we define innovation as the capability of changing the production function of a given company or organization (Stam, 2007). Innovation may be
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Eduardo Tomé incremental, if the organization begins to make things better, improving the production function of the products and services that are already provided or produced. But the innovation can also be radical, when the organization starts the production of new goods and services, that were inexistent and that it did not produce until that moment; the change in fact requires the definition and implementation of a new production function. So far so good, but reality is not black and white. At least three important qualifications must be made to this definition. First innovation can be technical or social, meaning that it can require the introduction of news techniques or the readjustment of routines in the organization, or both. Second, in principle innovation should be beneficial for companies, organizations, workers, the government and the society as a whole. Incremental innovation should mean a better utilization of the same resources, and more quality, quantity or both derived from the same production function. Radical innovation should give society new products, services, or ways of organizing which would be welcomed by the public. The benefits from innovation should and might be big and are difficult to summarize and enumerate. We can only enumerate a very approximated list of benefits from innovation: higher satisfaction for consumers and clients; wages and better employment for workers; productivity, market shares, profits, for organization; income and employment for societies; taxes for the government. Thirdly, innovation is first and foremost about change. And for human societies change is a big issue. We don’t need to be Luddites to resist change (Bailey, 1998). Resistance to change has roots and is a natural human form of acting. In fact in every society there are some people that may be harmed by change and the social responsible behavior is to help them and even compensate them. In order for change to be embraced it should be prepared, monitored and its rewards should be distributed by the maximum number of people. In a Knowledge Based Economy (KBE) innovation requires extensive and deep learning and unlearning processes to take place (Cegarra Navarro and al, 2005; Kainto, 2008). Finally, last but not the least, we must never forget the old sporting adage “you don’t change a winning team”. Therefore, it is not clear cut that innovation is definitively all good, and that more innovation is always better. We will come again to this problem, when we analyze Sveiby’s 2012 analysis in the next subsection.
2.2 Macroeconomic Stability Economics may be divided in two big subsections: Macroeconomics and Microeconomics (Samuelson and Nordhaus, 2007). Microeconomics studies the behavior of each type of economic agent like the consumers, the producers, the government, the unions, the rest of the world, the banking system and also analyses each type of market: free competition, monopoly, oligopoly, markets of production factors etc. Macroeconomics on the other hand studies the behavior of all those economics agents at the same time and in the same space. Somehow, microeconomics is interested in the trees, and macro in the forest. It is also common knowledge that the public intervention in the economy may achieve one of three functions (Musgrave 1959): allocation, redistribution and stabilization. Allocation refers to helping production factors being used by the organizations. Redistribution relates to balancing the society through taxes and subsidies. And stabilization aims at keeping the economy balanced: unbalances may exist in the internal accounts, when the public deficits are over a certain limit, but the worse unbalances exist at the external level when the Current Accounts Balance or the external debt are so huge that the country begins to face insolvency problems. In fact the two unbalances tend to be related nonetheless because governments tend to use public deficits as an extreme way to implement policies that would solve the external unbalance. The science of macroeconomic stabilization is a well-known field, following the work of Hicks (1937 and 1981) for closed economies and Mundell (1963) for open ones. In fact every graduate in Economics is sensed to know that the goals of macroeconomic stabilization may be achieved using several main instruments: exchange rate policy, monetary policy (using interest rates and emission of money), budgetary policy (using taxes, subsidies and public policies) and also prices and income policies and even sectorial policies. By and large, when a country is facing unbalances, the stabilization policy requires a contracionary budgetary policy (increase in taxes, reduction in subsidies and other public expenses), and also a tight monetary policy (increase in interest rates) in order to reduce demand (consumption, investment and public spending) which would reduce imports. But at the same time the country should also devaluate the currency, in order to increase its exports, and compensate in the labor market for the recessive effects of the monetary and budgetary policies. Prices would soar, real wages decline sharply, but at the end of the stabilization period, the country would find a new equilibrium and would be able to start growing again. The cycle of stabilization (and recession) and recovery
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Eduardo Tomé (and expansion) became known in the literature as “Stop and Go” (Bannock and al, 2011). Stabilization programs used to be defined by the International Monetary Fund (IMF) and some were implemented and became known as “success stories (Krueger and al, 2000). It is worth mention however that for the Go phase required usually some other form of support, like the one provided by the World Bank (World Bank, 2013) or by the European Structural Funds (European Commission, 2013). That is, the Go phase required structural changes and the Stop change only managed essentially conjunctural transformations.
3. Theories 3.1 The Sveiby analysis We follow the analysis presented by Sveiby in 2012. Competence is defined as the ability to act in a professional context (Polanyi, 1962). But competence is contextual – when the context changes, some competent may become incompetent. For instance, competence is dependent of technology – a carriage th th driver from the 19 century could not be competent driving a car in the 20 century (Tushman and al, 1986). Competence is decisively related with predictions. Competent people know how to make correct predictions over time. Following Polanyi, 1962, we may say that professional predictions might be wrong, but unprofessional predictions only can be right by chance. However, a professional expert may do wrong predictions, and become temporarily incompetent, if “unwittingly does prediction errors due to unnoticed change in the professional context”. In his paper, Sveiby details the application of this idea to the financial crisis of 2007-8 and onwards. Allan Greenspan, who in 2005 said that “Recent regulatory reform, coupled with innovative technologies, has stimulated the development of financial products, such as asset-backed securities, collateral loan obligations, and credit default swaps, that facilitate the dispersion of risk (Greenspan, 2005). “ had to recognize in 2009 that he had become temporarily incompetent: “Those of us who have looked to the self-interest of lending institutions to protect shareholders' equity (myself especially) are in a state of shocked disbelief”. And also “I made a mistake in presuming that the self-interest of organizations, specifically banks, is such that they were best capable of protecting shareholders and equity in the firms ... I discovered a flaw in the model that I perceived is the critical functioning structure that defines how the world works. (Greenspan, 2009)” According to Sveyby, a massive change occurred. The new products based on future speculation and issued st massively and increasingly during the first decade of the 21 century were an “excessive innovation”. This means that they were not fully understood and also that they disrupted previously established and well organized and good working equilibria. Warnings existed since early hours and by reputed voices as The Economist in 1987, Robert Rubin in 1994, Li in 1997, and Merton in 2005 (Sveiby, 2012). But those warning bells were not enough for a radical change in the financial industry to take place: the biggest banks began to sell Credit Default Options, betting in the future markets. That change led to temporary incompetence. Temporary incompetence generated wrong predictions. Those predictions originated trillions of dollars in losses. Sveiby 2012, then points out six flaws in the reasoning of incompetent experts: 1) new situation measured with instruments of the old; 2) predictions built in the products; 3) herding behavior in industries, that led to a push in the wrong direction; 4) pro-innovation bias, guided by the belief that more and faster innovation is always better; also, no learning occurred from mistakes; 5) experts and legislators blinded by an ideology that defended the CDOs; critique was dismissed as political opposition from extreme quarters !; 6) legislation was carried out that facilitated the existence of the CDOs and generated a grave path dependence. From this case, Sveiby 2012 derives for myths on Innovation: 1) Innovation is always good; 2) The innovation firm is the risk taker (in fact it is the society who pays the bill); 3) more innovation is better; 4) acceleration of innovation is vital for survival.
3.2 Relation with KM Even if for an outsider KM and incompetence may be not related, we believe that the Sveiby model we just presented highlights that relation very much. In fact, according to Sveiby, incompetence derives from a situation in which the change in the environment due to innovation, creates a gap in knowledge. New tools and new knowledge are needed but the analysis is still done with the old tools and politicians and deciders don’t understand that the old knowledge is wrong, leading to increasingly incompetent decisions. Therefore innovation, and radical innovation, which creates a change in context also generates a need for KM and for knowledge creation and sharing, and also for unlearning, Only when the new knowledge is created, a new
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Eduardo Tomé competence will be defined and then with unlearning, will be put into practice. When the new knowledge will be shared and implemented, the crisis derived by the radical innovation will be solved.
4. Cases 4.1 Description: summary In this paper we will discuss the case of the Eurozone, and particularly the case of Portugal, Spain and Greece as problematic EU members. We believe that an analogy can be made with Sveiby’s analysis of the financial markets. The description of the case will be made in several stages: a) Previous situation; b) Radical Innovation c) New context; d) Evolution over the new context; e) Ideological setting in the new situation; f) Incompetence in the new context; g) Social consequences of the incompetence. A summary of the two cases is presented in Table 1 above.
4.2 Description: details on the Euro case. a) Previous situation Since the Middle Ages, Europe had seen several currencies circulating. Only the Roman Empire succeeded unifying the European space under a single currency. After World War II more than thirty countries divided the European space. Each country its own macroeconomic policy, and of course its currency. When the USA abandoned the gold standard, the fluctuation between currencies began. In 1979 the European Economic Community created the European Currency Unit which was a basket of currencies, with a central rate and a margin of evolution which however never circulated. Even if coordination existed, until 1.1.2002 each Eurozone member had all the macroeconomic policies available: budgetary, monetary, and exchange rate. Prior to 2002 in each Eurozone Member the Economic policies were basically defined by the Ministry of Economy and Finance (on the Budget side) and by the Central Bank (on the Monetary side). It was also possible to devaluate the currency even if, in the 3 years before 2002, 11 countries had already fixed rates between themselves. Table 1: Two processes of Temporary Incompetence compared Financial Crisis 2000-2010 Previous situation Radical Innovation New context
Evolution over the new context
Hundreds of years of sound financial governance by banks with few exceptions Credit Default Operations. Future Markets. From 1993 onwards, with a peak in 2007-8 the market is flooded with new instruments the effectively begin to dominate it. The prediction is in the product. First it is a surprise, then an extraordinary backlash with trillions of losses, bankruptcies and nationalization of banks.
Ideological setting in the new situation Incompetence in the new context
Criticism dismissed as political opposition. Almost nobody foresaw the crisis and predictions went absolutely wrong.
Social consequences of the incompetence
Trillions of dollars of losses and subsequent problems with employment and unemployment
Reference
Sveiby, 2012
b) Radical Innovation
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Eurozone Crisis 2002-2013 and counting Hundreds of years of different currencies in different countries Single currency installed circulating in 1.1.2002 circulating first in 12 countries now in 17. No possibility of devaluation or monetary policy between each one of the Eurozone members. Connected budgetary policies. First a good feeling, then increasing financial stress, after divergence, then recession, plus unemployment amid programs of support by the so-called “Troika”. Criticism dismissed as political opposition. Expectations of growth and unemployment rates made by the Troika experts that should rescue the countries fail more and more. Increasing and unexpected unemployment, poverty, organizational failures, social dismay, loss of political credibility. Our own analysis
Eduardo TomĂŠ The Euro began to be the currency of 12 European Union (EU) Member States (MSs) in 1.1.2002 (Eurozone, 2013). Those 12 MSs were the following: Austria, Belgium, Germany, Greece, Finland, France, Ireland, Italy, Luxembourg, Netherlands, Portugal, and Spain. Since then, in the last 11 years the Euro extended its influence to 5 more countries: Cyprus, Estonia, Malta, Slovenia and Slovakia. At the time of writing (May 2013) the following EU members donâ&#x20AC;&#x2122;t have the Euro as a currency: Bulgaria, Czech Republic, Denmark, Lithuania, Latvia, Hungary, Poland Romania, Sweden, and UK. We donâ&#x20AC;&#x2122;t consider the Euro to be only an incremental innovation, even if the EU bodies tried to implement it by steps, with the phases of construction of the Economic and Monetary Union (EMU) (European Commission, 2013b). We think that the Euro, in 1.1.2002 was radical innovation because it meant a big, drastic and more than long lasting, may be definitive, change in the context and governance of the countries that entered the Eurozone. c) New context The Eurozone effectively meant that, for good, and forever, all the Eurozone members began to have the same monetary policy, the same exchange rate in relation with the external world and the same currency within the Eurozone with no possibility of devaluating within the Eurozone. This meant that the independent economic policy of those MSs who adhered to the Eurozone began to be done only by the Budget and that the Monetary policy and exchange rate policy began to be defined by the European Central Bank (ECB) (European Central Bank, 2013). The ECB is the center of the European System of Central Banks, and it is ruled by the Lisbon Treaty, according to which the ECB was the obligation of keeping the inflation rate in the EU and particularly in the Eurozone lower than 2 percentage point per year (European Commission, 2012). This dictum is the center of the EU economic policy and effectively subordinates all the economic policies to the monetary policy objective of price stability; price stability is an economic objective which, in recent years has been defended has the most important of all (Fieldstein, 1999); stability would be beneficial because it would reduce uncertainty and would foster the investment, contributing to growth and reducing unemployment (European Central Bank, 2013b). d) Evolution in the new context The Eurozone represents a block of 330 million people with an average GDP per capita of almost 30000 dollars, and a total GDP of around 9 trillion dollars that puts the block has the second major world economy in the world, only behind the USA (15 trillion) but in front of China (8.5 trillion). The importance and weight of the Eurozone in the world was the same almost from the start: the 5 countries that joined the Eurozone since 2002 only account for 15 million people, and a combined GDP of 200 Billion (Eurostat, 2013). Since 2002 growth has been weak in the Eurozone has a whole: only 1.3% on average, a figure that compares badly with the world average of 3%, with the USA (2%) and particularly with the emergent countries (China 8%, India 6%, Brasil 5% and Russia 4% (World Bank, 2013b). This slow growth was also market by a recession in 2009 after the financial crisis, when the Eurozone economy contracted by o.4%. (Eurostat, 2013). Slow growth was reflected in higher levels of unemployment and from 2002 to 2012 the jobless rate increased from 8% to 12% (Eurostat, 2013). Again the numbers are not good, particularly when compared with those of the other big OECD countries like the UK (from 4 to 8%), the USA (from 5 to 8%), Japan (from 6 to 4%) or Canada (from 4 to 7%) (World Bank, 2013). However the real problem regarding the Eurozone since the instauration of the Euro has been related to the appearance of growing discrepancies within the zone itself. Some countries began to have problems as soon as 2005, problems that were accrued during the financial crisis and this became almost unbearable since 2010. Those countries are above all Greece and Portugal, and also Cyprus, Ireland and Spain. Those countries effectively diverged from the Eurozone average, since 2002, and the process of divergence did not yet end. Greece brgan at 90% of the EU average in 2002, caught up with the EU average following the Olympic Games but diverged substantially since 2007 and as a whole went from 95% of the Eurozone average to 80% (Eurostat, 2013). Portugal was at 80% in 2002 and is with 75% now (Eurostat, 2013b). For Spain the figures are 100%, 1005 in 2007 and 98% nowadays. Ireland started with 138%, arrived at 147% before falling back to 129%. Cyprus was a success story going from 88 to 100% of the EU until the monetary problems that occurred this year, and a reduction of the GDP in 20% is forecasted (Wall Street Journal, 2013). Moreover, in general, for these countries, the divergence was coupled with recession, increase in unemployment, youth unemployment, long run unemployment and foreign debts (see Table 2):
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Table 2: Evolution in the new context
Greece Portugal Spain Ireland Cyprus
Foreing Debts
GDP
Unemployment
+70 +70 +60
-10% -5% 0% -20% -20% (+)
+20% +15% +15% +8 +10%
´+30
Youth unemployment +40 +30 +40 +20 +20
Long run unemployment +10 +5 +8 +8 +3
Source: Eurostat (+) Forecast The problems became so acute that four of those countries had to be supported by rescues packages, issued by the so-called Troika (International Monetary Fund, European Central Bank and European Commission). The “Troika Agreement” was in fact a new form of the “Stabilization Agreements” usually done by the IMF (see Concepts section / Macroeconomic stability) in which the “Letter of Intentions” was replaced by a “Memorandum”. The signature of the Memorandum effectively had binding force, and obliged the national Government to implement some policies, as a counterpart of receiving funds that would ensure the solvency of the country (European Commission, 2012). e) Incompetence in the new context The introduction of the Euro in so different economies was always a matter of discussion. The theory states that “Optimal Currency Zones should have the same currency”. (Mundel, 1961). But there was a debate over if the Euro was an OCZ (Furruter, 2012). Many interests related to party politics (some parties were in favor, some against in several countries), or business (managers welcomed the idea with different degrees of enthusiasm or caution) where in place (Goodhart, 2012). At the EU level the change that the Euro was somehow prepared, because the Euro should be the final and third step of the European Monetary Union (European Commission, 2013b). Also some studies were very clear pointing out that the consequences from the Euro would be complex and not equal to all the countries (De Grauwe, 1993). It was at that point in time that the Financial Times famously defined the notion of PIIGS (The Economist, 2008). So, people and organizations have been warned, and some strategies of defense had been put in place, but reality was much worse than it was expected. First, before 2007, the evolution was already worse than expected but anyway, as the economy was still growing there was a feeling that things would get better (Eurostat, line 2 Table 3). However, as Queen Elizabeth famously pointed out (Daily Telegraph, 2008), big and bad surprise happened and surprisingly even if they were so big, nobody saw them coming (Eurostat, line 3 Table 3). But, again, in this case, things are not so bad, because the incompetence was caused by the influence of the mechanisms described by Sveiby (2012) in the economy. Where and when incompetence began to show was at the time of the reaction to the crisis. In a word, the reaction foressen by the EU bodies did not happen, and the EU went from a crisis (based in the financial market) to another (based on its internal structures) (Eurostar). The discrepancy between reality and expectation is described in line 4 of Table 3. It was however when the rescue policies began to be put in place in the PIIGS that things really got worse. Since 2011, the Troika and the ECB have been making the wrong predictions time after time (see line 5, of Table 3). Nobody predicted such recession and such an immense volume of unemployment as those that have been verified recently in those PIIGS countries. The mistakes have to be considered as a clear sign of policy incompetence. In fact, the since 2011 the predcitions went wrong for the EU, the Eurozone and for the PIIGS as a whole. But the fact that the wrong predictions affect more than other countries those that were subject to support, is shocking. But we consider that the explanation to that increased failure lies in the fact that those countries were the ones that needed more than others the Macro policies they left behind with the Euro (see c) for a description of these policies). Table 3: Eurozone: evolution – from forecasts to reality – GDP growth rates Between 2002 and 2006 2007 and 2008 After the crisis: 2009 and 2010 With the Troika: 2011 onwards
Forecasts 2% 2% -3% 1.5%
Source: (European Central Bank, 2013 and Eurostat)
Actual data 2% 1.5 -3 0%
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Assessment Correct Weak Poor Bad
Eduardo TomĂŠ Finally, the comparison between what happened when those countries received the support from the IMF before joining the Eurozone, and what has been happening within the Eurozone, only reinforces the idea that a case of incompetence began to exist based on the Euro setting of radical innovation. Table 4: From success to failure: stability packages before the Euro and with the Euro compared UK 1976
Portugal 1978-9
Prediction Labor Party Government ask for 2.6 Billion loan to solve external debt problems Solving the Budget and external deficits.
Portugal 1983-4
Identical
Greece 2011-4
First bayout of 110 Billion Euros in 2010, to solve otherwise unsolvable debt crisis.
Portugal 2011-4
78 Billion Euros bailout agreement reached in 2012.
Spain 2012-4
100 billion Euros needed for the financial sector
Cyprus
Bailout of 10,5 Billion Euros in 2013
Ireland
After two decades of a booming economy Ireland endured a recession in 2007 due to troubles in the financial sector. The bailout of 113 billion Euros was agreed in 2010.
Reality Economy stands the schock recession would happen in 19791981,
Assessment Success
Source Rodgers, 2013
Achieved even if with raise in unemployment (2%) and recession (2%). More unemployment (3%) and recession (3%) but the problems were solved Second bailout needed in 2011 of more 109 Billion Euros. GDP falling sharply (10%) and unemployment reaching 25%. Initial forecast of recovery in two years, wrong. Expected recession of at least 7%. Unemployment at 17% level (previous record of 8%). Banks were rescued by unemployment is over 20% and youth unemployment in 55%. The end of the crisis is not in sight. Freezing the bank system could endanger all the European economy. Recession was been mild since 2010, and the economy is growing slowly. Unemployment rose from 6 to 14%
Success
Nunes, 2010
Success
Nunes, 2010
Failure
Eurozone 2013
Failure
PEP (2013)
Neutral
European Commission 2012
Prospects are gloomy.
BBC, 2013
Neutral
BBC, 2013
f) Ideological setting in the new situation Two very distinguished American economists, both Nobel Laureates, have been criticizing the logic of the Troika Agreements and the general idea that austerity is the way forward to the EU and the Eurozone (Inman, 2013; Stiglitz, 2013). The idea that one of more PIIGS countries should leave the Euro has been also defended (The Economist, 2011). And both Krugman and Stiglitz have made it clear that they donâ&#x20AC;&#x2122;t consider the EU governing bodies as competent in the way they have been managing the EU economy.
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Eduardo TomĂŠ However, when made by Europeans, the criticism over the Troika Agreements and the European Macroeconomic governance have been seen as ideologically motivated (Traynor and al, 2013). Even in the UK, policy concerns took over economic arguments (New York Times, 2013). g) Social consequences of the incompetence. At the present moment, the Eurozone is left with a stagnant economy in which the young have huge difficulties finding jobs and the middle aged fear for their pensions. Particularly in the countries that were more supported, unemployment (young, long lasting and overall) is reaching record levels, poverty rising, and emigration to the North of qualified people seems to be the only way of ensuring a decent survival.
4.3 Discussion: what went wrong ? We have two explanations for the temporary incompetence which is swallowing up the Eurozone. The first one is that the instauration of the Euro effectively disrupted the equilibrium that existed between the economic forces of the Common Market and the social forces of Social Cohesion (European Commission, 2013c) by giving too much strength to the Economic side. As that balance is essential to guarantee the dynamic between Integration and Development / Convergence takes place (TomĂŠ 2004), the Eurozone appears as to be an explanation for divergence. The second explanation relates to the differences between conjunctural adjustment and structural adjustment; conjuntural adjustments may be made using macroeconomic policies in the short and medium run; structural adjustments require micro policies in the medium and long run. The IMF agreements used all the conjunctural instruments to solve a conjuntural crisis and only after the structural changes occurred; within the Eurozone, the number of conjunctural policy instruments is much less reduced and the Troika effectively is trying to achieve conjunctural goals using structural instruments.
5. Conclusions The Eurozone meant a change in environment that implied that experts became incompetent to predict the evolution macroeconomic of the EU member states. That incompetence was a positive function of the level of change the Eurozone produced in each country: the greater the change in the environment the greater the incompetence that followed. We define environment as a set of Macroeconomic policies, of external (exchange rate), monetary (interest and money supply) and fiscal scope (taxes, expenditures and deficits). The Eurozone meant that 17 countries began to have a single exchange rate, interest rate and money supply and that the 27 countries were obliged to have strictly ruled fiscal policies (Stability Pact). Therefore 17 different environments were transformed in one new reality. Crucially not all the countries were similar in the Macro policy they would prefer. The greater the divergence between the new situation (Eurozone Macro), and the preferred situation by each state (National Macro), the bigger the divergence between reality and prediction of experts. Matters are aggravated with time, because the non-adjustment of policies to country desired Macro levels, aggravates the mistakes in predictions. The fact that in the late nineties 13 countries behaved as a single unit, was more due to chance than to success in applying economic theory. Namely not all the countries preferred a Macroeconomic policy aligned with Germany. Interest rates and exchange rates were two policy elements some countries lost entering the Eurozone. German tends to want higher rates than other countries. And also the euro meant the absence of devaluation for some countries. In the more affected countries, the change in the environment effectively was drastic, and the policies designed began to be ineffective because the experts did not understand the magnitude of the change. Worse than that the problems accrue with time because the experts donâ&#x20AC;&#x2122;t understand what they are missing and they keep using old receipts for a new problem. Finally as we mentioned in the theoretical section: new knowledge is needed about the Eurozone economies, and why may be survive in cohesion. Only when this new knowledge is achieved, incompetence will finish. Only when the new knowledge will be shared and implemented, the crisis will finish. In a way, we need a new Keynes (1936), to solve the current crisis as the great man solved the 1920-32 crisis, by proposing a new theory, which, when implemented through the New Deal, generated prosperity worldwide after WWII .
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Eduardo Tomé Daily Telegraph (2008) The Queen asks why no one saw the credit crunch coming http://www.telegraph.co.uk/news/uknews/theroyalfamily/3386353/The-Queen-asks-why-no-one-saw-the-creditcrunch-coming.html De Grauwe, P (1993) The Political Economy of the Monetary Union The World Economy Volume 16, Issue 6, European Central Bank (2013) Monetary Policy http://www.ecb.int/mopo/html/index.en.html European Central Bank (2013)b Economic Forecasts for the Eurozone GDP growth http://www.ecb.int/stats/prices/indic/forecast/html/table_hist_rgdp.en.html European Central Bank (2013) The benefits of price stability http://www.ecb.europa.eu/mopo/intro/benefits/html/index.en.html European Commission (2012) Lisbon Treaty. Brussels. 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(1999) The Costs and Benefits of Price Stability, University of Chicago Press, Chicago. Furruter M (2012) The Eurozone, an optimum currency area ? IFIER Papers Innsbruck. Goodhart, Charles. "Understanding the Euro Requires Political Economy, Not Just Economics". Econ Journal Watch 7(1): Greenspan A (2005) Economic Flexibility. Speech, October 12, 2005. Greenspan A. (2009) I was wrong about the Economy. Sort of. The Guardian, UK. http://www.guardian.co.uk/business/2008/oct/24/economics-creditcrunch-federal-reserve-greenspan Hicks, J. R. (1937), "Mr. Keynes and the Classics - A Suggested Interpretation", ''Econometrica'', v. 5 (April): 147-159" Hicks, J. (1981), "IS-LM: An Explanation", Journal of Post Keynesian Economics, v. 3: 139-155 Inman, P. (2013) Krugman call to arms against austerity. The Guardian, May 6. http://www.guardian.co.uk/business/2013/may/06/paul-krugman-battle-against-austerity Keynes, J. (1936) General Theory of Emplyoment. MacMillan. Kianto, A. (2008). Assessing Knowledge Renewal Capabilities. International Journal of Innovation and Regional Development, Volume 1, Number 2, pp. 115-129. Krueger. A. Fischer S. Sachs. J. (2000) IMF Stabilization Programs. in Martin Feldstein (Editor) Economic and Financial Crisis in Emerging Market Economies. University of Chicago Press Mundell, R. A. (1961). "A Theory of Optimum Currency Areas". American Economic Review 51 (4): 657–665. Mundell R (1963) . "Capital mobility and stabilization policy under fixed and flexible exchange rates". Canadian Journal of Economic and Political Science 29 (4): 475–485. doi:10.2307/139336. Musgrave R. (1959) The Theory of Public Finance: A Study in Public Economy. New York Times (2013) As British Prime Minister Visits Washington, His Party Splits Over European Union http://www.nytimes.com/2013/05/14/world/europe/britains-conservatives-split-overeurope.html?_r=0 Nunes A (2010) The International Monetary Fund's stand-by arrangements with Portugal. An ex-ante application of the Washington Consensus? Lisbon. Polanyi M (1962) Personal Knowledge: Towards a Post Critical Philosophy: Chicago. University of Chicago Press. . Portugal Economic Probe (2013) Troika Dasnboard - http://www.peprobe.com/topics/troika-dashboard Rodgers S. (2013) UK GDP since 1955. The Guardian – Data blog http://www.guardian.co.uk/news/datablog/2009/nov/25/gdp-uk-1948-growth-economy th Samuelson P. Nordhaus W. (2007) Economics, 20 edition, McGraw Hill. Schumpeter J. (1911) The Theory of Economic Development: An inquiry into profits, capital, credit, interest and the business cycle. Stam C (2007) Knowledge Productivity: Designing and testing a method to diagnose knowledge productivity and plan for enhancement, Doctoral thesis, University of Twente, Enschede. Stiligtz, J (2013) Citizens in Europe are rejecting austerity policies as deeply misguided, The Guardian, March 6. http://www.guardian.co.uk/business/economics-blog/2013/mar/06/citizens-europe-reject-austerity-misguided Sveiby KH (2012) Challenging the Innovation Paradigm. Consequences of Temporary Incompetence in the Financial Sector. Presentation at ECIC 23.4 2012. The Economist (2008) Ten years on, beware a porcine plot", June 5.
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Eduardo Tomé The Economist (2011) Should Greece leave the Eurozone and return to the dracma? http://www.economist.com/economist-asks/should-greece-leave-euro-zone-and-return-drachma The Wall Street Journal (2013) Cyprus's Bank Crisis Feeds Recession Risk http://online.wsj.com/article/SB10001424127887323605404578380592459312374.html Tomé E. (2004) Indicators to Guide the European Union’s Social Policy. World Social Congress Albertville. Traynor I. Hoppen J. Inman P. Italy votes against austerity leaving markets in turmoil. http://www.guardian.co.uk/world/2013/feb/26/italy-election-austerity-eurozone-turmoil Tushman M. and Anderson, P. (1986) "Technological discontinuties and organizational environments", Administrative Science Quarterly, 31,439-65 World Bank (2013) Social Development: Policies – Internet webpage. As assessed in May 25, 2013. http://web.worldbank.org/WBSITE/EXTERNAL/TOPICS/EXTSOCIALDEVELOPMENT/0,,contentMDK:20489652~menuP K:1304598~pagePK:148956~piPK:216618~theSitePK:244363,00.html World Bank (2013)b World Development Indicators.
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Competence Management in Industrial Engineering Departments in the Czech Republic David Tuček and Jaroslav Dlabač Tomas Bata University in Zlín, Czech Republic tucek@fame.utb.cz jaroslav.dlabac@e‐api.cz Abstract: This article deals with Industrial Engineering from the perspective of its appropriate incorporation into the organizational structure of a company. It is certain that it is not possible to design a universal method that will be valid for all businesses. When one creates one´s own system, it is always necessary to consider the specific and unique conditions ‐ which are, undoubtedly, the size of the company, the production program, the company culture or ways of procedures. However, we can define a number of principles and recommendations that we can use on the right path to a well‐ functioning production system. The aim of this paper was to reflect on the position of Industrial Engineering in the organizational structure of a company, on the job content of this department, as well as on the assessment and motivation of individual industrial engineers. At the end of the paper, the actual situation in Czech companies is outlined as is the role of Competence Management, which is based upon three determining elements:
Continuous: i.e. an on‐going effort.
Integrated: i.e. a systematic whole of personal systems and instruments.
Tuning: i.e. concerns the dynamic linking of what the company requires and what the individuals can ‐ and are prepared, to do.
In our article we would like to describe the Competence Management context in organizations that are building their own production system. It is not a matter of days, weeks or months. It's a long‐running process that lasts for several years. But we are now talking about the production system in its true meaning; which means a functioning production system. We have come across many companies that "built" their production systems in a few days. Actually, for them this expression meant only a set of methods like 5S, SMED, VSM, TPM, etc., that were visualized and described in the booklet entitled "The Production System of XY Company". From our point‐of‐view, this is definitely not what a production system should be. A system can be interpreted in many ways and thus, the understanding of the production system can be completely different. Keywords: lean manufacturing, wasting, visualization, standardization, utilization of machinery, production system, organizational structure, competence management, process improvement
1. General introduction In the academic world, researchers are usually focused on theories and models which address Corporate Competence and Human Resource Management and Development. In our article, we bring to light new information about Competence Management in a situation when a company builds a production system, Industrial Engineering methods and an Industrial Engineering department concurrently. To begin with, it is worth mentioning that if a box named “Industrial Engineering” is missing in the organizational structure scheme, this does not necessarily mean that the company does not intentionally pursue optimization and process improvement. It may just mean that the company simply calls this department by some alternative term which, however, is essentially identical – e.g. terms like Process Engineering, Engineering, Process Improvement or Kaizen Department are also used for such departments. Analogically, employees working in Industrial Engineering are then called process engineers, change managers, lean managers, lean specialists, kaizen managers, kaizen specialists, etc. In recent years Industrial Engineering has gained in importance, as evidenced by the fact that in Czech companies, it is now a far from unknown term. Moreover, there are only a few companies without this department in their organizational structures. If companies omitted it, they would not effectively take advantage of the opportunities offered by IE (Industrial Engineering). Industrial Engineering department and Industrial Engineering manager (worker) has the additional value in small and large company too. We can often find IE managers (worker) in company with for about 100 employees and more too.
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David Tuček and Jaroslav Dlabač However, in order to enable full utilization of the potential of this discipline, one should keep in mind several factors which could ultimately influence the efficiency of Industrial Engineering in the company in a significant way. The most important ones include:
The position of this department in the organizational structure
The job content of the department, its responsibilities and authorities
The department performance assessment system (Tuček and Dlabač, 2012).
2. Literature review 2.1 Industrial engineering – definition, history of the discipline, basics, opinions on this discipline and its classification In the context of system‐intensive and human–centric systems, we can include IE in the Human‐centred Engineered Systems group. Kimbler (1995) wrote that: This was the basis of our profession at its origin, and, although the form of the study has evolved, it is the basis of our profession today…. Just as other engineers foster advances through innovative new technologies, Industrial Engineers deal with those technologies in their implementation and use. But we can use, in the context of modern Industrial Engineering methods, some Process‐oriented Cost Accounting, calculations and budgets and Rates Costing, for example production salaries, machine hours, production material etc. (Rajnoha, Chromjaková 2009). Hughes (1986) published, that scholarship by Science and Technology historians suggests that the context, case‐history, and historical narrative need to be examined within the systems and networks produced by social agents and their organizations; conceptualizations of Industrial Engineering are similarly produced. IE continues to explicitly include the connection between people and technology as part of the definition of the discipline, said Bix (2004). Specifically, the description of Industrial Engineering as “Imaginary Engineering” recalls the notion that the sciences can be classified on a scale from “soft” to “hard.” A hierarchy of disciplines was published over a century ago by Comte (1988), classifying disciplines focusing on positivist approaches as “hard,” and by inference, superior to other disciplines. Some works ‐ for example, works by Beyer, Lodahl, and Gordon (1972), and Smith, Best, Stubbs, Johnston, and Archibald (2000) paralleled Comte’s (1988) conclusions and advanced the measurement of “hardness” from an ordinal scale to an interval scale. The trend toward refining the measurement of “hardness” is an indication that it is a widely‐accepted notion. Two perceptions that Foor and Walden (2007) identified as separating Industrial Engineering from other engineering disciplines were, distance from technology and a less rigorous curriculum. What Foor and Walden call “distance from technology”, has strong parallels to themes in earlier research that would describe this distinction as “less likely to use graphs,” “softer,” and “less positivist.” Murphy et al. (2007) in their study, ascribe the idea that IE is easier than other engineering disciplines to a lack of understanding of what IEs do. If Industrial Engineering is stereotyped as easier than other engineering disciplines, it is easier to marginalize the discipline, because Engineering has been found to operate as a meritocracy of difficulty. In deepening the analysis of the context of IE, some of the findings in the literature cited above may be related to the acquisition of “Social Capital” (Brawner, et al., 2012). According to Stanton‐Salazar and Dornbusch (1995), social capital refers to “a set of properties existing within socially patterned associations among people that, when activated, enable them to accomplish their goals or to empower themselves in some meaningful way”.
2.2 Lean production system like a connecting link of industrial engineering methods The approach now known as Lean Production has become an integral part of the manufacturing landscape in the United States over the last four decades. Its link with superior performance and its ability to provide competitive advantage is well accepted among academics and practitioners alike e.g., Krafcik (1988), MacDuffie (1995), Pil and MacDuffie (1996), Shah and Ward (2003), Wood, et al. (2004) in their publications.
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David Tuček and Jaroslav Dlabač Even its critics note that alternatives to Lean Production have not found widespread acceptance; for example, Dankbaar (1997) and Rinehart, et. al. (1997), admit that ‘‘Lean Production will be the standard manufacturing mode of the 21st century’’. Lean Production uses half the human effort in the factory, half the manufacturing space, half the investment in tools, half the engineering hours to develop a new product in half the time. It requires keeping half the needed inventory, results in many fewer defects, and produces a greater and ever growing variety of products, said Womack, Jones and Roos (1990) in their book entitled “The Machine that Changed the World”. If we were toll compare the earliest publications related to Japanese production systems ending with the most recent publications related to Lean Production, we can say that the early Japanese books were more precise in defining the Toyota Production System and in identifying its underlying components ‐ for example, Monden (1983), and Ohno (1988), as compared to the research articles because the latter focused on defining and describing specific components of the system rather than the whole, Sugimori (1977) and Monden (1981). Table 1: Ten elements of production system flexibility (Debnár, 2008) Production Mix Output Facilities Employees Start‐up Products Tact Layout Manipulation routes Transport of products Packaging of products
How many types of products are we able to produce in the production system? How quickly can we switch from one type to another? How much volume are we able to produce in the production system? How quickly can we respond to an increase in orders? How many types of operations can we handle with current production facilities without further investment? How many different activities can employees handle in the production process? How many new products are we able to implement without a change in our performance? How quickly does it take for launching a new product? In how many different tacts are we able to produce? How many variations of workplaces are we able to create in the workplace? By using how many different routes can we manipulate with the products? How many types of products are we able transport from Point A to Point B? How many types of products are we able to pack using equipment?
Shah and Ward (2005) in their article evaluated time line marking the critical phases in the Lean Production evolution, from 1927 (Philosophy of Henry Ford), cross progress in Japan (1945‐1978), the Toyota Production System in North America (1973‐1988), academic progress (1988‐2000), to the present. A Lean Production System = Flexible Manufacturing System. A Flexible Manufacturing System represents the ability to produce and assemble any product range, in any order, and quantity. What we mean by Flexible Manufacturing System is shown below in Table 1. Results
3. Position of the department in the organizational structure There are many various ways how the Industrial Engineering department is incorporated into the organizational structures of Czech companies. Probably the most common is to incorporate this department into the Production Department. It is also often incorporated into the Technology Department, the technical preparation of production or the Quality Department. However, we have also met a case when the Industrial Engineering Department is incorporated into the Logistics Department, Financial Management and Audit or even, into the Management and Maintenance of Premises Department.
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Figure 1: Example of an organizational structure (Tuček and Dlabač, 2012)
4. Department’s job content and its responsibilities and authorities The work‐content of the Industrial Engineering Department can be categorized from several points of view. From the field of activity perspective, the activities of this department can be divided into:
Improvement of processes in the development and pre‐production stages
Production process improvement
Non‐production process improvement
Employee training and education in the process improvement field.
Improvement of processes in the Development and Pre‐production stages As for this field, one cannot expect an industrial engineer to design technical or structural designs of a new product. However, they may assist in proposed solution with regard to subsequent problems and any wastage in production potentially caused by this solution. In the case where the Industrial Engineer has some knowledge of innovative methods, they can also be, at this stage, a very useful moderator of a workshop focused on products, processes and technological innovation. Production process improvement The improvement of production processes is probably the most common job content of an Industrial Engineer. This includes all those activities associated with the optimization and standardization of production processes. Non ‐ production process improvement Nowadays, the field of Non‐production Processes is becoming more and more popular. In this field, the Industrial Engineer most often plays the role of a moderator. Both internally and externally realized projects can be in question. As for intercompany projects, their objective is often to reduce the customer product delivery duration through the optimization of administrative or logistic processes. Industrial Engineers are also increasingly engaged in the workshops at their suppliers with the intention to optimize customer‐supplier processes. Employee trainings and education in the process improvement field The education and development of employees in the process improvement field is a very important and, at the same time, much underrated domain. The entire Lean Production stands or falls depending on the employees
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David Tuček and Jaroslav Dlabač themselves, which is something we often overlook or do not want to realize. The Industrial Engineer´s task is not only to methodically educate the employees, but also to keep them sufficiently informed about projects realized, results gained and future strategy, and thus to motivate and integrate them into the IE activities. From the activities performed by Industrial Engineers perspective, the categorization can be as follows:
Realization of activities and minor projects. The majority of activities are performed directly by the Industrial Engineering Department.
Leadership of processes leading to improvements in projects of a complex nature. The Industrial Engineer plays the role of Project Manager.
Moderating workshops aiming to improve processes in both the production and non‐production stages. Probably the most common example from the production stage is workshops focused on the identification and elimination of wastage, reduction of time for machinery restructuring or on increases in the efficiency of a chosen workplace.
The education and training of employees in the Process Improvement field of. Another factor with a significant impact on the job content of this department is the technology used.
5. Assessment of the industrial engineering department’s performance How can one assess the work efficiency of an Industrial Engineering department or of an Industrial Engineer as an individual? Some managers or company owners have a relatively clear answer to this question. The profit yielded by an Industrial Engineering manager to the company should be at least ten times higher than their monthly salary. This criterion for the assessment of an Industrial Engineer´s performance seems to be comprehensible. However, reality is often slightly different, and some issues regarding performance assessment arise, especially in the following areas, that we describe in next chapters.
5.1 Corporate culture It is necessary to realize that in cases where the corporate culture is insufficiently developed and the Industrial Engineer does not feel any other than financial motivation, the entire assessment of the department performance may easily deteriorate into “playing with numbers”. And who else should master this “playing with numbers” better than an Industrial Engineer?
5.2 The job content of the department Job content plays a large role. In the case where we want to stare into the pre‐production stages, it is self‐ evidently irrelevant to reward an Industrial Engineer´s work with a proportion of the savings realized in the current production process. Another thing we ought to bear in mind is that this department´s work is not only about rough figures and optimization proposals. Often, it is a much more complicated to impress the suggestions on the employees themselves or on other departments. Therefore, it is very important to also master so‐called soft skills, which are however, a bit difficult to incorporate into the metrics of the department´s performance assessment. Job content can be supported by Business Process Change Management, as we define in our article (Tuček and Tučková, 2010). But all roles and competences you can see in complete list on the Figure 2.
5.3 Authorities and competencies In this field especially, projects take place across several departments or even across the entire company. The Industrial Engineering Department is often assessed negatively on some projects, although in essence it did not have any competence to influence them. This certainly relates to the previously‐mentioned way the IE department is incorporated into the organizational structure. In cases where the Industrial Engineering Department comes under the Production Department, it has only very few competencies to bring a Lean Administration project to a successful conclusion. So how can one assess and reward the work of Industrial Engineers correctly? There is no choice but to agree assessment and hence, remuneration should be linked to well‐defined objectives. However, these objectives need to take into account whether the Industrial Engineering Department is actually able to influence them through its actions. What proportion of their salary should be conditional on meeting these objectives? We propose that this conditional part should be somewhere between 30% and 40%. If the conditional part were
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David Tuček and Jaroslav Dlabač lower, it might not be motivational enough. On the other hand, if the proportion of the conditional part of the salary were higher, no space would remain for activities which cannot be unambiguously transformed into measurable objectives.
Figure 2: Roles of an industrial engineer (Tuček and Dlabač, 2012)
5.4 Competence management in the practice of other companies Now we would like describe some specific nature of leadership tasks and responsibilities: emphasise the need for a structured leadership competence management tool. Vukovic (2011), e.g. described how specifically tailored leaders will represent an advantage for organizations. The Leadership Competence Management Model in a metallurgical enterprise presented in his article gives organizations the possibility to do just that (Vukovic, 2011). Three sets of problems were addressed. Leadership Competences definition – target factor – the leadership performance review problem was resolved by introducing standard competences that leaders need to pose and a target factor – the Competence Profile Correlation Matrix, which connects leadership competences with a target factor. The second problem that Vukovic (2011) described is the Strategy Deployment Problem. The solution proposed is to set repetitive goals for leaders. These goals need to be reset every time that a new occurrence in the organization happens. Leaders need to interpret a specific occurrence in the operational level context. The third problem set includes the Communication – Motivation – Performance Chain.
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David Tuček and Jaroslav Dlabač
5.5 Competence framework The conceptual framework we propose intends to represent the links between key concepts involved in the building of design core competence. These concepts refer to the capabilities of the design organizations and to the criteria used to appreciate if the capability of the global design organization is a core competence or not. This framework is presented by means of the unified modelling language (UML) class diagram in Figure 3 (Object Management Group, 2011). Dependency matrices play the role of interconnecting models – in the centre of the figure (Tunkele, et al., 2011). The production company all too often don’t use competence and dependency matrix in this form. That is the reason why we would like to introduce for example this one (see Figure 3). But there are other possibilities too. F. g. using the process diagrams (VAC – Value added chain, and other types).
Figure 3: Design core competence framework (Object Management Group, 2011) A growing interest in solutions extracting more informative and meaningful results from knowledge resources is witnessed both in literature and in actual commercial systems. Fully in this trend, Colucci propose the integration in a single knowledge manage‐ ment system of a variety of advanced semantic‐based services supporting competence management (Colucci, et al., 2011). Professional communities are supported also by change agendas and opportunities. These conditions provide the subject matter for strategic studies. The use of professional strategic positions must be understood in two ways: Firstly, LIS sector national level of competences is asymmetric. Secondly, the introduction of changes in LIS competences profiles and praxis are more effective in vocational training models than in educational models (Ochôa and Pinto, 2008). And what about Small and middle enterprises (SME)? The necessary management competences are equal in the SME and large enterprises but the kind of their topicality and application differs, therefore, it is very important to organize professional development activities that are aimed at the definite model and kind of business. The efficiency of introduction of the statements obtained in the result of the research in planning of activities for professional development is closely related to academic or professional education in the corresponding field. The professional teaching models for SMEs should be according to enterprises
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David Tuček and Jaroslav Dlabač management specifics, focusing on the fact that managers of SMEs combine several competences. Therefore, in future research it is necessary to broaden the research field also including the evaluation of influence of formal education (Bonjour and Micaëlli, 2010).
6. The factual situation in Czech companies We have recently tried to carry out a minor survey with the objective of mapping basic information about departments dealing with Process Improvement issues. Approximately 20 Czech companies from various industries were involved in the survey. Here, we state some examples of the survey questions:
The name of the organizational unit dealing with Process Improvement
The incorporation of this unit into the organizational structure
The number of employees in this department in relation to the total number of company employees
The job content of the department
The remuneration system for department employees
6.1 Name of the organizational unit dealing with process Improvement The most commonly used name for this department is Industrial Engineering, or Process Engineering, occurring in more than 50% of companies (the English version, Industrial Engineering, was included in this category as well). Such terms like Process Optimization, Technical Development or Business Excellence, occurred less frequently.
6.2 Incorporation of this unit into the organizational structure Quite an interesting finding is that this department comes directly under the Managing Director or factory manager in only three companies. In six companies, this department is under the Production Department, and in four companies it is under the Technical Manager. The remaining 40% of companies use relatively individual incorporation into their organizational structures. The interesting ways of incorporation include the Industrial Engineering Department coming under the Logistics and Asset Management Department.
6.3 Number of employees in this department in relation to the total number of company employees On average, the ratio for each employee of this department is almost 250 other company employees. The absolute number of employees in this department ranged most frequently from 2 to 5. Probably the greatest extreme was found in a Moravian company where the ratio was nearly 1 000 employees to one Industrial Engineer.
6.4 Job content of the department This question was aimed at the main topic of this work and the definition of a further three most frequent secondary activities. The most common job content of the department or of the Industrial Engineer as an individual was Production Process Optimization. Only in three of the companies surveyed did Industrial Engineers also deal with the improvement of Non‐production processes. This department is not involved in the rationalization of the pre‐production stages in any company. In the Production field, the department most frequently deals with layouts and assembling line optimization or monitoring and increasing pre‐defined indicators – like KPI, CEZ, etc. Another indispensable activity is employee training.
6.5 Remuneration system for employees department In the vast majority of companies, the remuneration system is linked to objectives. The extent to which these objectives are met is then transformed into a variable component of their salaries. Only in three companies is the salary completely independent of meeting the set objectives and thus only has the nature of a fixed component. The objectives are usually linked to fulfilling the performance and work quality indicators, or to the introduction of production system elements. Only in two cases are the objectives linked to fulfilling the tasks and meeting the deadlines of concrete projects. The proportion of the variable salary component ranges
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David Tuček and Jaroslav Dlabač from 3% to 10% in 60% of the companies. Only in 15% of the companies did the IE employees´ salary variable component exceed 20%.
7. Conclusion Finally, we can only add that besides knowing methods and techniques of work rationalization and processes optimization, an Industrial Engineer also needs to master soft skills ‐ primarily communication skills. Especially when taking into account that, for an Industrial Engineer, it is a must to cooperate with colleagues throughout the organization from the top management down to the ordinary employees. That is why this skill needs to be developed too. It should also be noted that thanks to their knowledge of production processes, as well as support and administrative processes, the IE employee has a great potential to rise within the organizational structure. Important contributors to this potential are not only the gaining of experiences and an excellent knowledge of individual processes from their basics, as well as working on a project management basis.
Acknowledgments This paper is one of the research outputs of the project “A Methodology for Optimizing Assembly‐lines in Czech Companies” registered as: “IGA/FaME/2012/017”. The authors are thankful to the Operational Programme Education for Competitiveness co‐funded by the European Social Fund (ESF) and national budget of the Czech Republic, the grant No. CZ.1.07/2.4.00/31.0096 ‐ “Building partnerships and strengthening cooperation in the field of lean manufacturing and services, innovations and industrial engineering with the emphasis on the competitiveness of the Czech Republic”, which provided financial support for this research.
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David Tuček and Jaroslav Dlabač Rajnoha, R. and Chromjaková, F. (2009) Activity based costing and efficiency of its application in the wooden houses production. Instytut Technologii Drewna, Poland, Vol 52, No 181, Pages: 105 – 127. Rinehart, J., Huxley, C. and Robertson, D. (1997) Just Another Car Factory? Cornell University Press, Ithaca, New York. Shah R. and Ward, P. T. (2003) „Lean manufacturing: context, practice bundles, and performance“, Journal of Operations Management, Vol 21 No. 2, pp. 129–149. Shah, R. and Ward, P. T. (2005) „Defining and developing measures of lean production“, Journal of Operations Management, Vol 25, No. 4, pp. 785‐805. Smith, L. D., Best, L. A., Stubbs, D. A., Johnston, J. and Archibald, A. B. (2000) „Scientific graphs and the hierarchy of the sciences: A Latourian survey of inscription practices“, Social Studies of Science, Vol 30, No. 1, pp. 73–94. Stanton‐Salazar, R. D. and Dornbusch, S. M. (1995) „Social capital and the reproduction of inequality: Information networks among Mexican‐origin high school students“. Sociology of Education, Vol 68, No.2, pp. 116–135. Sugimori, Y., Kusunoki, K., Cho, F. and Uchikawa, S. (1977) „Toyota Production System and Kanban system: materialization of just in time and respect‐for‐human systém“, International Journal of Production Research, Vol 15 No. 6, pp. 553– 564. Tuček, D. and Dlabač, J. (2012) „Industrial Engineering in Organizational Structure of Company“, 1th WSEAS International Conference on Finance, Accounting and Auditing (FAA '12), pp. 158‐163. Tuček, D. and Tučková, Z. (2010) „IT and SW support of Business Process Change Management“, Proceedings of the 5th WSEAS International Conference on Economy and Management Transformation, Vol 2, pp. 698‐ 703. Tunkele, S., Marcinš, J. and Domkins, A. (2011) „Management Competences Assessment in small and medium‐sized forest Enterprises“, Research for Rural Development ‐ International Scientific Conference, Vol 2, No. 2, pp. 71‐77. Vuković, A., Ikonić, M. and Doboviček, S. (2011) „Model for Leadership Competence Management in Metalurgical Enterprise“, Metalurgija, Vol 50, No. 3, pp. 167‐171. Womack, J. P., Jones, D. T. and Roos, D. (1990) The Machine that Changed the World, Harper Perennial, New York. Wood, S. J., Stride, C. B., Wall, T. D. and Clegg, C. W. (2004) „Revisiting the use and effectiveness of modern management practices“. Human Factors and Ergonomics, Vol 14 No. 4, pp. 413–430.
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Economic Evaluation of the Level of Knowledge Services in Selected OECD Countries Zuzana Tučková and David Tuček Tomas Bata University in Zlín, Czech Republic tuckova@fame.utb.cz Abstract: The theme of this article is generally defined by the issue and characteristics of Knowledge Services ‐ primary focusing on the definition of Knowledge Services and consequently, on the economic evaluation of their level in selected OECD countries, as well as the identification of areas for the development of Knowledge Services in the Czech Republic. Services generally occupy more and more space in human activities. Basically, they have accompanied mankind since time immemorial. When people started exchanging products among themselves and a middleman – the merchant ‐ appeared, we can speak of providing intermediation services. In human communities, individuals have always existed who started to take care of others at the time of disease or during injuries, but they also paid attention to various ceremonials that developed in their community. In later years, people extended their sphere of activity and seaside countries especially, conducted exploratory journeys and on the basis of these, performed transport services between countries and continents. They started to spread their experience and knowledge and developed them progressively into Intentional Education. Nowadays, a huge area has opened up to services involving various activities from the earlier times as well as new opportunities for services that arise every day. The above facts are also reflected in the economic indicators of developed countries, where up to a quarter of employees out of the total in employment are working in the Knowledge Sector. Therefore, there is a shift in job opportunities from primary and secondary activities to tertiary ‐or more likely, quaternary services. The whole article consists of several consequential parts. Next to the usual sections of the scientific work, the introductory part is focused on a thorough survey of the current state of the issue and the level of knowledge in the field of Knowledge Services. In the following section, comparative analyses of Knowledge Services in selected OECD countries are carried out. In conclusion, there are different findings generalized and formulated into the conclusions and the application of the knowledge contained in the publication is defined. Keywords: knowledge, knowledge management, knowledge services, economics sectors, knowledge activities
1. Literature review The European Union is making great efforts to encourage creative activity, to somewhat mitigate the gap with the U.S. in science and technology and, if possible, keep up with the "Asian tigers", particularly China and India. Therefore, the interest in monitoring development activities in the field of knowledge‐intensive activities (hereinafter KISA). Under the heading of the OECD project was implemented, which was asked to look at 11 countries, ongoing innovation and the share of services in these innovations. The results are presented in the report: Innovation and Knowledge ‐ Intensive Service Activities (KISA) in 2006.
1.1 KISA objectives The huge increase in quaternary sector and the service sector in general in post‐industrial societies, among other things, due to the development of technology and globalization, and it is the quaternary sector, thereby pushing the economy still further, primarily through research and development.Because of the knowledge economy places great emphasis on intangible products, both for final consumers or firms is being restructured many European economies that are moving away from industrial activities, to the service sector. (Boyer 2010; Fellman et al. 1997; Brunn et al. 2003) Knowledge‐intensive business services (KIBS) in particular have emerged as a dynamic industry supplying for instance, management consulting, accounting, legal, marketing, and personnel services. KIBS organizations are private service businesses that sell their services on markets to other businesses and organizations (Miles, 2003). As such, KIBS represent a subset of KISA that has an important role in the innovation system. They have been studied as an industry in their own right. For example, accounting and legal solutions are sold, even exported, and the growth rate of these kinds at businesses is significant (see for example Toivonen, 2004; Miles, 1993).
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Zuzana Tučková and David Tuček However, KIBS are not the only source of knowledge‐intensive services. USA is a wider concept including activities carried out by private and the public sector service suppliers as well as services produced within the organizations. For instance, government funded research and technology organizations are very well established suppliers of USA, and they have been excessively studied as important actors in innovation systems. A range of other types of organizations — for instance, industry associations — supply various types of KISA at varying levels of complexity. Knowledge‐intensive service activities and their role in innovation USA can have different roles In relation to innovation. Some services are routine like, such as most accounting. In other cases, KISA can be a vehicle for bringing in important new knowledge to the organization. For example, advice on a specific issue of intellectual property protection might change the whole way a firm looks at its new ideas and values its routines. Some services are tailored to the organization, and may be the source of new ideas about products or about implementation. Knowledge‐intensive service activities may have at least three different roles in facilitating innovation in the client organization (adapted from Miles et al., 1995). KISA can act as:
A facilitator of innovation supporting the organization during the process that leads to an innovation.
A carrier of innovation transferring existing knowledge and innovations between organizations, industries or networks, or within the organization so that it can be applied into a new context.
A source of innovation playing major role in initiating and developing innovations within client organizations.
The different ways that services operate depend partly on the response of the client organization: how it engages with the supplier of the service, how it retains learning from the service, and how it manages knowledge throughout the organization. Al1 these features are part of the interactive nature of the service process (OECD, 2006). Research approach to KISA The KISA project takes a novel approach to studying the nature services. It investigates knowledge‐intensive set‐vice activities and their role in innovation. The focus of KISA is not on one sector or a particular technology, but on the role of knowledge‐intensive service activities in a range of different settings including: the software industry, health care, tourism and leisure, and resource‐based industries. The project investigates the nature and use of knowledge‐intensive services in innovation in these different settings. As such the study is exploratory in nature, but it also seeks to achieve a certain level of comparability across the industries and participating countries. The ultimate objective of the KISA study is to contribute to the development of innovation policies in OECD member countries by assisting them in the production and use of knowledge‐intensive service activities. This can be achieved by combining qualitative case studies and aggregate level national statistics and generic indices of service trade. Potentially, increased understanding of knowledge‐intensive service activities can transform into many new insights into innovation systems — given that the flow of such activities clearly plays a key role in innovation systems (OECD, 2006).
1.2 Statistics on high‐tech industry and knowledge‐intensive services Statistics on high‐tech industry and knowledge‐intensive services (sometimes referred to as simply 'high‐tech statistics') comprise economic, employment and Science, technology and innovation (STI) data describing manufacturing and services industries broken down by technological intensity. The domain uses various other domains and sources mainly within Eurostat's official statistics (CIS, COMEXT, HRST, LFS, SBS, SES, PAT and R&D). Its coverage is therefore dependent on these other primary sources. Two main approaches are used in the domain to identify technology‐intensity: the sectorial approach and the product approach. A third approach is used for data on high‐tech and biotechnology patents aggregated on the basis of the International Patent Classification.
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Zuzana Tučková and David Tuček The sectorial approach: The sectorial approach is an aggregation of the manufacturing industries according to technological intensity (R&D expenditure/value added) and based on the Statistical Classification of Economic Activities in the European Community (NACE) at 3‐digit level. The level of R&D intensity served as a criterion of classification of economic sectors into high‐technology, medium high‐technology, medium low‐technology and low‐technology industries. In some cases, due to restrictions of the data sources used, the aggregations are made at NACE 2‐ digit level. Services are mainly aggregated into knowledge‐intensive services (KIS) and less knowledge‐intensive services (LKIS) based on the share of tertiary educated persons at NACE 2‐digit level. The sectorial approach is used for all indicators except data on high‐tech trade and patents. Note that due to the revision of the NACE from NACE Rev. 1.1 to NACE Rev. 2 the definition of high‐technology industries and knowledge‐intensive services changed. For high‐tech statistics it means that two different definitions (one according NACE Rev. 1.1 and one according NACE Rev. 2) are used in parallel and the data according to both NACE versions are presented in separated tables depending on the data availability. For example as the LFS provided the results both by NACE Rev. 1.1 and NACE Rev. 2, all the table using this source have been duplicated to present the results by NACE Rev. 2 from 2008. For more details, see both definitions of high‐tech sectors under 21.3. Within the sectorial approach, a second classification has been created ‐ Knowledge Intensive Activities ‐ based on the share of tertiary educated people in each sectors of industries and services according to NACE at 2‐digit level and for all EU 27 Member States. A threshold was then applied to rank sectors as knowledge intensive. In contrast to first sectorial approach mixing two methodologies one for manufacturing industries and one for services, the KIA classification is based on one methodology for all the sectors of industries and services. The aggregations in use are total Knowledge Intensive Activities (KIA) and Knowledge Intensive Activities in Business Industries (KIABI). Note that due to revision of the NACE Rev.1.1 to NACE Rev. 2 the list of Knowledge Intensive Activities has changed as well. The two definitions are used in parallel and the data according both NACE versions are shown in two separate tables. NACE Rev.2 collection includes data starting from 2008 reference year. For more details please see the definitions under, 21.3. The product approach: The product approach was devised to complement the sectorial approach. It opens the way to far more detailed analysis of trade. The product list is based on the calculations of R&D intensity by groups of products (R&D expenditure/total sales). The groups classified as high‐technology products are aggregated on the basis of the Standard International Trade Classification (SITC).
2. Level of use of knowledge services in the OECD economies On the basis of selected economic indicators, this part of the article analyses the position of the Czech Republic among other OECD economies using economic indicators. The analysis was compared to all EU Member States and the other countries belonging to the OECD. Thus, a large sample was chosen deliberately, because of the opportunity for a complete comparison between states. All data was obtained from the European Statistical Office (EUROSTAT), where it is necessary to state their relevance. The time period used was 2005 to 2011. As mentioned above, the main objective of this part of the qualitative research was based on a comparative analysis and case studies in order to assess the level of Knowledge Services in comparison with selected OECD economies and subsequently; based on this information, to determine the characteristic level of Knowledge Services in this country by synthesis and generalisation. Among the targets of this research, we may include:
To characterize the economic level of OECD economies (using economic indicators: the development of the Gross Domestic Product at market prices (%), Average Annual Growth Rate (AAGR) of the value‐added by a high‐tech KIS sector, EU‐27 ‐ 2000‐2005 and the evolution of Knowledge Workers in services).
To determine the economic value‐added of enterprises belonging to the Knowledge Services ‐ KIS, LKIS (using indicators, number of enterprises, turnover in thousands of EURO, or the total cost of sold goods “services” in thousands of EURO, and the average cost per employee in thousands. EURO).
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Zuzana Tučková and David Tuček
2.1 Characteristics of the economic level of individual OECD economies The following indicators were selected to characterise the macroeconomic level of individual OECD countries:
Development of gross domestic product at market prices (%);
Development of the share of workers in Knowledge Intensive Services as % (Quaternary sector);
Average Annual Growth Rate (AAGR) of the value‐ added by a high‐tech KIS sector, EU‐27, 2000‐2005.
One of the basic indicators of economic growth is the indicator of the development of Gross Domestic Product. To assist analysis, Gross Domestic Product at market prices was chosen as an indicator. In the majority of selected countries over the last seven years when the percentage change has dropped on a year by year basis. This is due primarily to the global crisis, which is reflected in all of the analysed countries. The data in Table 1 depicts employment in Knowledge Intensive Services in individual countries as a share of total employment. Table 1: Employment in services demanding on knowledge – share in overall employment in %, (i) – transport, 1 storage, links; (b) – other economic activities (services) ‐ without 51.57 and 90 (CZSO, 2011)
The data source is the Labour Force Survey (CLFS ‐ Community Labour Force Survey). The definition of Knowledge Intensive Services, including high‐tech services, according to EUROSTAT, is based on a selection of the relevant NACE Rev. 1.1 2 Digital Level and is oriented on the proportion of high‐skilled labour in these 1
Available at: http://apl.czso.cz/ode/tab/tsc00012.htm
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Zuzana Tučková and David Tuček sectors. It is necessary to note that Knowledge‐based Services are classified according to the NACE Rev. 1.1 2 Digital Level, which was in force until 2008. After the changeover to NACE Rev. 2, alternations in the area of Knowledge Services occurred. From Table 1, the fact a growing tendency in each year is evident, which also is reflected in visible changes in the economies of individual countries, i.e., there is a spillover of primary or secondary sector employees´ activities ‐ mostly to the Knowledge Services. The largest proportion of workers in Knowledge‐based Services is in countries like Denmark, the Netherlands, Finland, as well as Iceland and Norway (up to 46%) and Great Britain. The Czech Republic, with its nearly 26%, has outstripped such countries like Portugal, Greece, or Romania. It should be noted however that its position is in the lower half. The value‐added is an important indicator for the measurement of economic performance and shows how much the added‐value of companies´ services (or products), rather than what is offered on the market. From analysis AAGR it is clear that the value‐added of EU High‐tech KIS + is growing at an annual growth rate of 6.6%, between 2000 and 2005. But, in each Member State, there were significant differences. For instance, in 2004 and 2007, most of the new Member States had great growth, while Luxembourg and Greece experienced a large decline in value‐added according to EU High‐tech KIS +. The Czech In comparison, the Czech Republic does not have a bad standing, but it is essential to realise that other Eastern European countries have faster growth (e.g. Poland, Hungary, Romania). Here, it must also be noted that the values in the individual areas are calculated according to NACE Rev. 1.1, ‐ which applied only to 2008.
2.2 Analysis of KIS enterprises in comparison with OECD countries In the context of the given chapter, enterprises falling under the KIS and LKIS categories are analysed, and broken‐down according to the literature. For comparison with other OECD countries purposes, the following indicators were selected:
Number of companies (in absolute values);
Turnover of enterprises (in. EURO);
Average personnel costs per employee in thousands. EURO).
The results of the comparative analysis are presented in the following two tables: Table 2: Number of enterprises in the KIS‐High‐tech and KIS‐Market (Author´s elaboration, according to 2 EUROSTAT, 2012) Stát/ rok
2008 2008 KIA KIABI
2009 KIA
2009 KIABI
2010 KIA
2010 KIABI 2011 KIA
2011 KIABI
European Union (27 countries)
74 164
28 715
74 769
28 749
74 953
KIABI
75 699
29 061
Belgium
1 819
656
1 818
634
1 866
651
1 853
664
Bulgaria
840
271
831
275
787
258
759
244
Czech Republic
1 384
551
1 416
551
1 459
569
1 477
594
Denmark
1 019
415
1 051
415
1 049
421
1 035
412
Germany
13 847
5 655
14 139
5 838
14 185
5 837
14 564
5 865
Estonia
180
60
183
59
179
54
188
63
Ireland
774
373
772
362
772
352
760
349
Greece
1 407
485
1 399
480
1 392
470
1 350
456
Spain
5 758
2 364
5 674
2 208
5 760
2 110
5 763
2 121
France
9 972
3 464
9 962
3 476
9 991
3 533
10 118
3 673
Italy
7 583
3 135
7 481
3 066
7 419
3 071
7 478
3 027
Cyprus
130
55
125
52
127
53
127
55
Latvia
308
88
286
87
285
88
286
86
2
Available from: http://appsso.eurostat.ec.europa.eu/nui/setupModifyTableLayout.do
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Zuzana Tučková and David Tuček
Stát/ rok
2008 2008 KIA KIABI
2009 KIA
2009 KIABI
2010 KIA
Lithuania
434
433
112
432
112
2010 KIABI 2011 KIA 115
433
2011 KIABI 120
Luxembourg
110
48
122
54
125
57
125
55
Hungary
1 275
492
1 255
462
1 292
479
1 305
493
Malta
61
25
62
25
63
26
68
27
Netherlands
3 138
1 397
3 092
1 300
3 045
1 250
3 030
1 228
Austria
1 370
554
1 415
569
1 437
578
1 415
569
Poland
4 185
1 280
4 380
1 390
4 512
1 427
4 534
1 476
Portugal
1 320
430
1 321
418
1 306
399
1 342
415
Romania
1 707
499
1 744
513
1 756
532
1 790
567
Slovenia
298
119
305
124
309
126
308
126
Slovakia
677
242
686
237
698
233
716
245
Finland
905
388
893
368
873
365
891
372
Sweden
1 871
747
1 856
739
1 902
758
1 946
787
United Kingdom
11 791
4 809
12 069
4 935
11 933
4 776
12 038
4 974
Iceland
73
31
70
30
69
29
67
29
Norway
918
340
944
362
928
345
969
371
Switzerland
1 652
801
1 735
826
1 679
824
1 717
845
Croatia
418
151
425
143
426
148
416
148
Former Yugoslav Republic
:
:
:
:
:
:
166
46
Turkey
:
:
3 815
992
4 031
1 067
4 254
1 098
United States
:
:
55 012
23 367
54 846
23 076
54 954
23 524
Japan
21 210
10 310
21 130
10 020
21 330
9 960
:
:
From Table 2, it is clear that the number of enterprises belonging to selected KIS activities is increasing annually, which is evident in most states. Once again however, the lines between countries with economic problems and their slowdown are mingled, for instance with emerging businesses in the given service sector; which of course, prevents higher economic development, e.g. Portugal or Spain. Here, the Czech Republic occupies a very respectable place in comparison to Poland for example, where the market of that state is much bigger and yet despite this, we can say that the number of firms is comparable. Table 3: Turnover of enterprises belonging to the KIS‐High‐tech and KIS‐Market sectors (Author´s elaboration, 3 according to EUROSTAT, 2012)
State/year
2008 KIS‐High 2008 KIS Tech ‐ Market
2009 KIS‐ High Tech
2009 KIS ‐ Market
State/year
2008 KIS‐ High Tech
2008 2009 2009 KIS ‐ KIS‐High KIS ‐ Market Tech Market
European Union (27 countries)
1 032 325
1 550 354
:
1 402 445
Luxembourg
:
:
:
:
Belgium
:
52 568
29 503
:
Hungary
10 787
13 395
10 301
12 131
Bulgaria
3 129
3 799
3 148
3 832
Netherlands
49 757
111 168 51 705 109 240
Czech Republic
12 805
21 598
12 014
19 134
Austria
16 711
31 272
16 509
29 848
Denmark
20 509
49 907
:
:
Poland
:
:
19 505
:
3
Available here: http://appsso.eurostat.ec.europa.eu/nui/setupModifyTableLayout.do
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Zuzana Tučková and David Tuček
State/year
2008 KIS‐High 2008 KIS Tech ‐ Market
2009 KIS‐ High Tech
2009 KIS ‐ Market
State/year
2008 KIS‐ High Tech
Germany
190 152 252 837
184 320
253 408
Portugal
12 695
:
12 412
:
2008 2009 2009 KIS ‐ KIS‐High KIS ‐ Market Tech Market
Estonia
1 210
:
:
:
Romania
9 231
9 758
7 815
8 048
Ireland
:
21 590
:
17 999
Slovenia
2 933
4 414
2 786
3 995
Greece
:
:
:
:
Slovakia
4 246
4 038
3 748
:
Spain
77 178
94 785
Finland
:
:
:
:
France
:
149 990
:
Sweden
:
:
29 755
:
102 298
124 466
United Kingdom
219 133 314 437 188 207 266 911
Italy
105 074 74 376
:
106 729 147 244
Cyprus
987
:
951
:
Norway
17 835
:
16 817
38 621
Latvia
1 376
:
1 159
:
Switzerland
:
:
:
:
Lithuania
1 561
2 229
1 369
1 617
Croatia
:
:
:
:
Tab. 3 complements the data in the previous table. It is included because of a closer examination of selected KIS activities. Here for example, it is possible to assess just how good a performance firms in the selected services Have. For example, in 2009, the CR had 203,171 companies providing selected KIS services; in Germany, it was 419,975 businesses ‐ which is almost twice as much. But if you look closely at individual turnovers in the same year, it must be mentioned that the turnover of these enterprises in the Czech Republic in 2009 amounted to approximately 31 million EURO, while in Germany it was 437 million EURO. Thus, the turnover of enterprises is up to 14 times higher than that of the Czech Republic. If we look at the other Eastern European countries, the situation is similar, which may be caused by cheaper purchasing of the given service, for example. Table 4: Average personnel costs per employee in the KIS‐HTCH and KIS‐ Market sectors (Author´s elaboration, 4 according to EUROSTAT, 2012)
State/year
2008 KIS‐ High Tech
2008 KIS ‐ Market
2009 KIS‐ High Tech
2009 KIS ‐ Market
State/year
2008 KIS‐ High Tech
2008 KIS ‐ Market
2009 KIS‐ High Tech
2009 KIS ‐ Market
European Union (27 countries)
292
365
284
347
Luxembourg
:
:
:
:
Belgium
:
525
394
:
Hungary
129
147
121
135
Bulgaria
52
64
57
67
Netherlands
:
:
314
407
Czech Republic
155
176
154
168
Austria
355
377
350
387
Denmark
377
517
:
:
Poland
:
:
108
:
Germany
309
349
308
359
Portugal
170
:
167
:
Estonia
104
:
:
:
Romania
73
68
58
65
Ireland
:
473
:
450
Slovenia
169
233
169
229
Greece
:
:
:
:
Slovakia
117
147
147
:
Spain
257
315
267
323
Finland
:
:
:
:
4
Dostupné z http://appsso.eurostat.ec.europa.eu/nui/setupModifyTableLayout.do
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Zuzana Tučková and David Tuček
State/year
2008 KIS‐ High Tech
2008 KIS ‐ Market
2009 KIS‐ High Tech
2009 KIS ‐ Market
State/year
2008 KIS‐ High Tech
2008 KIS ‐ Market
2009 KIS‐ High Tech
2009 KIS ‐ Market
France
:
:
410
:
Sweden
0
0
327
:
Italy
286
372
289
370
United Kingdom
343
432
312
366
Cyprus
:
:
:
:
Norway
434
:
417
575
Latvia
77
:
Lithuania
72
113
73
:
Switzerland
:
:
:
:
73
102
Croatia
:
:
:
:
I used another indicator of average personnel costs per employee in each country for a closer examination of selected KIS services, viz. Tab. 6. While it is true that in most countries the data is undetectable, but given the numbers that are available, we can see the following:
All Eastern European countries have some of the lowest costs per employee;
These costs are about 50% lower;
Countries with economic problems have similar costs per employee to Eastern European countries;
Norway has the highest cost per employee (up to three times more as compared to the Czech Republic);
The Czech Republic attains half the average cost per employee in the EU.
3. Conclusion The Czech Republic should follow the trend of economically developed countries (e.g. Germany, Belgium, Great Britain, the Netherlands, Austria), since these countries employ a higher proportion of workers in knowledge‐based services and thus, achieve higher GDP growth. The Czech Republic is well‐prepared for this trend since it has developed a fairly broad base of businesses with KIS and LKIS knowledge services. It is necessary to state that their turnover to‐date has only reached eight percent as compared to developed European countries, but the competitiveness of the Czech Republic in this context is high because the personnel costs for workers in KIS and LKIS knowledge‐based services are often up to fifty percent lower. In the Czech Republic, the share of people with a university education (%age of population aged 15‐64 years) in 2010 was 14.5%, but in the case of the EU‐27 or Germany in 2010, it was 22.7% and in France, even 28.1%. From the long‐term perspective, technical and scientific disciplines (e.g. Sciences, Maths, Computers, Engineering, Manufacturing and Construction) will prove to be the most important areas for the development of the economy in the future. Even the Czech Republic however, is threatened by the outflow of students from these areas because the EUROSTAT database depicts that if in 2000, a total of almost 32% enrolled in technical and natural science disciplines at Czech universities; nine years later, it was less than 26%. In the future, however, there should be a dramatic change since a third of the economically active population currently works in the secondary sector. From a strategic point of view, it is impossible to realise such a high transformation of workers from one sector to another; because, for instance, such change in the secondary sector over the last ten years (2001‐2011) was a mere 7% and includes the effects of the global economic crisis. Another direction of the development of research activities should be the same as in Sweden oriented on the fields of Electrical Engineering and Optics, transport resources and some of the other Engineering and Chemistry sectors. Furthermore, in the aforementioned country, one of the priorities of the national innovation strategy in each sector research is the question of research in Engineering, Sustainable Development and improved knowledge transfer between universities, industry, institutions and companies, which could prove to be the right way for the Czech Republic.
Acknowledgements The authors are thankful to the Operational Programme Education for Competitiveness co‐funded by the European Social Fund (ESF) and national budget of the Czech Republic, the grant No. CZ.1.07/2.4.00/31.0096 ‐ “Building partnerships and strengthening cooperation in the field of lean manufacturing and services,
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Zuzana Tučková and David Tuček innovations and industrial engineering with the emphasis on the competitiveness of the Czech Republic”, which provided financial support for this research.
References Boyer, J., 2010. The Plaid Avenger's World. 4th ed., revised. USA: Kendall Hunt. ISBN 978‐0‐7575‐8292‐9. Fellman, D. J., Getis, A., Getis, J., 1997. Human Geography: Landscapes of Human Activities. 5th ed. USA: Times Mirror Higher Education Group, Inc. ISBN 0‐697‐29038‐7. Brunn, S. D., Williams, J. F., Zeigler, D. J., 2003. Cities of the World: World Regional Urban Development. Maryland: Rowman & Littlefield. ISBN 978‐0‐ 8476‐ 9898‐1. Miles, I. a kol., 1995. Knowledge –Intensive Business Services: Users, Carriers and Sources of Innovation. Luxemburg: EIMS Publication.. Miles, I, 1993. Services in the New Industrial Economy. Futures. Vol 25, No. 6. Miozzo, M., Grimshaw, D., 2006. Knowledge Intensive Business Services. USA: Edward Elgar Publishing Limited. Muller, E., 2001. Innovation Interactions between Knowledge‐Intensive Business Services and Small and Medium‐Sized Enterprises. New York: Physica‐Verlag Heidelberg. ISBN 3‐7908‐1362‐1. OECD. 2006. Innovation And Knowledge‐Intensive Service Activities. Francie: OECD Publishing. ISBN‐92‐64‐02273‐2. Tučková, Z. 2012. Importance of Knowledge Services in the Czech Republic and Germany: A Case Study. In Proceedings of the 13th European Conference on Knowledge Management. Cartagena: Academic Publishing International Limited. ISBN 978‐1‐908272‐63‐8. Toivonen, M. 2004. Expertise as Business: Long‐Term Development and Future Prospects of Knowledge‐Intensive Business Services (KIBS), PhD Thesis, Helsinky University of Technology, Finland.
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A Three‐Dimensional Model of Identifying Barriers to Knowledge Management1 Anna Ujwary‐Gil Management Department, Nowy Sacz Business School – National‐Louis University, Nowy Sacz, Poland ujwary@wsb‐nlu.edu.pl. Abstract: This article is a continuation of the author’s discussion concerning the identification and classification of barriers to knowledge management, which appeared in 2012 (see References). The author divides these barriers into three interrelated dimensions: a) the prevalence of these barriers (exceeding the classical triad of: individual, organizational, and inter‐organizational levels), b) the stages of knowledge management processes, and c) the types of barriers. The three‐ dimensional model presented is based on the field of morphology utilized in the theory of combinations. This paper is conceptual in nature, and is aimed at identifying barriers to knowledge management, and new areas of research. Intrinsically related to knowledge management are numerous barriers influencing the process of managing this intangible resource both on an epistemological and ontological level. Researchers present different points of view in terms of performance, or causes of failure in projects and initiatives related to knowledge management. However, one fact remains indisputable. The causes and reasons for failure must be taken into account when considering potential limitations. The very awareness of their existence allows companies and individuals involved the opportunity to undertake appropriate steps in reducing their negative impact in the future. The approach presented in this article may lead to the recognition of areas considered as barriers relevant to the functioning of an organization, and with an analysis of the impact exerted by the external environment. Keywords: barriers, knowledge management, morphological analysis
1. The morphological analysis as a research tool The morphological analysis is one of heuristic methods of seeking new solutions and exploring possible research areas. A large number of scientists all over the world use this classic tool to explore problems and seek directions of possible solutions, such as: decision support modeling (Ritchey 2011), problem structuring (Ritchey 2006), or vocabulary development (Anglin 2000) and many, many others. It belongs to a group of combinatory methods, originating within the pragmatic field of heuristics. The method is focused on a specific goal and offers practical applications. Moreover, it belongs to heuristic methods (creative problem‐solving, here heuristic means: conducive to discovery). The development of its principles and empirical application is 2 attributed to F. Zwicky (1967, 1969), however, the forerunner of methods based on the theory of combinations is a medieval scholastic ‐ Raimundus Lullus (Ramon Llull; see more in: Bonner 2007). Lullus assumed that all possible judgments and new truths can be obtained by combining the fundamental and general concepts and predicates. Thus, even though he failed to prove his assumptions, he lay foundations for the methods based on a detailed analysis and systematic consideration of all possibilities through combinations of partial solutions. The morphological analysis according to the definition of its creator is “a logical and analytical method of searching for and attaining creative solutions to problems by means of a systematic analysis of all possible solutions”. The morphology of a problem field allows us to discern problems, create concepts or indicate the existence of possible directions of research. Therefore one of major functions of the morphological analysis is to search for new problems and research areas rather than ready solutions. Being a heuristic method it does not guarantee anything. It should be seen more as a method reducing ways of seeking the solution. This paper aims at applying the morphological analysis to the search for new research areas concerning identification of possible barriers to knowledge management. The paper is of conceptual nature and is based on the review of subject literature and the procedure applied in the morphological analysis. The morphological procedure (from Greek word morphë, which means form: Góralski 1980) distinguishes three stages in problem‐solving. In the first stage we precisely determine the field, scope and content of the problem, in the second one – we analyze the problem and identify independent elements or dimensions of the 1
The project was funded by the National Science Centre allocated on the basis of the decision number DEC‐2012/05/D/HS4/01338. Swiss astronomer who invented the morphological analysis in around 1940 using the knowledge and experience gained in the research on space rockets.
2
741
Anna Ujwary‐Gil problem and then specify each element, determining the attributes (alternatives) of particular dimensions. This method is logical and analytical in nature, however, in the second stage we perform free exploration of possible attributes of each dimension. It should be emphasized that among traditional alternatives we should also find unexpected attributes, original regardless of their usefulness or possibilities of application. As a result of combination we often come up with proposals of solutions which then can become starting point for a new, original product, service or problem to be solved. Like in each intuitive method, delayed evaluation of alternatives accepted for further analysis and general conditions supporting spontaneous group thinking are of key significance. In the third stage – synthesis of the solution ‐ we construct a morphological chart. Within the synthesis of the solution Zwicky also established ways of evaluating solutions, reducing the morphological chart (for example by means of Moles discovery matrix: Góralski 1980) and a choice of a solution. For the effective use of the morphological analysis its users must have extensive knowledge of the subject (process) of research and an ability of practical application of knowledge acquired by them. The morphological chart is a rather auxiliary tool and offers a starting point for the so‐called hard variation of the method in which we juxtapose consecutive attributes of each parameter in order to achieve a set number of the so‐called morphological products or the possibilities of solutions to the problem defined in the first stage.
2. Areas of possible application of the morphological analysis Subject literature contains numerous examples of applications of the morphological analysis and two‐ dimensional discovery matrix. The author divided them into: intuitive (stimulating imagination), technical‐ technological (making inventions, new products, improving existing products) and searching for new scientific and research possibilities. In the last field we can list the following examples (Zwicky 1969; Kaufmann et al. 1975; Guilford 1959; Scerri 2006; Ritchey 2011):
making a list of all possible energy conversions
developing methodology of simultaneous foreign language teaching
examining relationships between various fields of scientific research and industrial production
Guilford’s model of intellect
Mendeleev’s periodic table of elements
creating concepts of: new market segments and applications and new ways of developing competitive advantage
attempts at systemizing scientific terminology
analysis of social problems.
A well‐known example, listed above, is empirical verification of the use of discovery matrix in seeking new research possibilities, made, for example by Mendeleev. When he organized separate categories of chemical elements on a two‐dimensional table, empty spaces could be found at the intersections of columns and rows, drawing the scientist’s attention to searching for simple substances that had not existed yet and this led to the discovery of radioactive substances. A result of the morphology of a problem field is also a full, three‐ dimensional model of intellect developed by Guilford (Guilford 1959). Using Zwicky’s morphological analysis the scientist could, through combining various elements of three dimensions: operations, contents and products, determine all theoretically existing mental capacities. Another interesting example is provided by Kaufmann et al (Kaufmann et al. 1975). The authors present the use of the discovery matrix to seek relations (or their lack) between various fields of science and industry. The idea of the research consists in identifying possible contribution and application of science in economy. If we construct a similar table, taking into account contemporary industries (mostly high technologies), where – as a result of conjunction of randomly juxtaposed elements of the matrix new, unclassified branches of industry may appear.
3. Identifying barriers to knowledge management using the morphological analysis It should be noticed that the morphological analysis has the so‐called strong and weak forms. In the strong variation we make combinations of the listed attributes of a particular dimension with all the other attributes, which constitutes its advantage on one side, as it provides us with a possibility of thorough analysis and examination of all originated morphological products, but on the other hand, such examination is time‐ consuming. Moreover, a side effect of methods based on combination theory is a certain number of products
742
Anna Ujwary‐Gil which are clearly absurd and for which we cannot use rational evaluation criteria. There might also be traditional solutions, however, the main goal of this method is to search for new and original solutions, especially as the subject of the research is the search for new products or improving the existing ones. One of the requirements of effective application of the morphological analysis is creation of morphological charts (boxes) with low dimensionality and a small number of elements created as a result of exploration of main categories (Table 1). This is obviously possible provided that we use the method without any auxiliary tools facilitating quick combination and analysis. This condition, however, in the era of advanced computer systems supporting creative thinking processes, is losing its significance. Based on computer programs, new methods are created, allowing to identify attributes, analyzing them quickly, making instant combinations, after which proposals of solutions are selected and specified along particular evaluation criteria. Here, computers show great advantage over humans in this respect. Morphological analysis was for the first time used for identifying new areas of research connected with identification of barriers to knowledge management. For this purpose the author divided barriers to knowledge management into three dimensions: the level of knowledge analysis, types of barriers and the knowledge process while the columns were assigned attributes (features of the above dimensions), as in Table 1 below: Table 1: Morphological chart Attributes Dimensions
Barrier level
(a1) Individual
(a2) Group
(a3) Organization
(a6) Inter‐sector (inter‐industry) (b1) Locating Searching (b6) Developing Learning
(a7) Country
(a8) Global
(a4) Inter‐ organizational
(a5) Sector (industry)
(b2) Identifying Recognizing (b7) Codifying
(b3) Acquiring
(b4) Organizing
(b5) Gathering
(b8) Transfer, diffusion, sharing (b13) Measuring
(b9) Making available Popularizing (b14) Evaluating
(b10) Adapting Absorbing (b15) Controlling
(c3) Cultural
(c4) Intercultural
(c5) Organizational
(c8) Systemic
Knowledge process
(b11) Implementing and using
Barrier type
(c1) Psycho‐social
(c6) Financial
(b12) Preserving (organizational memory) (c2) Technical‐ technological (c7) Legal
The main dimensions are: level of knowledge, knowledge process and types of barriers. Knowledge may be analyzed on various levels, thus attributes of this dimension may be: an individual (as the main “carrier” and source of implicit knowledge) and a team, organization and inter‐organizational associations, on the macro level the sector (industry) inter‐sector (Inter‐industry), country and global attributes were listed. Barrier types attributes comprise: psycho‐social, technical‐technological, cultural, intercultural, organizational, financial (resulting from deficit of resources), legal (connected with protection of intellectual property and developed know‐how) and systemic attributes. A definite catalogue of these barriers cannot be created, though, due to individuality and complexity of human nature. However, we can talk of a certain group of barriers. Psychological barriers on the level of an individual employee could be barriers related to (Project EQAL 2007):
natural fear of change,
protection of their own interest and position,
fear of one‐way transfer of valuable experiences,
743
Anna Ujwary‐Gil
unwillingness to do extra work,
limited need for professional and personal development,
lack of initiative,
inability to acquire and evaluate knowledge on one’s own,
lack of courage to share one’s observations,
fear of making a mistake and its consequences
inability to receive criticism and making constructive criticism
inability to ask for advice or help.
Difficulties and barriers to knowledge management are not limited only to the level of an individual employee but also appear on the level of an organization, therefore organizational barriers may include:
lack of clearly determined strategy or persistence in its implementation,
lack of coupling with the field of human resources management,
incorrect information flow,
developed, hierarchical organizational structure,
lack of people with new knowledge joining the company,
lack of staff integration,
unfavorable corporate culture,
losing experienced employees who take early retirement,
fear of information leaks from the company.
Social barrier, on the other hand, include: inability to work in a group, low awareness of benefits derived from knowledge management, low involvement of management in implementing and monitoring knowledge management, lack of a leader, fear of investing in employees who may leave for another company and bring no benefits for our business, or national and cultural differences. Serious problems in knowledge management appear also in the technical sphere. The major technical and technological barriers and financial difficulties in the level of an individual employee, organization and macro‐environment (systemic barriers) cover:
inability to use new technologies,
incomprehensible codification of knowledge and freedom of interpretation,
difficult access to the latest research achievements,
lack of possibilities of financing the services related to access and acquisition of new skills and knowledge,
unfavorable architecture of the organization and distance,
technical infrastructure that is not integrated or that does not exist,
lack of a system of archiving information,
inability to substitute an employee during their training,
limited possibility of making expenditure on implementation and realization of the knowledge management concept,
wrong priorities leading to seeking savings in expenditures on improving employees’ qualifications,
deficit of knowledge management specialists (depending on a country),
lack of highly‐specialized and flexible trainings,
education system that is ill‐fitted to meet the needs of economy and its inertia,
lack of contacts with the field of science and research,
lack of a uniform system of acknowledging qualifications gained outside the formal education system,
poor financing of science and research programs.
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Anna Ujwary‐Gil
The knowledge process in this form is extremely specific, we took into account all possible stages of process presentation of knowledge, that is: locating, acquiring, organizing, gathering, developing, codifying, transferring, making available, adapting, implementing and using, preserving, measuring, evaluating and controlling knowledge. Special attention should be paid to the process of measuring and evaluating knowledge, which offers a possibility of conducting the next stage of knowledge management, that is controlling knowledge. Without measuring knowledge we are unable to show the dynamics of changes taking place on the level of examined variables in a given period of analysis.
Based on the morphological chart constructed in this way we perform the so‐called morphology of a problem field, consisting in combining particular attributes of specific dimensions (level, process, type). For this purpose we systematically juxtapose each attribute with each remaining one and obtain 960 combinations of possible 3 barriers which not only need generating (identifying) but also evaluating and verifying empirically . In the weak variation of the morphological analysis we use Moles’ discovery matrix. We pick any two dimensions and then we juxtapose their attributes in the initial matrix with two entrances, as we can observe in Table 2 below. Table 2: Initial matrix Dimension bj Dimension ai (a1) Individual
(b1) Locating / Searchin g
(b2) Identifying/ Recognizin g
(b3) Acquiring
(b4) Organizin g
(b5) Gatherin g
(b6) Developing Learning
(b7) Codifying
(b8) Transfer, diffusion, sharing
a1 b 1
a1 b2
a1 b3
a1 b 4
a1 b5
a1 b6
a1 b7
a1 b 8
(a2) Group (a3) Organizati on (a4) Inter‐ organizatio nal (a5) Sector (industry) (a6) Inter‐ sector (inter‐ industrial) (a7) Country
a2 b 1
a2 b2
a2 b3
a2 b 4
a2 b5
a2 b6
a2 b7
a2 b 8
a3 b 1
a3 b2
a3 b3
a3 b 4
a3 b5
a3 b6
a3 b7
a3 b 8
a4b1
a4 b2
a4 b3
a4 b 4
a4 b5
a4 b6
a4 b7
a4 b 8
a5 b 1
a5 b2
a5 b3
a5 b 4
a5 b5
a5 b6
a5 b7
a5 b 8
a6b1
a6 b2
a6 b3
a6 b 4
a6 b5
a6 b6
a6 b7
a6 b 8
a7 b 1
a7 b2
a7 b3
a7 b 4
a7 b5
a7 b6
a7 b7
a7 b 8
a8 b 1
a8 b2
a8 b3
a8 b 4
a8 b5
a8 b6
a8 b7
a8 b 8
(a8) Global
Table 2 continued: Dimension bj Dimension ai (a1) Individual
(b9) Making available Popularizi ng
(b10) Adapting Absorptio n
(b11) Implemen tation and use
(b12) Preserving (organizati onal memory)
(b13) Measuring
(b14) Evaluating
(b15) Controllin g
a1 b 9
a1b10
a1b11
a1b12
a1b13
a1b14
a1b15
3
Due to the size of the analyses, generated morphological products will not be identified or discussed in detail here
745
Anna Ujwary‐Gil Dimension bj Dimension ai (a2) Group (a3) Organizatio n (a4) Inter‐ organization al (a5) Sector (industry) (a6) Inter‐sector (inter‐ industry)
(b9) Making available Popularizi ng
(b10) Adapting Absorptio n
(b11) Implemen tation and use
(b12) Preserving (organizati onal memory)
(b13) Measuring
(b14) Evaluating
(b15) Controllin g
a2 b 9
a2b10
a2b11
a2b12
a2b13
a2b14
a2b15
a3 b 9
a3b10
a3b11
a3b12
a3b13
a3b14
a3b15
a4 b 9
a4b10
a4b11
a4b12
a4b13
a4b14
a4b15
a5 b 9
a5b10
a5b11
a5b12
a5b13
a5b14
a5b15
a6b9
a6b10
a6b11
a6b12
a6b13
a6b14
a6b15
(a7) Country
a7 b 9
a7b10
a7b11
a7b12
a7b13
a7b14
a7b15
(a8) Global
a8 b 9
a8b10
a8b11
a78b12
a8b13
a8b14
a8b15
The intersections between columns and rows of the initial matrix generate products which are then evaluated alongside established criteria (for example reliability, rationality, innovativeness). Another step is to reduce products in Table 2. In this process we select those products from the matrix (marked in Table 2) which can determine possible research directions and we juxtapose them also in a matrix with two entrances and with another dimension ‐ c. The number of matrixes is determined by the number of dimensions (elements), as exploration is conducted until we juxtapose the last element with morphological products of the previous matrix. Thus the number of matrixes in this case equals 3 (Table 3): Table 3: Final matrix Dimension ck Dimension a ib j a4 b 1
(c1) Psycho‐ social
(c2) Technical‐ technologica l
(c3) Cultural
(c4) Inter‐ cultural
(c5) Organization al
(c6) Financial
(c7) Legal
(c8) Systemic
a4b1c1
a4b1c2
a4b1c3
a4b1c4
a4b1c5
a4b1c6
a4b1c7
a4b1c8
a4 b 5
a4b5c1
a4b5c2
a4b5c3
a4b5c4
a4b5c5
a4b5c6
a4b5c7
a4b5c8
a4b11
a4b11c1
a4b11c2
a4b11c3
a4b11c4
a4b11c5
a4b11c6
a4b11c7
a4b11c8
a4b12
a4b12c1
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a4b15c1
a4b15c2
a4b15c3
a4b15c4
a4b15c5
a4b15c6
a4b15c7
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a5 b 5
a5b5c1
a5b5c2
a5b5c3
a5b5c4
a5b5c5
a5b5c6
a5b5c7
a5b5c8
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a5b11c1
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a5b12
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a5b15c2
a5b15c3
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a6 b 1
a6b1c1
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a6b1c4
a6b1c5
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a6b1c7
a6b1c8
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Anna Ujwary‐Gil Dimension ck Dimension a ib j a6b12
(c1) Psycho‐ social
(c2) Technical‐ technologica l
(c3) Cultural
(c4) Inter‐ cultural
(c5) Organization al
(c6) Financial
(c7) Legal
(c8) Systemic
a6b12c1
a6b12c2
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a6b15c2
a6b15c3
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a6b15c5
a6b15c6
a6b15c7
a6b15c8
a7 b 1
a7b1c1
a7b1c2
a7b1c3
a7b1c4
a7b1c5
a7b1c6
a7b1c7
a7b1c8
a7 b 5
a7b5c1
a7b5c2
a7b5c3
a7b5c4
a7b5c5
a7b5c6
a7b5c7
a7b5c8
a7b11
a7b11c1
a7b11c2
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a7b11c4
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a7b11c7
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a7b15c1
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a7b15c3
a7b15c4
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a7b15c7
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a8 b 9
a8b9c1
a8b9c2
a8b9c3
a8b9c4
a8b9c5
a8b9c6
a8b9c7
a8b9c8
a8b11
a8b11c1
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a8b11c8
a8b12
a8b12c1
a8b12c2
a8b12c3
a8b12c4
a8b12c5
a8b12c6
a8b12c7
a8b12c8
a8b13
a8b13c1
a8b13c2
a8b13c3
a8b13c4
a8b13c5
a8b13c6
a8b13c7
a8b13c8
a8b14
a8b14c1
a8b14c2
a8b14c3
a8b14c4
a8b14c5
a8b14c6
a8b14c7
a8b14c8
a8b15
a8b15c1
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a8b15c3
a8b15c4
a8b15c5
a8b15c6
a8b15c7
a8b15c8
As we can see in the final matrix, barriers have been selected for further exploration depending on where they appear, emphasizing the inter‐organizational, sector (industry), inter‐sector (inter‐industrial), national and global levels. As far as the stage of knowledge management process is concerned, practically each stage is worth considering here. However, we skipped mostly the processes of knowledge transfer, diffusion and sharing, as they constitute a very popular area of research for scientists all over the world. Some examples of products that could be further explored and empirically verified are provided in Table 4 below: Table 4: Three‐dimensional morphological products of the final matrix Morphological product
a4b14c4 a5 b 5 c 2 a6b11c7 a7b15c8 a8 b 9 c 6
Description of the product Intercultural barriers to knowledge management on the inter‐organizational level related to the process of knowledge evaluation Technical and technological barriers to knowledge management on the sector (industry) level related to the process of knowledge gathering Legal barriers to knowledge management on the inter‐sector (inter‐industrial) level related to the process of knowledge implementation and use
Systemic barriers to knowledge management on the level of a particular country related to the process of knowledge controlling Financial barriers to knowledge management on the global level related to making knowledge available and popularizing it
4. Directions for further research There is little research dealing with the problem of analyzing barriers to knowledge management on the inter‐ organizational, sector (industry), inter‐sector (inter‐industrial), national or global levels. The author has found only a short review of research in this area on the level of construction industry (Carrillo et al 2004) and pharmaceutical industry (Lilleoere, Hansen 2010) as well as on the national level (Kuznetsov, Yakavenka 2005). It would be interesting to examine the differences between barriers to knowledge management between countries to see their specificity and to find out the origin of these differences. On the other hand, the concept related to seeking ideas and knowledge between sectors or industries is called technology brokering (Hargadon 2003). It is based on, in each case listed here, on re‐combining old ideas, joining distant
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Anna Ujwary‐Gil technologies and concepts taken from particular ideas in order to generate new technological combinations, new ways of initiating revolution. Barriers to knowledge management on the individual, team and organizational levels have already been subject of numerous research (Martini, Pellegrini 2005; BenMoussa 2009; Sharma et al. 2012; Vashisth et al 2010). On the other hand, as far as the process of knowledge and identifying barriers in particular stages is concerned, the least explored area is the stage of locating/seeking, identifying/recognizing, acquiring, implementing/using, measuring, evaluating and controlling knowledge. Talking of measuring, evaluating and controlling knowledge the author does not mean the methods of evaluating, measuring and estimating intellectual capital, as these are widely exposed in subject literature (the author wrote about them in her book (2009). It would be more purposeful to refer to the methodology of knowledge audit understood as a tool for analyzing and evaluating organization’s knowledge taking into account its usefulness and opportunities of achieving competitive edge by applying it (see more in Ujwary‐Gil 2011; Levantakis, Helms, Spruit 2008). In the process presentation of knowledge management and barriers appearing at various stages of this process, authors have conducted abundant research, especially into knowledge transfer and sharing (Riege 2005; Yih‐Tong Sun, Scott 2005) and cultural barriers to knowledge management (Levy et al. 2010; Khakpour 2009; de Long, Fahey 2000). Thus the area for potential research may focus more on the analysis of intercultural, legal and systemic barriers to knowledge management on the indicated stages of analysis (inter‐organizational, sector (branch), inter‐sector (inter‐industry), national and global ones.
References Anglin, J.M. (2000) Vocabulary Development: A Morphological Analysis, 1st ed. Wiley‐Blackwell. Bonner, A. (2007) The art and logic of Ramon Llull: a user’s guide. Brill. BenMoussa Ch. (2009) Barriers to Knowledge Management: A Theoretical Framework and a Review of Industrial Cases, World Academy of Science, Engineering and Technology, No. 30, pp. 901‐912. Carrillo P., Robinson H., Al‐Ghassani A., Anumba Ch. (2004) Knowledge management in UK construction: Strategies, Resources, Barriers, Project Management Journal, Vol. 35, No. 1, pp. 46‐56. De Long D.W., Fahey L. (2000) Diagnosing cultural barriers to knowledge management, Academy of Management Executives, Vol. 14, No. 4, pp. 113‐127. Góralski, A. (1980) Twórcze rozwiązywanie zadań. Warszawa: PWN. Guilford, J.P. (1967) The Nature of Human Intelligence, First Edition. McGraw‐Hill. Hargadon, A. (2003) How Breakthroughs Happen: The Surprising Truth About How Companies Innovate, 1st ed. Harvard Business Review Press. Kaufmann A., Fustier M., Drevet A. (1975) Inwentyka. Metody poszukiwania twórczych rozwiązań. Warszawa: WNT. Khakpour A., Ghahremani M., Pardakhtchi M.H. (2009) The relationship between organizational culture and knowledge management (cultural barriers and challenges of knowledge sharing), The Journal of Knowledge Economy & Knowledge Management, Vol. IV FALL, pp. 43‐58. Kuznetsov, A., Yakavenka, H. (2005) Barriers to the absorption of management knowledge in Belarus. Journal of Managerial Psychology, Vol. 20, No 7, pp. 566‐577. Levantakis, T., Helms, R., Spruit, M. (2008) Developing a Reference Method for Knowledge Auditing, in: Yamaguchi, T. (Ed.), Practical Aspects of Knowledge Management, Lecture Notes in Computer Science. Springer Berlin Heidelberg, pp. 147–159. Levy M., Hadar I., Greenspan S. and Hadar E. (2010) Uncovering cultural perceptions and barriers during knowledge audit, Journal of Knowledge Management, Vol. 14, No 1, pp. 114‐127. Lilleoere A. M., and Hansen E.H. (2011) Knowledge‐sharing enablers and barriers in pharmaceutical research and development, Journal of Knowledge Management, Vol. 15 No. 1, pp. 53‐70. Martini, A., Pellegrini, L. (2005) Barriers and levers towards knowledge management configurations: A case study‐based approach, Journal of Manufacturing Technology Management, Vol. 16, No 6, pp. 670‐781. Projekt EQUAL (2008) Modelowy System Zarządzania Wiedzą w Przedsiębiorstwie. Analiza barier w zarządzaniu wiedzą. Warszawa – Gdańsk. Riege, A. (2005) Three‐dozen knowledge‐sharing barriers managers must consider, Journal of Knowledge Management, Vol. 9, No. 3, pp.18‐35. Ritchey, T. (2006) Problem Structuring using Computer – Aided Morphological Analysis. Journal of the Operational Research Society, Special Issue on Problem Structuring Methods, No 57, pp. 792–801. Ritchey, T. (2011) Wicked Problems ‐ Social Messes: Decision Support Modelling with Morphological Analysis, 2011th ed. Springer. Scerri, E. R. (2006) The Periodic Table: Its Story and Its Significance. New York: Oxford University Press. Sharma B.P., Singh M.D., and Neha (2012) Knowledge Sharing Barriers: An Approach of Interpretive Structural Modeling, The IUP Journal of Knowledge Management, Vol. X, No. 3, pp. 35‐52. Ujwary‐Gil A. (2012) Identyfikowanie i klasyfikowanie barier zarządzania wiedzą, Studia i Prace Kolegium Zarządzania i Finansów SGH. Warszawa, No 115, pp. 169‐179. Ujwary‐Gil A. (2011) Audyt wiedzy przedsiębiorstwa, Przegląd Organizacji, No 2, pp. 11‐14.
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Anna Ujwary‐Gil Ujwary‐Gil, A. (2009) Kapitał intelektualny a wartość rynkowa przedsiębiorstwa, Warszawa: Ch&Beck. Vashisth R., Kumar R. and Chandra A. (2010) Barriers and Facilitators to Knowledge Management: Evidence from Selected Indian Universities, The IUP Journal of Knowledge Management, Vol. VIII, No. 4, pp.7‐24. Yih‐Tong Sun P., Scott, J.L. (2005) An investigation of barriers to knowledge transfer, Journal of Knowledge Management, Vol. 9, No. 2, pp. 75‐90. Zwicky, F. & Wilson A. (eds.) (1967) New Methods of Thought and Procedure: Contributions to the Symposium on Methodologies, Berlin: Springer. Zwicky, F. (1969) Discovery, Invention, Research ‐ Through the Morphological Approach, Toronto: The Macmillan Company.
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From KM Evaluation to Developing Evaluative Capability for Learning Christine van Winkelen and Jane McKenzie Henley Business School, University of Reading, UK Christine.vanWinkelen@henley.ac.uk Jane.McKenzie@henley.ac.uk Abstract: This conceptual paper considers the issues associated with evaluating the impact of knowledge management (KM) programmes and activities in the post‐bureaucratic organization. The metaphor of ripples and waves is used to describe multiple and varied interacting impacts of KM practices on individuals, groups and the organisation. There is reinforcement, dampening, cancellation and barriers to these ripples and waves of impact as a result of everything else that is happening in the organisation. Amongst many influential factors are other organisational development initiatives such as Lean or Six Sigma, leadership development initiatives and organisational restructuring, political considerations and existing embedded mindsets. This creates difficulties for knowledge managers asked to make direct cause and effect links between individual KM practices and aspects of organizational performance, particularly on the short timescale of budget cycles. This paper offers a new perspective on this issue based on examining current research in the disciplines of Strategy as Practice and evaluation and relating them to organizational learning and organizational knowledge theory. An argument is developed that moving from expecting to evaluate individual KM initiatives to a participatory approach to building evaluative capability into KM programmes offers two potential benefits: amplifying the impact of those programmes by positively developing the learning environment; and providing a more effective means of understanding what is working, what is not, and what improvements could look like. The principles that underpin building evaluative capability are founded in the language systems of the organization (the means by which learning happens). They include adopting approaches that prompt reflection and questioning, mobilising dialogue between groups, and developing contextual views of success . A participatory approach means seeking and enabling widespread involvement in those approaches. Action learning groups and appreciative inquiry workshops are identified as potentially useful approaches for knowledge managers to build this capability in practice. Developmental evaluation theory suggests that these would be most relevant within the development and implementation phases of KM activities, but it will be argued that the complex social context of a distributed knowledge system could make their application valuable beyond these. To build this capability, knowledge managers will need to develop critical thinking and evaluative questioning skills within the organisation, and demonstrate skilled facilitation and an awareness of complex dynamics associated with political considerations. Examination of the relevance of the principles to more hierarchical organizational structures is recommended, together with case‐study based research to examine their value and application in practice. Keywords: evaluation, evaluative capability, knowledge management, reflection, action learning, appreciative inquiry
1. Introduction This conceptual paper examines the challenges associated with evaluating the impact of knowledge management programmes and activities on organizational performance, starting from a metaphor of ripples and waves. Metaphors are comparative devices for illuminating the attributes of one object or activity by drawing on understanding of another (Andriessen 2008). Ripples and waves communicate the challenge of isolating the multiple, interacting and overlapping impacts of KM programmes and activities upon individuals, groups and the organisation, and highlights the reinforcement, dampening, cancellation and barriers to KM impact as a result of everything else that is happening. Many other organisational development initiatives such as Lean, TQM or Six Sigma, leadership development programmes or organisational restructuring, as well as political considerations and existing embedded mindsets are confounding influences, creating difficulties for knowledge managers asked to make direct cause and effect links between KM investments and aspects of business performance, particularly on the short timescale of budget cycles. This paper offers a new perspective on this issue based on examining current research in the disciplines of Strategy as Practice and evaluation and relating them to organizational learning and organizational knowledge theory. An argument will be developed that moving from expecting to evaluate individual KM initiatives to a participatory approach to building evaluative capability into KM programmes offers two potential benefits: amplifying the impact of those programmes by positively developing the learning environment; and providing a more effective means of understanding what is working, what is not, and what improvements could look like.
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Christine van Winkelen and Jane McKenzie The intention of this paper is to generate insights for knowledge managers as practitioners. The subject has not been examined from the perspective of the researcher studying, diagnosing or assessing learning organizations or knowledge management practices. As a consequence of this desire for research relevance, the paper offers key principles underpinning a new approach and examples of interventions that are consistent with these principles, which could form the basis of future research with practitioners. In the following sections, the nature of organizations as discursive learning systems and the organizational knowledge environment will first be examined as the social context in which knowledge management activities are undertaken. This is followed by a brief review of how the field of evaluation has developed to take account of social context. These themes are then brought together into a focus on evaluation in relation to knowledge management.
2. Organizations as discursive systems The scope of this paper is limited to organizations in which flexibility, participative management and efforts to sustain a consensus building dialogue predominate, rather than control, direction and an emphasis on hierarchy and rule‐following. This is typical of contemporary flat, networked and team‐based organizations and has been described as a post‐bureaucratic organization in which “knowledge is to be taken not only as a property of the individual mind of an expert or a knowledge worker, but also as something that is shared and which takes the form of accumulated experience and learning embodied in the organization’s culture, systems, processes and practices” (Moisander and Stenfors 2009, p229). In understanding these organizations, complexity theory provides a way of “looking at how systems of living, thinking individuals interrelate and why interactions between people and groups of people give rise to unexpected, creative and sometimes surprising behaviour” (McKenzie and van Winkelen 2004, p95). The organization is viewed as a discursive system in which “language does not merely reflect social reality but is the very means of constructing and reproducing the world as it is experienced” (Mantere and Vaara 2008, p343). These complex processes of discussion, negotiation and collective learning have been recognised in the development of the Strategy as Practice school over the last decade. This views strategy‐making and implementation in relation to prevailing organizational and societal practices (see Vaara and Whittington 2012 for a review of research in the field). Strategy practices, and by implication knowledge strategy and the implementation through knowledge management practices, do not happen in isolation, rather they are affected by organizational and wider social practices. It recognises that, as they adopt practices and respond to proposed changes, the everyday interactions and activities of those in the organisation include the gossiping and rumour‐mongering which influence sensemaking (Balogun and Johnson 2005) and thus the reality of implementation. In organizations, discourses, which are “linguistically mediated constructions of social reality” are the means through which “beliefs, values, and norms are reproduced and at times transformed in social life” (Mantere and Vaara 2008. p341). Wider participation enhances the learning potential of the system because “we need the stimulus of different perspectives to avoid the trap of ‘groupthink’, a collective bias that distorts our interpretation of signals and trends” (McKenzie and van Winkelen 2004, p67). Mantere and Vaara (2008) showed that certain discourses promote participation in strategic praxis. The first (self‐actualization) helps people find meaning in organizational activities by “in‐depth reflection concerning the identity of the organization and one’s role in it” (Mantere and Vaara 2008, p351). They develop by expanding their capacity to interpret linguistic distinctions. The second (dialogization) stimulates constructive dialogue up, down and across the organization, allowing associations between the refined linguistic interpretations of specific groups to be made. The final discourse (concretization) shows how socially agreed constructions are embedded within other organizational decision‐making processes, making the strategy process natural and transparent. (see McKenzie and van Winkelen 2004, chapter 3, for a more detailed review of the literature on how language processes mediate learning processes in organizations). Discourses that limit participation include mystification (top down approaches which limit questioning), disciplining (where the discourse is shaped by organizational command and control structures), and technologization (where a specific system provides the rules to be followed). In other words, they create barriers to reflecting, questioning, challenging assumptions and dialogue, thus limiting the adaptabilty of the organization as a discursive system.
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3. Organizational learning and knowledge A number of empirical investigations into the relationship between learning in organizations and organizational performance have been undertaken and a recent meta‐level review (Goh et al. 2012) suggested that learning capability has a positive relationship with organizational performance judged in both financial and non‐financial terms. In the review, learning capability was broadly defined as “the managerial practices, mechanisms and management structures that can be implemented to promote learning in an organization” (Goh et al. 2012, p94). Interestingly, this meta‐study reached the conclusion that organizational learning capability could be assessed using a single aggregated scale encompassing, amongst other things, “knowledge transfer, an experimenting culture, a learning orientation, knowledge acquisition and sharing, team work and group problem solving, shared vision, and leadership that supports learning and open‐mindedness” (Goh et al. 2012, p97). The fact that these need not be viewed as distinct constructs and that they together contribute to organizational learning capability, again implies that various organizational conditions, activities and practices that relate to knowledge and learning need to be viewed as interacting and reinforcing each other – creating and amplifying ripples and waves of impact. However, it is difficult for knowledge managers to know how best to shape the most influential set of knowledge and learning practices for a complex, changing environment. Alvesson and Karreman (2001, p995) comment “knowledge is an ambiguous, unspecific and dynamic phenomenon, intrinsically related to meaning, understanding and process, and therefore difficult to manage.” The Strategy as Practice approach supports the view of the organization as a distributed knowledge system, with knowledge being derived from the broader industrial and societal context, and being “continually (re)constituted through the activities undertaken within it” (Tsoukas 2005, p110). Organizations are in constant flux and management “can, therefore, be seen as an open‐ended process of coordinating purposeful individuals, whose actions stem from applying their partly unique interpretations to the local circumstances confronting them. Those actions give rise to often unintended and ambiguous circumstances, the meaning of which is open to further interpretation and further actions, and so on” (Tsoukas 2005, p111). It is evident that although a well‐developed learning capability offers the potential for improving overall performance, it is not straightforward to attribute performance outcomes to any particular change or practice. Knowledge managers influence knowledge processes within the distributed knowledge system. In line with this view, the following definition is adopted here: “KM is the set of processes that seeks to change the organization’s present pattern of knowledge processing to enhance both it and its outcomes ... the discipline of KM is the study of such processes and their impact on knowledge and operational processing and outcomes” (Firestone and McElroy 2005, p191). In complex situations, expertise, memory and embedded routines may result in a tendency to learning for improvement rather than for transformation. Single loop learning refines the current pattern of activities, while double loop learning involves the questioning of assumptions underlying current practices and their suitability for a changing environment (Senge 1990); both are needed for long term organizational sustainability (McKenzie and van Winkelen 2004). Organizational learning research identifies both how to develop learning capability and the potential barriers to organizational learning. The social system of work has been viewed as a set of nested complex systems of individuals, groups, organizations and networks of organizations and the 4I framework proposed as a set of processes that offer insights into organizational learning (Crossan et al. 1999). An individual’s intuitive understanding gained through experience and exposure to new knowledge is interpreted through discussion with the group, developing a shared understanding which can be integrated with other organizational knowledge and institutionalized through systems, structures and processes. Various barriers to these processes exist: Schilling and Kluge (2009) reviewed them comprehensively and categorised them in terms of individual, structural and societal factors. They act as potential barriers to the ripples and waves of impact from single and double loop learning processes. In contrast, other initiatives also build learning capability and potentially amplify the impact of KM initiatives, including for example introducing “lean” and “agile” principles into the organization (Putnik and Putnik 2012), implementing business process reengineering initiatives (Vakola and Rezgui 2000), or leadership development programmes (McKenzie and Aitken 2012). Examining the organization as a distributed knowledge system and the organizational learning processes through which knowledge has to travel and be converted to impact performance leads to the
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Christine van Winkelen and Jane McKenzie conclusion that evaluating knowledge management initiatives in isolation from other organizational and social practices is meaningless. In summary, although there has been empirical proof that organizational learning capability has a positive relationship with performance, intervening in the organisational knowledge environment is problematic and requires an awareness of complex organizational dynamics. If the firm is viewed as a distributed knowledge system and the learning environment operates through multiple nested interacting complex systems, then the proposition of this paper is that it would be beneficial to rethink how the practices knowledge managers seek to implement are evaluated. At the level of system‐wide organizational learning capability (the whole integrated set of influences that come together in a particular context) impact on performance can be measured. However, no individual KM practice can be viewed meaningfully in isolation so an approach to evaluation that reflects the complexity of the linguistic, socially constructed knowledge context is required. The next section examines current thinking in the field of evaluation to gain insights into how to approach this.
4. Evaluation in complex social contexts “An evaluation is ultimately about reality testing, getting real about what’s going on, what’s being achieved – examining both what’s working and what’s not working” (Patton 2011, p5). As evaluation has developed as a field, there has been increasing recognition of the need to take account of the social context. Evaluation differs from research in that there is an intended user; however in maintaining the separation of evaluator from user to bring objectivity to the process, it was found that the adoption of evaluation findings by users was often disappointing. An overlay of organisational learning theory suggested that users’ involvement in the evaluation process itself could address this (Cousins and Earl 1992). Collaborative, participatory and empowerment evaluation emerged as a related set of themes which were explored through the 1990s. By 2000, Gregory (2000) concluded that the arguments for a participatory approach were sound in terms of both usability and transformational evaluation purposes, but all too often political considerations (for example, who should be involved) were not properly considered, undermining participative intentions and the organisational learning benefits. Today, attention to the political dimensions of participatory evaluation is recognised as necessary. The application of collaborative / participatory evaluation methodologies to different contexts became a focus of study, including mapping them against the simple, complicated, complex and chaotic framework of decision situations outlined by Snowden and Boone (2007). Patton (2011) examined in some depth the specific implications of the complex context, proposing the term “developmental evaluation” for evaluations in such circumstances. Conditions of complexity involve high uncertainty and unpredictability, no right answer, a diversity of approaches, and cause and effect unknowable in advance. Developmental evaluation demands evaluative questions and evaluative thinking about evidence as a team conceptualises, develops and tries out new approaches to an activity. It is particularly suited to the exploration and innovation stages of adaptation and change, often before there is a crystallised programme model to be tested through formal evaluation mechanisms. Developmental evaluation needs participants with the capacity to think evaluatively and to interpret findings critically. It also requires evaluators to be flexible, creative, adaptable, open‐minded and “able to facilitate rigorous evidence‐based reflection to inform action” (Patton 2011, p26). Drawing on organisational learning theory, further consideration about how participatory evaluation could become an organizational learning exercise identified the centrality of the social production of knowledge. Stakeholder groups participating in the evaluation “learn to reflect on their own experiences, mutual interactions and shared information”. (Suarez‐Herrera et al. 2009, p323). Participatory evaluation has the potential to be a basis for double loop learning in which underlying strategies and assumptions are challenged, but this requires a learning environment in which there is a focus on expanding the capacity of those involved “to create, to think and to act openly in the quest to learn together..... The creation of learning environments implies a participatory and politically articulated process of social mobilization” (Suarez‐Herrera et al. 2009, p335). If an overly utilitarian (as opposed to empowerment) rationale for participatory evaluation is adopted, then single loop (refinement and improvement oriented) learning tends to result, which can end up creating a resistance to more substantial change.
5. Developing participatory evaluative capability: a KM focus Shifting focus from the evaluation of individual KM practices by looking for cause and effect relationships, to evaluating for learning through developing participatory evaluative capability within KM programmes offers
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Christine van Winkelen and Jane McKenzie two potential advantages. Firstly, it has the potential to amplify the benefits of those programmes by positively impacting the learning environment. Secondly, it offers a more effective means of understanding what is working, what is not, and what improvements should look like, directly from those involved in using the practices. The discourses promoting participation drawn from a Strategy as Practice perspective (Mantere and Vaara 2008) have been integrated with the factors that support developmental evaluation (Patton 2011) and mapped onto the 4I organizational learning framework (Crossan et al. 1999) in Table 1. This is used to shape principles for developing participatory evaluative capability within KM programmes. Table 1: Relating organizational learning to aspects of Strategy as Practice and participatory evaluation. 4I’s of organizational learning (Crossan et al. 1999) Individual: Intuitive understanding gained from experience. Group: Interpretation of experience.
Organization: Coordinating and integrating knowledge created through these processes to institutionalise it as refinements of current practices and development of new practices.
Discourse that promotes participation (Mantere and Vaara 2008) Self‐actualization Promote ways to help people find meaning in work. Dialogization Stimulate constructive dialogue between groups. Concretization Ensure contributions to learning from KM practices are viewed as being naturally and normally integrated within other decision‐ making processes.
Developmental evaluation capacity (Patton 2011) Critical thinking, openness, asking evaluative questions. Evaluator skills in situation recognition, systems thinking, facilitation, and methodological flexibility.
Widespread participation mobilised with sensitivity to political realities.
The principles that underpin building evaluative capability are founded in the language systems of the organization (the means by which learning happens). They can be summarised as:
Develop the necessary skills and adopt approaches that prompt reflection and questioning about the KM practices, both by individuals and in groups.
Mobilise dialogue between groups experiencing the practices to support wider sensemaking.
Adopt a participatory approach to agreeing what “good” looks like in relation to the KM practices to reflect the specifics of the local social context.
Building organizational capability in line with these principles is already part of many knowledge management programmes through well‐established practices such as after action reviews, peer assists and communities of practice (see for example Collison and Parcell 2004 for an overview of these KM practices) as they stimulate dialogue about what is working and what is not in relation to specific organizational activities, though they risk single loop rather than double loop learning unless the facilitator has the skills to prompt questioning of assumptions and underlying principles about why the activity is being undertaken. Adopting similar practices in relation to knowledge management programmes is a basis for learning‐based evaluating, while reframing these practices (whether applied to business activities or KM practices) as facilitating the development of participatory evaluative capability could prompt new insights about where additional investment could be useful. For example, coaching facilitators to prompt more challenging questioning and deeper reflection, or thinking differently about who to involve. Transparent processes to incorporate the feedback from these activities into future practices are also needed. In line with the intention of the paper of considering application to practice, two additional approaches that could be of value for knowledge managers are briefly outlined in the following sections: mobilising dialogue through action learning approaches to implementing KM; and adopting participatory ways of agreeing what good looks like in relation to specific KM activities in context.
5.1 Mobilising dialogue through action learning Action learning is a group‐level process for mobilising dialogue, improving questioning skills and stimulating reflection through social interaction, whilst simultaneously developing individual capacity. It is “a method to
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Christine van Winkelen and Jane McKenzie generate learning from human interaction occurring as learners engage together in real‐time work problems. Learning arises not just from representations of conceptual material but from questioning among fellow learners as they tackle unfamiliar problems” (Raelin 2006, p152). The principles of learning from others’ practice, using the different perspectives that collective interaction can bring creates an expectation that participants will “demonstrate a learning‐to‐learn aptitude which frees them to question the underlying assumptions of practice” (Raelin 2006, p153). Groups (or sets) of typically five to seven people individually work on projects (either their own or one in which they are jointly involved) and come together in intermittent meetings, supported by a facilitator, to discuss the challenges they are facing and respond to questions from others in the group. Personal reflection and group questioning are fundamental elements of the process. The impact comes from the ways in which “transformations of individual thought and action influence changes in organizational practice” (Vince 2012). It has been argued that “organizational reflection implies “reflective learning” supported by organizational routines, practices and cultures; in other words, under conditions that prepare and enable professionals to sense uncertain situations and act upon them by way of inquiry” (Jordan et al. 2009, p467). To lead to double loop learning, this requires a culture that allows criticism to be voiced and the collective capacity to question assumptions. Jordan et al. (2009) noted a tendency towards structural practices that induce reflection–on– action and explored how the literature on mindfulness could help shift the focus to encompass reflection–in– action with the intention of further developing a reflective culture. “Mindfulness is a state of mind or mode of practice that permits questioning of expectations, knowledge and the adequacy of routines in complex and not fully predictable social, technological and physical settings” (Jordan et al. 2009, p468). Although grounded in individual behaviour, it can be built into organizational mechanisms (for example action learning) through mutual checking and questioning practices that aim to discover something unexpected in the situation. Facilitating action learning sets associated with the adoption of KM practices could develop evaluative capability most effectively if efforts are made to prompt double loop learning and to broaden participation to bring in diverse views.
5.2 Adopting a participatory approach to agreeing what good looks like Rather than a top‐down knowledge‐strategy discourse defining the standard and contribution of KM practices, which acts as a barrier to participation (Mantere and Vaara 2008) and therefore limits the development of evaluative capability, a participatory approach recognises polyphony and uses dialogue to allow an agreed direction to emerge in context. Maturity models adopt a growth perspective to describe what progressive levels of performance look like in terms of key factors (Filstad and Gottschalk 2010). When they are tailored to a context by those involved in a specific activity, they are a way of negotiating an agreed vision of what good looks like (Collison and Parcell 2004). Similarly, Stead (2004) describes a workshop‐based approach for establishing critical success factors (relating to individual and organizational learning) participatively with those commencing an action learning programme designed to integrate learning from a development activity into their practice. This was used to stimulate critical reflection on learning at regular intervals within the group and also as the basis of final reporting. Another potentially useful approach to developing a shared view of desirable outcomes is Appreciative Inquiry (AI). AI is an alternative to a “problem solving” approach to organisational development (Cooperrider and Srivastva 1987). It uses existing strengths to seek new opportunities, arguing that the language of problems reinforces unhelpful mental models, making it more difficult to achieve a momentum for change. In a complex situation, identifying the characteristics of success in advance needs to be treated with caution as cause and effect relationships are inherently unknowable. However, as a vehicle to create a shared language and as the basis for creating shared systems of meaning in relation to productive activities, these approaches have value. To be consistent with the principles explored in this paper, they need to be subject to regular feedback and renewal. Deviation from initially agreed visions of success should be interpreted as a potential signal of the need to revisit underlying assumptions, rather than as inherently problematic.
6. Power and politics Strategy as Practice and developmental evaluation both emphasise widespread participation because the ripples and waves of impact of any activity in an organizational system reach far beyond those immediately involved. This suggests that the evaluative capacity of many individuals needs development to establish
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Christine van Winkelen and Jane McKenzie effective organisational evaluative capability. However, while a diversity of perspectives enriches decision‐ making in complex social contexts, it also creates the potential for disagreement that results in inertia. Social and political relationships can result in the emergence of a “knowing‐doing” gap ((Pfeffer and Sutton 2006): the gap between knowing something is important and actually doing something about it. Organizational energy can be dissipated when divergent interests outweigh convergence over a prolonged period of time. Direct intervention from senior managers narrows the perspective of the “collective mind” and its ability to comprehend complex environmental factors (Weick and Roberts 1993). Vince (2012) has reflected on how power relations generate contradictions in how action learning methods are experienced and called for engagement with these contradictions to improve its impact and effectiveness. Similarly, Lawrence et al (2005) explored the power dynamics associated with each process of the 4I organizational learning framework, arguing that these can act as the “social energy” that fuels the processes. Constructively engaging with the contradictions, tensions and dilemmas of a knowledge‐based perspective on the organization (McKenzie and van Winkelen 2004) will require knowledge managers to develop a particular dialectical approach to knowledge leadership (McKenzie and Aitken 2012).
7. Conclusion The ripples and waves metaphor has been used to illustrate the interdependency of organizational influences on learning and the difficulty this causes in establishing the impact of specific KM practices. Reframing the evaluation of knowledge management initiatives as developing organizational evaluative capability offers the possibility of both amplifying the impact of the KM practices through improving the learning environment in which they are enacted, and generating more meaningful insights into what is working and what needs to be changed in relation to those practices in specific contexts. Knowledge managers may require new skills to develop the capacity of individuals and groups to ask evaluative questions and critically reflect on experience of using KM practices, to mobilise dialogue, for example by facilitating action learning groups implementing new the practices, and to shape and deliver effective participatory approaches to surfacing what good KM practice looks like in context. The translation of the learning from these evaluative activities into future KM initiatives requires in‐built feedback loops, but one of the main challenges is to avoid an overly process‐ oriented approach that could bias incremental refinement. Although developmental evaluation theory argues that an evaluative capability approach is most relevant in the early innovative and implementation stages of change initiatives, viewing the organization as a complex distributed knowledge environment points to the relevance of these principles over the longer term too. At the centre of the approaches that build organizational evaluative capability is the view of the organization as a discursive community. Developing understanding through questioning allows individuals to learn through making increasingly fine linguistic distinctions; mobilising dialogue, for example through action learning, builds connections across systems of meaning making; and developing the discourse about what good looks like develops the shared language necessary to coordinate and integrate knowledge in the organization and recognise the need to change. Embedding the combination of these stages into KM programmes may go some way to overcoming political barriers to organizational learning. Combining judgement with experience, and ensuring differences of opinion are valued and heard are essential if assumptions are to be challenged and double loop learning ensures that KM practice adapt to changing requirements. As Weick said: “The power of conversation, dialogue, and respectful interaction to reshape ongoing change has often been overlooked. We are in thrall to the story of dramatic interventions in which heroic figures turn around stubbornly inertial structures held in place by rigid people who are slow learners. This is a riveting story. It is also a deceptive story. It runs roughshod over capabilities already in place, over the basics of change, and over changes that are already underway” (2009, p238). The principles and approaches outlined in this paper offer knowledge managers a different way of developing activities and engaging stakeholders in understanding the strategic contribution of their work. Further research to assess their value and application in practice will necessarily play close attention to context, suggesting case studies using qualitative methods will be most appropriate. The scope of this paper was limited to post‐bureaucratic organizations and the application to more formally structured and hierarchical organizations in which questioning and dialogue are not culturally accepted needs further consideration.
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Profiling the Intellectual Capital of Italian Manufacturing SMEs: An Empirical Analysis Chiara Verbano and Maria Crema Dept. Management and Engineering, University of Padova, Str.lla S. Nicola 3, Italy chiara.verbano@unipd.it crema@gest.unipd.it Abstract: Since the early 1990s research stream of Intellectual Capital (IC) has been grown, and in the new, knowledge‐ intensive economies, it has become a source of competitive advantage. Scholars have tried to defined and categorized it, but still literature is not concordant and different methods of IC measurement have been developed. Some authors have emphasized the benefits of qualitative and perceptive measures that catch what the traditional systems have failed to report; furthermore, they are able to describe IC in a prospective approach. Nevertheless, until now, little research has been devoted to analysing IC components and their interconnections and impact (directly or in conjunction) on firm performance. In particular, IC in small and medium‐sized enterprises (SMEs) remains understudied. This paper intends to provide a theoretical framework to synthesise the fragmented previous works in order to identify the appropriate IC measures and apply them to SMEs. In more detail, the objectives are: 1) to measure the level of IC and its components in Italian manufacturing SMEs, 2) to identify clusters of companies with different levels of IC and characterise them in reference to IC components and firm‐specific factors and 3) to analysed the impact of IC on firm performance. To this end, a survey has been conducted in the Italian manufacturing context and a database of 107 responses was obtained. After a descriptive analysis revealing the level of IC components, three well‐distinguished groups emerged from cluster analysis and a positive relationship was identified between IC and firm performance using multiple linear regression models. This study contributes to measuring IC, understanding its structure and level in SMEs and its effect on firm success. Managers of SMEs can draw guidelines for IC evaluation to develop internal auditing system, to communicate adequately IC value to any external stakeholders and to support decisions in managing their IC, thereby maximizing firm performance. Keywords: intellectual capital, SMEs, knowledge management, firm performance, survey
1. Introduction In the current information‐ and knowledge‐based era, intellectual capital (IC) has become a source of competitive advantage that can determine the success or failure of a firm, significantly affecting financial and innovative performance. IC is recognised as a fundamental part of the process of firm value generation, but as traditional accounting methods are insufficient in terms of reporting the growing value of these intangible assets, other complementary tools are necessary (Bukh, 2003; Beattie & Thomson, 2007). This type of resource is particularly relevant and strategic for small and medium‐sized enterprises (SMEs), although they do not usually measure or recognise it (Ngah & Ibrahim, 2009; Steenkamp & Kashyap, 2010). Most of the literature on SMEs highlights that they do not have huge amounts of tangible and financial resources, so their challenge is to demonstrate and exploit a higher value of intangible assets, especially regarding human capital, to compete successfully (Erikson, 2002). Since SMEs have different characteristics from large companies – which are the ones usually analysed – empirical analysis is necessary to address this issue (Hadjimanolis, 2000; Cohen & Kaimenakis, 2007). The strengths of SMEs are as follows: their motivation; their networks; tacit knowledge of human resources which is difficult to imitate; communication channels that are short and informal; less bureaucracy; and market proximity (Nooteboom, 1993; Camuffo & Comacchio, 2005). In SMEs, the organisational structure and the managerial systems are usually informal and some employees play a key role, so individuals are the main – and often the only – source of organisational knowledge. In this context, the development of an integrated social system leads improvement in efficiency and facilitates coordination and knowledge exchange (Camuffo & Comacchio, 2005). A system of measurement provides SMEs with a tool that can create a friendly atmosphere; this can foster the growth of employee collaboration (Ngah & Ibrahim 2008). The firm culture and climate not only create reciprocal support among employees, but also encourage creativity; companies that promote the building, sharing and use of knowledge are firms with a flat organisational structure where the responsibilities are shared at all levels (Montequin et al. 2006).
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Chiara Verbano and Maria Crema Guidelines for SMEs in terms of measuring and understanding how IC is constituted and linked to a firm’s context and performance have not been properly provided. Therefore, the intention of this research is to investigate IC in SMEs in the manufacturing sector, including its impact on firm performance and the influence of contextual factors. The remainder of the paper is organised as follows. The second section is devoted to a literature review to identify gaps in the research and create a theoretical framework. In the third section, the research objectives and methodologies are described, while the fourth section reports the results of the analyses conducted. In the final section, the discussion and conclusion are developed.
2. Literature review The first studies on IC date back to the early 1990s; since this time, there has been an increasing amount of research on this subject (Marr & Adams, 2004). Different definitions of IC have been formulated in the literature. One of the most recent definitions considers IC as a knowledge asset regarding the creation and connection of experience, capacities and competence inside and outside the company; this, in turn, can be converted into value (Cabrita & Bontis, 2008). Zerenler et al. (2009) provided a similar definition, including all intangible assets, knowledge, capacities and relations at the level of both the employees and the organisation, categorising IC into human, structural and relational capital. Hsu and Wang (2012, p.181) focused on a knowledge perspective, defining IC as ‘the stored knowledge possessed by an organization, which is tacit knowledge, personal knowledge possessed by employees and available to network relationships through interaction the knowledge of a company’. Kaufmann and Schneider (2004) noted a lack of theoretical fundamentals in IC literature, and where theory exists, the lack of linkage to practice. Moreover, according to Diefenbach (2006), there is no rigorous research identifying all types of intangible assets in a systematic way, although a widely recognised classification could be useful to define IC (Gröjer, 2001; Choong, 2008). For these reasons, in this research, the definitions of IC provided in literature have been mapped and compared, the main IC components have been recognised and a theoretical framework has been developed for IC measurement. Following Sveiby’s (2010) categorisation, different kinds of measurement systems have been used, but some authors have emphasised the benefits of qualitative and perceptive measures that catch what the traditional systems have failed to report; furthermore, they are able to describe IC using a prospective approach (Kannan & Albur, 2004; Brennan & Connell, 2000; Ng 2006; Sonnier et al. 2009). The theoretical framework shown in tab. 1 has been developed through considering definitions of IC and the more widely used measures of IC components which have adopted a semi‐qualitative approach, even if some categorisations are controversial (i.e. motivations, training, internal social capital). Moreover, in defining the theoretical framework, the main futures of SMEs have been considered in order to adapt the general IC literature to this research purpose. Accordingly, the brand and research and development (R&D) items used by some authors have not been included, as in SMEs, brand, image and reputation are not usually well exploited (Raymond et al., 2001; Berthon et al, 2008), innovation activities do not tend to be formalised and no resources are institutionally dedicated to R&D (Santarelli & Sterlacchini, 1990). Most authors distinguish three categories of IC: human capital (HC), internal structural capital (ISC) and relational capital (RC). These are defined as follows:
Human capital (HC): According to Marr (2005), whose perspective is based on human resources, IC refers to the capabilities, knowledge, education and attitudes of employees; other aspects included by other authors are creativity, experience, training, talent and skills (Bontis, 2001; Seetharaman et al., 2004; Kaplan & Norton, 2004; Youndt et al., 2004). According to Roos et al. (1997), skills and education fall under the umbrella of competence. In our framework, considering that the concept of employees’ innovative attitude comprises versatility of the knowledge and creativity (Schilling, 2010; Tidd & Bessant, 2009), three variables are identified for HC, specifically competence, creativity and versatility (Table 1).
Internal structural capital (ISC): Stewart (1997), Bontis (2001), Zerenler et al. (2009), Hsu and Wang (2012) defined IC as knowledge stored inside the company that can be used by employees. Combining the studies
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Chiara Verbano and Maria Crema of different authors, ISC includes the following: intangible property rights, organisational capital (culture, type of leadership, ability to share knowledge, teamwork), institutionalised knowledge and codified experience stored in databases and manuals, procedures and processes (Kaplan & Norton, 2004; Subramaniam & Youndt, 2005; Youndt et al., 2004). Synthesising these studies, the variables we will consider for ISC are patents, internal social capital, employee incentives, organisational structure and knowledge coding (see Table 1).
Relational capital (RC): Most studies have focused on customers and markets to define RC, but Ngah and Ibrahim (2009) also included relations with all other stakeholders, as they are fundamental for companies (Guthrie et al., 2006). For SMEs specifically, relations with different partners could be essential in overcoming their lack of resources (Van de Vrande et al., 2009, Verbano et al., forthcoming). Other aspects considered in RC are reputation, the value of brand, the firm’s image (Przysuski et al., 2004; Brennan & Connell, 2000; Guthrie et al., 2006) and finally collaborative routines supporting the open innovation approach (Petroni et al., 2012). At any rate, using a semi‐qualitative approach, the firm image is not usually considered, and therefore this factor has been excluded from the theoretical framework of this research. Thus, the variables identified for RC are relations and collaborative routine (Table 1).
Table 1: IC resulting from literature review – theoretical framework SUB‐COMPONENTS REFERENCE FOR SEMI‐QUALITATIVE MEASURES HC COMPETENCE & CAPABILITIY Youndt et al. (2004); Huang & Wu (2010); Hsu & Sabherwal (2011); Subramaniam & Youndt (2005); Hsu & Sabherwal (2012); Reed et al. (2006) Ahmed & Hussainey (2010); Sharabati et al. (2010); Bontis et al. (2000); Cabrita & Bontis (2008); Suraj & Bontis (2012); Bozbura (2004) CREATIVITY Sharabati et al. (2010); Bontis et al. (2000); Cabrita & Bontis (2008); Suraj & Bontis (2012); Reed et al. (2006); Youndt et al. (2004); Huang & Wu (2010); Hsu & Sabherwal (2011); Subramaniam & Youndt (2005); Hsu & Sabherwal (2012); Ahmed & Hussainey (2010); Bozbura (2004) VERSATILITY Youndt et al. (2004); Huang & Wu (2010); Hsu & Sabherwal (2011); Subramaniam & Youndt (2005); Hsu & Sabherwal (2012); Reed et al. (2006); Liu et al, (2010) ISC PATENT Sharabati et al. (2010); Bontis et al. (2000); Cabrita & Bontis (2008); Suraj & Bontis (2012) INTERNAL Youndt et al. (2004); Huang & Wu (2010); Hsu & Sabherwal (2011); Subramaniam SOCIAL CAPITAL & Youndt (2005); Hsu & Sabherwal (2012); Ahmed & Hussainey (2010); Bozbura (2004); Reed et al. (2006); Wu et al. (2007); Montequin et al. (2006); Sharabati et al. (2010); Bontis et al. (2000); Cabrita e Bontis (2008); Suraj & Bontis (2012) EMPLOYEE DEVELOPMENT Bozbura (2004); Ahmed & Hussainey (2010); Sharabati et al. (2010); Bontis et al. SYSTEM (2000); Cabrita & Bontis (2008); Suraj & Bontis (2012) ORGANIZ. STRUCTURE Sharabati et al. (2010); Bontis et al. (2000); Cabrita & Bontis (2008); Suraj & Bontis (2012) KNOWLEDGE CODING Youndt et al. (2004); Huang & Wu (2010); Hsu & Sabherwal (2011); Subramaniam & Youndt (2005); Hsu & Sabherwal (2012); Reed et al. (2006); Wu et al. (2007) RC RELATIONS Sharabati et al. (2010); Bontis et al. (2000); Cabrita & Bontis (2008); Suraj & Bontis (2012); Liu et al. (2010) COLLABORATIVE ROUTINES Sharabati et al. (2010); Bontis et al. (2000); Cabrita & Bontis (2008); Suraj & Bontis (2012)
As mentioned in the introduction, there has been little investigation of IC in SMEs, as most of the studies follow a descriptive approach focusing only on specific IC components and the context of analysis (Piperopoulos, 2010; Cohen & Kaimenakis, 2007), and in some cases adopting quantitative measures (Durst, 2008; Tseng & Goo, 2005). Moreover, few studies have described the characteristics of IC components, their interconnections or their impact on firm performance (Steenkamp & Kashyap, 2010; Durst, 2008; Costa, 2012; St‐Pierre & Audet, 2011). A positive relationship between IC and business performance has been verified by Bontis et al.(2000), Moon and Kym (2006), Bontis and Serenko (2009), Cabrita and Vaz (2006), Chareonsuk and Chansa‐Ngavej, (2010) and Sharabati et al. (2010) using perceptive semi‐qualitative data. They all considered the main components of IC instead of each variable within the IC components. Other authors tested only the impact of single specific components of IC on firm performance (Rose & Kumar, 2006; Zack et al., 2009), while still others (Alipour, 2012; Firer & Williams, 2003 Hsu & Wang, 2012) used quantitative financial and accounting data to study this relationship. Crema and Verbano (2013) investigated the relation between IC and innovation performance in
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Chiara Verbano and Maria Crema manufacturing SMEs. Furthermore, most research has been limited to single industrial sectors (e.g. Ng, 2006; Huang & Wu, 2010; Sharabati et al., 2010; Chen, 2004; Bollen et al., 2005; Seleim et al., 2004) or service sectors (Cabrita & Bontis, 2008; Bontis & Fitz‐enz, 2002). Finally, considering papers with a qualitative IC measure, little research has included the influence of contextual variables. In particular, Sonnier et al.’s (2009) study considers how size and age affect IC disclosure; however, this research focuses on high tech companies and does not follow a semi‐qualitative approach. To sum up, the gaps which have become apparent from the literature review are as follows:
There is a need to map and synthesise different measures of IC;
The profile of IC in SMEs is not adequately described in the literature, especially in terms of the manufacturing sector;
There has been little research focus on the impact of the variables of the three IC components on performance;
The context variables are usually not considered in IC research.
3. Objectives and methodology To fill in the identified gaps, the main purpose of this research is to focus on SMEs in an Italian context in order to profile their IC level and structure, analysing the impact of IC on firm performance and the influence of context variables on this relation. The research questions (RQs) have been formulated as follows:
What are the levels of IC and its components in Italian manufacturing SMEs?
Can clusters of companies with different levels of IC be identified and characterised in reference to IC components and firm‐specific factors?
What is the impact of IC on firm performance?
The research framework, generated from the theoretical framework in section 2, is reported in Fig. 1. Data collection was carried out by randomly extracting 2,250 manufacturing firms from the AIDA Bureau van Dijk database in the Italian context, as the highest proportion of European SMEs are located in Italy (Ecorys, 2012). An online survey was conducted, inviting the director of R&D or the CEO/entrepreneur to complete a web questionnaire; 107 responses were obtained (4.75% response rate).
Figure 1: Research framework The firms in the sample are mostly manufacturers of machinery and equipment (27.2%) and producers of fabricated metal products (other than machinery and equipment) (17.4%); they are mainly located in the north of Italy (62%). Run‐tests for the context variables confirm the randomness of the sample and the chi‐square test verifies representativeness using the distribution of technological intensity and firm size in the reference population
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Chiara Verbano and Maria Crema (sig. < 0.01). The theoretical framework was operationalised to create and validate the constructs of each IC component through a factor and reliability analysis (Cronbach’s alpha). For the same reason, another factor analysis was conducted to measure firm performance. The measures of the constructs created and the context variables are reported in Table 2. Table 2: Measures of IC, firm performance and contextual variables
Constructs were created based on the literature and following the following criteria for the factor analysis: KMO > 0.6, cumulative explained variance > 60%, factor loadings more than 0.5, communalities >0.4 and Cronbach’s alpha of each factor > 0.6. In order to maintain the independence of the factors within each single component of IC, varimax rotation was adopted. The three components of IC – which are further considered in the cluster analysis – were generated with all the related constructs added for each of them. Thanks to a descriptive analysis of the obtained constructs, the main characteristics of IC have emerged, answering the first RQ. The second RQ has been solved using cluster analysis and hypothesis testing. To accomplish this, a lot of different clustering methods have been tested and the k‐means option has been selected and a three‐cluster solution was generated. Thanks to the results of the Kolmogorov‐Smirnov test, the hypothesis of normal distribution for the variables used was not rejected. For RC, which had homogeneity of variance, analysis of variance (ANOVA) resulted in high significance, while for ISC and HC (which did not have homogeneity of variance), the Welch test has confirmed their influence in differentiating the clusters. For these three components, post‐hoc testing validated the claim that the clusters are well differentiated. As the normal distribution for contextual variables was not accepted, non‐parametric Kruskal‐Wallis tests (verified by Median tests) were applied to verify whether they discriminate the clusters; the ANOVA and post‐hoc tests were applied to evaluate differences in firm performance. In order to avoid potential problems with abnormal distribution, the non‐parametric Kruskal‐Wallis test was also executed for the constructs of each IC component. Finally, to understand how IC affects firm performance (RQ3), a linear multiple regression model
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Chiara Verbano and Maria Crema has been employed, as the assumptions for linear models were satisfied (linear relations between independent and dependent variables, variables measured without errors, normal distribution and independence of errors, constant variance of the probability distribution of errors, no multicollinearity problems [VIF<2]). Only the variables with a p‐value less than 0.10 have been inserted into the model, excluding the variable with highest p‐value step by step. All analyses have been performed using SPSS Statistics 17.00.
4. Results From the results, the answer for each RQ is presented below.
4.1 IC composition in SMEs To solve the first RQ, a descriptive analysis was conducted which considered the average value of the mean of each item for each construct, using the original 7‐point Likert scale (Fig. 2).
In the scheme between brackets can be read mean and standard deviation
Figure 2: Descriptive analysis for IC In Fig. 2, the most developed IC components are highlighted in a darker colour. The thresholds are as follows: values less than 3.5 are considered low, means between 3.5 and 4.5 represent the medium level and values greater than 4.5 are judge high. Among the three IC components, SMEs seem to excel in HC, as high levels are reported for all of its variables. Companies in the sample also rely moderately on collaborations with external partners and adopting collaborative routines. On the other hand, the ISC results were moderately developed and inhomogeneous: Low values were found for organisational structure and patents, high values for internal social capital and medium values for the other variables.
4.2 IC profiling Following the criteria of cluster analysis described in section 3, three clusters emerged (CL1, CL2, CL3) which were well differentiated in all IC components (Table 3). Table 3: Results of cluster analysis
In addition, post‐hoc hypothesis tests confirmed that the clusters were well distinguished from each other. Cluster 1 presented the highest level of all IC components, while cluster 3 exhibited the lowest, especially for
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Chiara Verbano and Maria Crema HC and ISC; cluster 2 had medium IC (overall IC mean: CL1=0.73161; CL2=‐0.3619; CL3=‐2.6647). In order to better describe the three clusters, the means of each IC component were translated into the original Likert scale, following the same criteria using for descriptive analysis (Fig. 3).
6 5,5 5 4,5 4 3,5 3 2,5 2 1,5 1
CLUSTER1 CLUSTER2 CLUSTER3
Figure 3: Description of IC profiles In the same figure, firm performance has also been added, since this variable differentiated the clusters in ANOVA (sig<0.001); the Bonferroni, Tukey HSD and Scheffe tests confirmed the significance of this differentiating variable (even if at a lower level in discriminating CL1 and CL2: sig<0.1). The Kruskal‐Wallis test demonstrated that the clusters were also well distinguished considering each single variable of the IC components, with the exclusion of organisational structure; therefore, a bar chart describing IC in more detail for each cluster is provided in Fig. 3. From the histogram on the left side, it can be observed that the higher the IC level, the greater firm performance becomes. Specifically, performance is high for CL1, whose RC and HC are well developed, while ISC is at a medium level (low for organisational structure and high for internal social capital and employee development systems). Firm performance is medium for CL2, in which all HC variables are high, while ISC is medium (only internal social capital is high), and the relationships with external partners are poorly adopted. Finally, CL3 shows low performance, not well developed HC and ISC, while relations and collaborative routines are adopted at the medium level.
4.3 Impact of IC on firm performance The linear regression models obtained are described in Table 4. In the first model, only the main effects and the interaction variables were used. The latter were created by multiplying pairs of variables within each IC component. The model improved after the inclusion of the contextual variables, of which only the technology intensity was statistically significant; thus, the others were excluded. For both of the models, ANOVA exhibited high significance (sig <0.001). The variables which showed significant results in explaining firm performance were as follows: collaborative routine, relations, employee development systems, creativity, “versatility x competence”, “relations x collaborative routines”, “internal social capital x organizational structure”. Considering RC, the main effects of the relations and collaborative routines are included in the model, as well as the effect of their interaction variable on firm performance. The only variable of HC inserted into the model as a main effect is the creativity but, interacting together, also versatility and competence influence firm performance. ISC influences firm performance through the main effect of employee development systems and the conjunct effects of internal social capital and organisational structure.
5. Discussion and conclusion The purpose of this research was to measure the level of IC and its components in Italian manufacturing SMEs, to identify and characterise clusters of companies with different levels of IC components and firm‐specific factors and to analyse the impact of IC on firm performance. As a general view, the results suggest that manufacturing SMEs have well‐developed HC; moderately developed RC, in accordance with previous
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Chiara Verbano and Maria Crema empirical analysis of open innovation in small companies; and heterogeneous results for ISC, where high values are shown for internal social capital, but low values for patent and organisational structure. Table 4: Results of multiple linear regression model
Three well‐distinguished groups of companies emerged from the cluster analysis, with performance level increasing with the developments of IC components. The linear regression analysis confirms the relation between IC and performance, highlighting the variables of IC components which are significant in explaining firm performance, specifically collaborative routine, relations, employee development systems, creativity, “versatility x competence”, “relations x collaborative routines”, “internal social capital x organizational structure”. Therefore can be concluded that RC, employee development systems and creativity impact directly on firm performance, while the development of the internal competence impacts indirectly interacting with versatility and two variables of ISC (internal social capital and organizational structure) influence firm performance only conjunctly. From a theoretical point of view, these results provide a contribution in the debated literature on IC measurement (especially in SMEs), presenting an integrated framework which follows a semi‐quantitative measurement approach. This framework, obtained from an in‐depth literature review, was successfully tested with the empirical analysis in manufacturing SMEs. Furthermore, this research contributes to the understanding of the structure and extent of IC in manufacturing SMEs, as well as its effect on firm success issues which were previously understudied. From a practical point of view, managers of SMEs can draw guidelines for IC evaluation from this research in order to develop an internal auditing system, support decision making in managing their IC, maximising firm performance and communicating the value of IC to any external stakeholders. In particular they can identify their firm in one of the groups emerged from this analysis and therefore understand on which IC components it is necessary to firstly invest, in order to improve performance. If they are looking for immediate impact on firm performance they should implement or improve firm employee development system and stimulate the creativity of the employees; moreover, they should use collaborative routine to support the relations with external environment, in order to build successful partnerships.
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Chiara Verbano and Maria Crema The framework developed in this research could be further tested, extending the sample of SMEs to other countries and industrial sectors beyond manufacturing. In this way, the field of application could be extended to service companies. We believe, however, that this paper represents a valuable basis for future research and managerial discussions in the field.
Acknowledgements The authors gratefully acknowledge financial support for this research from Padua University (Research project CPDA109359) and from “Fondazione Studi Universitari di Vicenza”.
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The Obligatory Passage Point: Abstracting the Meaning in Tacit Knowledge John Walton Sheffield Hallam University, Sheffield, UK J.R.Walton@SHU.ac.uk Abstract: The lived experience of individuals in the workplace results in an accumulation of dispositions to act. Such dispositions have been termed knowledge [Boisot 1998]. Further, this knowledge is considered to be tacit or explicit [Baumard 1999]. Therefore tacit knowledge is one of the precursors of new knowledge. There have been a number of hypotheses as to how such knowledge is transformed into explicit knowledge [Nonaka et al 2000], and then subsequently diffused [Boisot 1998]. Moreover it is impossible to know the magnitude of tacit knowledge that is not articulated, however insightful, original or crucial it may be. The transformation to the explicit rendition can and will act as a filter in an attempt to eliminate meaningless utterances. Therefore some tacit knowledge will be lost in this process. This is an Obligatory Passage Point (OPP) a concept normally associated with Actor Network Theory [Latour 2005]. This is where the decision is made as to ‘what counts’ as legitimate knowledge and it is irreversible. This obligatory passage point is for all tacit knowledge in a community, an organization or even a nation. The case study presented comprises a small number of individuals working in a third sector environment. Although there is agreement as to what is to be achieved, the how question remains open. Despite the common concrete experiences, the tacit appreciation of the perceived action world varies significantly. A process by which an inventory of tacit knowledge can be established, abstracted and combined to act as a base to affect dispositions and expectations is described. The path to the subsequent generation of actionable knowledge is plotted which can subsequently form the basis for an intervention. The delineation between tacit knowledge and explicit knowledge in this context is explored by the application of the obligatory passage point. Utilizing the principles of language by Karl Buller the notion of legitimation is discussed. The OPP is significant because when tacit knowledge is shared, there is a process of gaining inter‐subjective agreement which legitimizes the explicit representation of the tacit knowledge. The eye can see and interpret the world, but it cannot see itself. All tacit knowledge is gained through the mind’s eye. The collective minds are seeing the collective tacit knowledge of the group and agreeing. Keywords: actor network theory; tacit knowledge
1. Introduction Organizations are effective as a result of collective action. The annual appraisal is a process that evaluates an employee’s contribution against targets set previously; it is perceived as an objective way of assessing a subordinate by a superior. Fundamentally, the appraisal system is reductionist; central to this process are the assumptions of the design and planning schools of strategy [Mintzberg 1998]. It is appropriate where the environment and the operations of the firm are very stable. The subject of this paper proposes an alternative, that of a review of the previous year, with an assessment by the employee of the success or otherwise of the work undertaken. Comparing the two approaches, appraisals are mechanistic and objective, with asymmetric power relationship between the appraiser and the appraisee. Whilst appraisals rely on explicit knowledge, targets, budgets and the like, reviews place much more emphasis on tacit knowledge [Baumard 1999]. The appraisal process is synoptic, the review is potentially panoptic. With an appraisal system there is a unitary view, and the outcome must lie on a predetermined path. With a review, the organizational is with insight, providing its own rationalization. This paper investigates the reification of tacit to explicit knowledge during the annual review process described as a case study. The literature is explored to provide the focus of the methodological issues involved in the research. Following this the results from the field work are presented. Finally, the relevance and the diffusion of the outcome are discussed, together with a suggestion of the location of the obligatory passage point. This paper takes a scientific perspective of theoretical knowledge and seeks to link the theoretical aspects of the SECI spiral [Nonaka et al 2000] and the Generative Dance [Cook and Brown 1999] with the work of Karl Popper's worlds in the generation of actionable knowledge [Popper 1972]. This may allow a bridge to be built between second and third generation knowledge management [Snowden 2007].
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John Walton Utilizing the approach here, we dare to know, to consider and to act on an exposition of those directly involved in the realization of a superordinate goal by means of actionable knowledge.
2. The case study Access Space, a not for profit organization, was founded in 2000 by the current CEO, James Wallbank. The original purpose was to provide digital opportunities at all levels as well as developing a new empowering relationship with technology. One of the super ordinate goals is to embrace the development of the individual. Participants are encouraged to build a functioning computer using reconfigured scrap hardware and freely available software. This means that the cognitive powers of the individual in the right environment can build a machine of equivalent functionality to one marketed by the major companies yet at a fraction of the price. The cost is the experience and time of the staff and the commitment of the participant. In engaging in purposeful action interpersonal skills are developed, together with opportunities for creativity and innovation. All these lead to enhanced self‐ esteem and the acquisition of intellectual capital [Walton 2010]. The core competence of Access Space was the skill in using disparate parts to make a functioning whole. The Board of Trustees was conscious that appraisals had not been undertaken for the staff including the CEO. This was in part due to an assumption that, because of the close proximity of the staff both with each other and the CEO that this would be unnecessary. However, it was decided to implement not an appraisal, but review for each member of staff. The intention was not to measure contribution against pre‐determined targets (appraisal) but invite each member of staff to look back at the year and talk about their lifeworld. From the outset Access Space has received funding from the United Kingdom’s Arts Council. When this funding was withdrawn in 2011 the effect on the Access Space was profound. Therefore, this was a time of questioning, not for answers, but a search for what the right sort of questions might look like. The point had been reached where a thorough review of the way things were done, who did them and why things were done in the first place was required.
3. Literature review Data is the physical state of a system, [Boisot 1998]. Information is the difference that makes a difference [Bateson 1972]. That is, it allows individuals to understand what it is they perceive. Information is an alteration of expectations, therefore the more our expectations are altered the more informative the information is said to be. Being informed however is not the same thing as action. The information is taken by the individual, and as a consequence of this the cognitive process can give rise to actions. Accordingly, knowledge is a disposition to act [Boisot 1998]. Knowledge therefore arises out of data, but is significant from it. To qualify as being legitimate, knowledge must satisfy three criteria: it must be true; the knower must be able to justify its existence by reasoning from other knowledge and the knower must believe it. This triumvirate criteria of knowledge being justified true belief, as stated by Plato holds true today just as it did three millennia ago [Ayer 1956]. Knowledge has been classified variously as objective versus subjective, tacit as opposed to explicit. Tacit knowledge [Baumard 1999] is difficult to formalize in coherent language therefore it can be difficult if not impossible to imitate. Explicit knowledge [Nonaka et al 2000] can be written down and represented by narrative, tables and formulae. The delineation between tacit and explicit knowledge, and the transition from one to another, gave rise to the SECI framework [Nonaka et al 2000]. This formed the basis of second generation knowledge management. The notion of the three worlds of knowledge [Popper 1972] is depicted in figure one. World one, according to Popper is the locus of action and phenomena. World two is the interaction of the individual with the world one mediated by the perception of the senses so is the site of concrete experiences. World three is the locus of abstract concepts so it is in this world that critical discussion and debate take place. This is the world of scientific theories.
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John Walton The crucial point is worlds one and three are not in direct communication; world two is the mediator between them. This representation is not new however, it was known to Plato and the Stoics. The significance is that it makes the distinction between the object in focus and the logical representation of it. The perception of the object resides in world two and the latter in world three. In line with this, the conception of tacit knowledge is generated by the interaction of world two with world one. There could be an abstract conceptualization which will then reside in world three. This would occur if the perception of the lived experience (the tacit knowledge) is made explicit and there is inter subjective agreement of its logical conceptualization placing it in world three.
Figure 1: The three worlds of Karl Popper Following this line of argument, tacit knowledge will be initiated at position α whilst agreed explicit knowledge will reside a position β. Therefore there are two levels of capture and two filters. First there is that impression of the action world on the individual. This is the concrete experience (first capture). Secondly, there is a conceptualization of this to form an abstract concept. This is the first filter and the second capture as the concrete is now represented in world three as an abstract concept. Finally, abstract concepts, because they can be expressed in fully formed language, can be subject to refutation furnishing the second filter. The remaining content of world 3 (which will often but not always shared) contains yet to be refuted abstract knowledge This is why theories are abstract and simple and experiences are always event concrete and rich [Popper 1972]. This has an impact on the way tacit and explicit knowledge are viewed: explicit knowledge must reside in world 3, whilst tacit knowledge must reside in world one. This, Popper explains is why knowledge in world three is independent of the knower. There is another bifurcation of knowledge proposed by Cook and Brown (1999) where they draw the distinction between knowledge in possession and knowledge in action. This has a significant impact on the work proposed by Popper. What is required then, is a methodology that will allow in situ capture of the deployment of knowledge (knowledge in action) which will then enable the construction of conceptual models (knowledge in possession). Grounded theory methodology (GTM) is an attempt to collect and interpret data in situ [Glasier & Corbin 1990]. GTM enables a complex situation to become analytically tractable. By using the constant comparative method, items of data are compared and contrasted enabling categories (free nodes) of data germane to the situation to emerge. These categories or nodes will in turn permit the relationship between the nodes to be articulated allowing the generation of a conceptual model that will bring greater understanding of the phenomena under consideration. When no more categories or attributes can be added to the model, it is said to be 'saturated' or complete. Data in GTM is not merely narrative data. Pictures, diagrams, body language and even the elocutionary force of an utterance can be considered to be data. In sum, grounded theory aims to elicit the actual lived experience of individuals groups and teams. It provides ‘initial systematic discovery of theory from the data of social research' [Glasier & Strauss 1967:3]. Actor Network Theory (ANT), developed by John Law, Bruno Latour and others is a way of conceptualizing the relationships between people, technology and society. As a methodology, it incorporates some novel features; for example inanimate machines are accorded equivalent agency to human actors in line with the principle of generalized symmetry [Latour 2007]. ANT provides for translation of the network of actors by the following stages: problematisation; interassessment; enrolment; and mobilisation [Callon, 1986].
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John Walton The identification of what constitutes a problem to be solved and resolved is problematisiation. This means that different actors might and indeed would perceive different ‘issues of concern’ within the same problem situation. Thus ANT acknowledges plurality of view. The focus of ANT is to the identification of issues of concern that all actors will enroll to address. The Obligatory passage point is the point of access into this collective action and is according to the ANT theories irreversible. Knowledge Management is advancing rapidly not necessarily from technological determinism, but from environmental velocity and knowledge intensity of products and services. The rate limiting step in both individual and organisational performance could be the generation conception and deployment of actionable knowledge. The three worlds of Karl Popper furnish an insight into the manner in which individual perceptions are captured, experienced and rationalized. This provides a route from sense experience to tacit knowledge and thence to explicit knowledge. Explicit knowledge that is capable of being critically assessed will result in a body of knowledge that is actionable
4. The research method The research was conducted in three phases: the engagement phase; the data collection phase and the interpretation phase. The methodology is inductive and qualitative in nature. The engagement phase involved co‐ordinating the trustees and the staff to the need and the benefits of a review process. The data collection phase relied heavily on Grounded Theory Methodology whilst the interpretation phase was unfolded using Actor Network Theory [Latour 2005]. Each session was conducted in a room furnished in a simple manner a table: with five chairs, a blank white board, and some marker pens. The intention was to create a neutral environment which would encourage reflection. The subject was introduced to the framework and then asked to instantiate it. At this point the researcher sat down to assume a passive posture, while the subject was standing in an active posture.
Figure 2: The ‘empty’ data collection framework. As the instantiation of the framework progressed to become a model, some subjects drew links from one item to another in a different quadrant. Instantiating a model is not a linear method; the model does not have a start and finish. Indeed, as the model is constructed, various parts will be embellished by additions and afterthoughts. This means that the subjects themselves are linking activates, qualities and roles in the diagram. In one instance lines were drawn to link together elements located in different quadrants. This was incorporated in the coding. In every case the session of one hour, was exceeded. The session ended when no more could be added to the model. This suggested that the model was saturated.
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John Walton The interpretation of the models was based on Grounded Theory methodology. The entire image was photographed and then input to NVivo10, and specific areas of the image were identified and coded. The aim was to select the minimum area that represented a coherent semantic statement. The implications of the initial data collecting phase gave rise first to open coding and then to the classification into nodes, as shown in table 1. Two meetings were then convened to present these initial findings. These meetings were recorded, and from them using GTM two further nodes were added. Although the data had been interpreted, the next stage involved the meaning of the information, and this used the insights of Actor Network Theory.
5. Findings Coding involved the identification of constituents of the model produced from each session. Using the constant comparison method, elements were progressively grouped into nodes that will categorise a single concept. The next stage was to validate and explore these categories; so two focus groups were convened, one composed of the trustees and one of the staff. Two members of the staff play a trustee role. After each group the nodes were fine tuned in the light of the feedback. All is data, and the comments lead to exploration directed by theoretical sampling. The meaning attributed to the node can be 'fine‐tuned' due to inter‐subjective agreement. These ‘free’ nodes shown in table one. This process acted as filter between worlds 1 and 2 i.e. searching for common experiences whilst discarding idiosyncratic comments. The nodes contain discrete categories of the life world of staff in Access Space. However, the nodes cover a spectrum of experiences of the staff. Each node is therefore expressed a spectrum of two elements held in binary opposition. The meaning given by individual will be a dialectic which will furnish a unique position on the spectrum of meaning. Table 1: The nodes 1
NAME James
EXPLANATION CEO <> Peer Colleague
2 3 4 5
The Environment Employment The Trustees Access Space
6
Knowledge Management
7 8
The Individual Polychronic Tension
Order <> Action , Individual Discretion <> Collective Action Back grounded <> Foregrounded Self‐actualisation through technology <> The New Realism Knowledge <> Knowledge Acquisition Transfer Individual Flair <> Role Responsibilities Monochronic <> Polychronic
9 10
Adaptability Well Being
Exploitation (Plan) <> Exploration (Respond) Participant Empathy <> Staff Development
To aid the understanding of the table, one mode, the individual is now explained. Access space as a setting does allow the staff members the freedom to bring their personal strengths to bear and this is a positive characteristic. This is represented at one extreme as personal flair. However the staff also have a role. They are enabling learning by both creating a safe environment whilst encouraging peer support. To maintain this environment requires the management of resources. This required staff to embrace role responsibilities. There is a tension between the fluidity are of flair and role. So much so that tasks are sometimes performed out of necessity due to the non‐hierarchical, amorphous structure of the organisation. Ensuring an appropriate balance is a challenge both for the individual and the organisation as a whole.
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6. Discussion The empirical work showed a gap between knowledge in possession of the collective action and' knowledge in action of the individual experience [Cook & Brown 1999]. Furthermore, it did illuminate the difference between the concrete experiences and the challenges to assume a desired future abstract state. The nature of these tensions shows how demanding working in this context can actually be. Given the limited resources of the organisation it was recognised that not every issue can be addressed, but this did not inhibit the sincere involvement of a staff in trying to articulate the issues. That codification of the data into nodes and the subsequent discussion brought out about inter subjective agreement is a positive outcome for the team; it gave the staff a legitimate vocabulary of semantic integrity. It also demonstrates that even in a benign almost collegiate environment, tacit knowledge can be difficult to articulate. Socialisation, the first stage of the SECI spiral [Nonaka et al 2000] is not an easy stage for anyone. This is congruent with the concept of problematisation in Actor Network Theory. In fact, as the research outcome was discussed with the staff and the notion that the nodes represented the perception of all the staff (albeit with slight reservations) it became clear the main discussion was about the meaning of the nodes and their relevance to the future abstract state. The future abstract state was also important even though it was almost impossible to articulate. There has to be a passage towards a future in which the superordinate goal can make itself manifest by the users (participants) of the services Access Space provides. Therefore, the organisation is going through a process of translation [Callon 1986] and this will involve four steps problematisation; interessement; enrolment; and mobilisation. The point is that problematisation had not been discussed openly; the review process had therefore provided a legitimate basis for this to take place, even though this was an unexpected outcome. Problematisation requires that a focal actor bring all the other actors though an obligatory passage point. As its name implies all actors have to agree to journey through this point. And the passage is irreversible. The passage was the willingness to produce an honest rendition of their individual lifeword. The focal actor was the researcher. From a knowledge management point of view, the OPP furnished the substratum from which tacit knowledge can be converted into explicit knowledge and then combined to form an expression of the collective knowledge generated de novo. It is the consideration of this collective de novo knowledge that prepares the way for interassessment between the actors in the network. The translation process, although incomplete (the enrolment and mobilisation had yet taken place), had a significant effect of the perceived level of power distance [Hofstede 1980]. This was a beneficial and cathartic outcome and one that was unexpected The case study ended during the process of enrolment, that is the commitment of the actors in the light of the now knowledge to engage in a disposition to act. Before this can happen of course, mobilisation has to occur. Though the space is not chaotic, the interaction with the participants can be so. The sessions are event driven, and so are unpredictable. In line with this staff rely heavily on bounded rationality as opposed to comprehensive rationality [Simon 1999] in addressing problems. This raises questions about the use of tacit knowledge, and the awareness the staff have of this. The obligatory passage point articulates and filters appropriate course of action that congruent with the agreed superordinate goals. As can be seen from the collection of nodes the question of prioritisation has still to be made. If tacit knowledge cannot be articulated, it will reside in world two of Popper’s conceptualisation. However anxious, frustrated, ecstatic or fulfilled the staff may be there is no way to gain intersubjective agreement. This is because the knowledge would have to reside in world three. The intuition of the staff and the judgement and maturity with which the integrity of Access Space is maintained is a significant achievement. And making it work requires a substantial amount of knowledge in action [Cook & Brown 1999]. This work shows that there is a meta knowledge, a knowledge flow that is
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John Walton comprehensible by view of actor network Theory. The role of these knowledge flows characterises third generation knowledge management [Snowden 2007].This suggests that a synthesis of knowledge management together with an understanding of the nature of tacit knowledge can provide a potent precursor to the powerful ramifications of Actor Network Theory.
7. Conclusion The case study illustrates how the review process can reify and synthesise these knowledge flows. Furthermore, using Actor Network Theory, certain fundamental, often unstated assumptions were surfaced and articulated. The researcher in the role of the central actor managed the obligatory passage point by which all the other actors pass to enable there interests to be recognised. This additional knowledge, now explicit, can be mapped between world two and world three of Popper's model of knowledge. This enhances the quality of the problematization, makes interassment amenable to critical appraisal, and create a foundation for enrolment. Whilst the review process described here recognising the contribution of the individual, it also made the allowed the performance of the many to become analytically tractable.
Acknowledgements The author would like to thank Sr Anne Walton, BEd CPS, for advice and insightful criticism during the preparation of this paper.
References Baumard Philippe (1999). Tacit knowledge in organizations. SAGE Publications. Boisot, M. (1999). Knowledge assets: Securing competitive advantage in the information economy. Oxford University Press, USA. Callon, M, Law, J and RIP, Arie (1986). Mapping the dynamics of science and technology. Springer. Cook, S. and Brown, J. (1999). Bridging epistemologies: The generative dance between organizational knowledge and organizational knowing. Organizational science, 10 (4), 381. Hofstede, G. (1980). Motivation, leadership, and organization: Do American theories apply abroad. Organizational dynamics, 9 (1), 42‐63. Latour, B. (2005). Reassembling the social: An introduction to actor‐network‐theory. Oxford University Press, USA. Mintzberg, H., Ahlstrand, B. W. and Lampel, J. B. (2008). Strategy safari: The complete guide through the wilds of strategic management. Financial Times/Prentice Hall. Nonaka, I., TOYAMA, R. and KONNO, N. (2000). SECI,, Ba and leadership: A unified model of dynamic knowledge creation. Long range planning, 33 (1), 5. PolyaniI, M. (1966). The logic of tacit inference. Philosophy, 41 , 1‐18. Popper, K R. (1972). Objective knowledge. Clarendon Press Oxford. Simon, H A. (1999). Bounded rationality and organizational learning. Reflections: The SoL journal, 1 (2), 17‐27. Snowden, D J. and Boone, Mary E. (2007). A leader's framework for decision making. Harvard business review, 85 (11), 68. Strauss, A. L., Corbin, J. M. and LYNCH, M. (1990). Basics of qualitative research: Grounded theory procedures and techniques. Sage Newbury Park, CA. Walton, J. (2010). Education and skill development through the reconfiguration of discarded hardware: Turning base metal into intellectual capital.
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New Knowledge Creation by Collaborating Goal‐Oriented Experts: Methodology and Models Igor Zatsman and Pavel Buntman Institute of Informatics Problems of the RAS, Moscow, Russian Federation IZatsman@yandex.ru PBuntman@gmail.com Abstract: The topic of the paper is a methodology and models for interactive knowledge creation processes. It is assumed that a knowledge system is being evolved by a team of collaborating experts that creates new evaluation indicators for research and their meanings. We proceed from the notion of a goal‐oriented knowledge system. The experts team creates this system to fill a knowledge gap in an evaluation area. The gap can be identified through observation of the subject area. We define a goal‐oriented knowledge system as an expert‐created system of concepts generated for filling the knowledge gap. The main emphasis is placed on a methodology and models for new knowledge creation by collaborating experts. We have studied relationships between emerging indicators’ meanings and their volatile denotata as new knowledge sources. In our study denotata are represented by indicators data together with computer programs for indicators calculation. According to the methodology, experts using computer programs can interactively fix their own emerging indicators’ meanings. Emerging meanings are temporal and changeable concepts opposite to conventional concepts that have time‐ stable signifiers. The goal‐oriented experts’ cognition and interaction are the basic mechanisms for the new indicators’ meanings creation. The basic conditions for their creation and propagation are a computer dictionary, which is a tool for new meanings propagation between collaborating experts, as well as information and communication technologies. According to the methodology, experts can specify time‐dependent states of new indicators in the computer dictionary. Each state of an indicator is described by its digital denotatum, a meaning definition, and a name as an indicator signifier, which are used by experts to describe new knowledge by dictionary descriptors. In order to show feasibility of proposed methodology, we are designing the computer dictionary as a tool for indicator development. The dictionary is a part of the evaluation system prototype for verified monitoring and evaluating implementation of a research programme. Our dictionary design is based on two semiotic models for a description of indicators development stages, including generation processes of expert knowledge about developed indicators by collaborating experts. The computer dictionary based on these models of knowledge creation has shown new possibilities for developing indicators for evaluating research programmes. Collaborating goal‐oriented experts could interactively fix their own emerging indicators’ meanings, specify the states of developed indicators as dictionary descriptors, and harmonize emerging indicators with research programme’s goals using information and communication technologies. Keywords: interactive knowledge creation, frozen‐state model, time‐dependent model, development of new indicators, proactive dictionary for new indicators
1. Introduction From the point of view of economic policy, generating and evolving new knowledge provides the potential to create competitive advantage (Spender 1996; Boisot 1998; Mitchell and Boyle 2010). From the point of view of science policy, new knowledge provides the potential to reduce the incompleteness of indicators system for research programmes (Zatsman and Durnovo 2010). This paper presents a methodology and models for interactive processes of knowledge creation comprising new indicators’ meanings. The central debate is about two semiotic models of these processes. At present the spiral model of knowledge creation, which is described in the works (Nonaka 1991; Nonaka and Takeuchi 1995), is one of the most popular. The model is very often mentioned in discussions about knowledge creation. Two categories of knowledge are determined in this model: individual knowledge and group knowledge. Each of these categories is divided, in turn, into two sub‐categories: explicit knowledge and tacit knowledge. The generalization of spiral model of knowledge creation was proposed in the works (Wierzbicki and Nakamori 2006, 2007), where the definition of the creative space was given. In essence this generalization was related to the division of knowledge, not into two categories as in the works (Nonaka 1991; Nonaka and Takeuchi 1995), but into three:
Individual knowledge;
Group knowledge;
Humanity knowledge.
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Igor Zatsman and Pavel Buntman Taking into account the division of each of three categories into two sub‐categories (tacit and explicit knowledge) Wierzbicki and Nakamori obtained six types of knowledge in the creative space. In the generalized model of knowledge creation, besides these six types, nine transition processes are defined, including socialization, externalization, combination and internalization. Additionally, they included individual, group and conventional emotions in the space. Consequently, they obtained a generalization of the spiral model of knowledge creation and within its framework they defined six types of knowledge, nine types of transition processes between various sub‐categories of knowledge, and three types of emotions. The generalized model does not describe how new knowledge parts are created, it does not have a quantitative time axis and does not define those discrete points in time where new knowledge parts are produced. In order to use a time axis and to increase an applied potential of knowledge creation modelling, two semiotic models have been proposed (Zatsman 2009a; Zatsman 2009b; Zatsman 2012a):
The model describing a frozen state of a knowledge creation process, named the frozen‐state model;
The model for identifying a dynamics of a knowledge creation process, named the time‐dependent model.
These frozen‐state and time‐dependent models are based on two pillars, namely:
Examination and categorization of Human Computer Interaction (HCI);
Definition of a digital semiotic triangle.
The main objective of this paper is to describe new indicators’ meanings emergence using these models and their pillars. Our main applied fields are research programme evaluation by indicators and science policy in general. From the point of view of research evaluation, these models have an applied potential to create new indicators of scientific performance assessment and to design evaluation systems for verified monitoring, analysis and assessment of scientific activities (Zatsman and Kozhunova 2009; Zatsman 2012b; Zatsman and Durnovo 2012).
2. Categorization of human computer interaction In our study we use three following media for the examination and categorization of HCI by medium borders: knowledge medium, social information medium, and digital medium. The knowledge medium contains indicator concepts, i.e. individual or collective experts’ notions concerning indicators. The social information medium contains indicator names, i.e. indicator designations. The digital medium contains indicator denotata including both indicator computer programs and source data, as well as computer codes of names, words, pictures and so on. These source data together with the computer program, which calculates indicator values, is by definition a digital denotatum of a stable indicator or a digital denotatum state of a changeable indicator it a discrete point in time (Zatsman, 2009b). Usually we capture concepts using names or words, employing semiotic sign system. In terms of three mentioned media this sign system belongs to the border between the knowledge and the social information media. We will name the HCI on this border as the sign HCI. On the Fig. 1 one sign integrating a concept with its name is shown as a circle on this border. The second border is located between the social information medium and the digital one. This border separates the names and the words from their computer codes. We did not find a conventional name for the twofold entity, which integrates a name or any other word with its computer code just as a sign integrates a concept with its name. Most likely, this entity has had no conventional name so far. It has been given a name of formcode by Zatsman (2003). In terms of three mentioned media formcodes belong to the border between the social information and the digital one. We will name the HCI on this border as the formcode HCI. On Fig. 2 the formcode is represented by a circle on this border. Here, sign and formcode categories of HCI are always considered jointly. Two categories do not fully represent the categorization of HCI by medium borders. There is a third border between the knowledge medium and the digital one. This border separates concepts from their computer codes. We did not find a conventional name for the twofold entity, which integrates a concept with its
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Igor Zatsman and Pavel Buntman computer code. Most likely, this entity has also had no conventional name so far. It has been given a name of semcode by Zatsman (2003).
Figure 1: Three media, sign and formcode categories of HCI The border between these media is depicted as a double line on Fig. 2. We will name the HCI on this border as the semcode HCI. According to the semcode HCI experts can code their emerging individual indicator concepts with the help of computer codes. To perform that, they use the proactive dictionary for fixing the personal introspection results after a semantic interpretation of indicator denotatum state. Eventually, we have obtained the first base pillar for the frozen‐state and time‐dependent models as three following categories of HCI: sign, formcode, and semcode HCI.
3. Two models of indicator development The idea of the frozen‐state and time‐dependent models has arisen in the course of our study of knowledge generation processes (Nonaka 1991; Nonaka and Takeuchi 1995; Nonaka and Nishiguchi 2001), the creative space introduced by Wierzbicki and Nakamori (2006, 2007) and its employment for providing electronic support for knowledge creation in a research institute (Ren et al. 2007). These models are based on the Frege’s triangle, which consists of concept, name and denotatum vertices. In terms of semiotics for each state of new developed indicator these three vertices of the triangle are the sign‐ meaning (the indicator concept), the sign‐form (the indicator name), and the denotatum of the sign (both an indicator computer program and source data). We use three following media for modelling the indicators development process: the knowledge medium, the social information medium and the digital medium (Fig. 1&2). On Fig. 3, the digital medium is divided into two parts. The top digital medium includes an indicator denotatum. The bottom digital medium contains three computer codes for the indicator denotatum (object code), its concept (semantic code) and its name (information code). By definition, these three computer codes generate a digital semiotic triangle (Zatsman, 2009b). The digital semiotic triangle is the second pillar for the frozen‐state and time‐dependent models for a description of indicators development stages, including generation processes of expert knowledge about developed indicators. It’s important to emphasize that both pillars, which belong to different knowledge areas (HCI categorization to informatics, Frege’s and digital triangles to semiotics), were described by the term system developed for modelling new knowledge creation processes (Zatsman, 2003; Zatsman, 2009a).
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Figure 2: Semcode HCI Two models descripted further are based on these pillars. The first one is the frozen‐state model of a new developed indicator state. The indicator is evaluated by a computer, which uses both source data and a computer program for its evaluation. By definition (Zatsman, 2009a), at a discrete point in time, the frozen‐ state semiotic model of any state of the new developed indicator consists of (Fig. 3):
Knowledge, social information and digital media;
The Frege’s triangle of a new developed indicator state including its denotatum state, concept and name;
The digital semiotic triangle of this state including denotatum state, concept and name computer codes.
According to the frozen state model, three vertices of the Frege’s triangle are encoded into three computer codes, recorded into the proactive dictionary at the discrete point in time. These codes are:
A semantic code for the indicator concept;
An information code for the indicator name;
An object code for the indicator denotatum state.
During introspection processes, experts make state descriptions of the new developed indicator as proactive dictionary descriptors. In other words, experts fix the personal introspection results in the proactive dictionary: each personal semantic interpretation of any state of the new developed indicator is described as a separate dictionary descriptor. Each dictionary descriptor includes denotatum state and concept descriptions, indicator name and their three computer codes at the discrete point in time. The main idea of computer coding of each new emerging indicator concept, its name and its denotatum is that each developed indicator is analyzed and described by experts in the proactive dictionary from three points of view:
As an emerging indicator concept;
As a variable indicator name;
As a changeable indicator denotatum.
Each changeable indicator denotatum is a computer program of indicator computation together with corresponding source data, which are information resources of the evaluation system. The indicator names are given and changed by the experts, which are developing these indicators. The second model for identifying the dynamics of the indicator development process is the time‐dependent model. During any indicator development process, its emerging concept description, its name, and its denotatum may be changed by the experts in great extent. Moreover, key stages of the development process
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Igor Zatsman and Pavel Buntman of each indicator are the iterative coordinations of different personal concept descriptions between the experts to form a collective (group) concept description of the indicator.
Figure 3: The frozen‐state semiotic model of a state of new developed indicator The time‐dependent semiotic model takes into account the variability of emerging indicator concepts, their names, and their denotata. By definition (Zatsman, 2009b), the time‐dependent model of a new developed indicator consists of (Fig. 4):
Knowledge, social information and digital media.
Frege’s triangles of all states of the new developed indicator at the discrete points in time (ti, i = 1,2, ...).
Digital semiotic triangles of states at ti, i = 1,2, ... .
The time‐dependent model is based on the frozen‐state model. Fig. 4 shows two stages of the new indicator generation. The first stage of the new indicator generation is on the left. At this stage, we see the first state of the indicator at the discrete point in time t1. At the first stage, the new indicator denotatum has been created and interpreted by the expert. Its concept and its name have been created by the same expert. The first digital semiotic triangle comprises three computer codes (semantic, information, and object codes) generated by the evaluation system at t1. The second stage of the new indicator generation is on the right. At this stage, we observe the second state of the indicator at the discrete point in time t2. At the second stage, the new indicator’s denotatum has been changed and interpreted by the expert. During indicator interpretation the expert is able to change its concept and its name. The second digital semiotic triangle also comprises three computer codes generated by the evaluation system at t2. The time‐dependent model describes all development stages of each indicator at discrete points in time (ti, i = 1,2,...), where ti is the i‐th stage of the indicator development. It is supposed, that at each ti for developed indicators changed at the ti , experts describes indicator frozen states into the proactive dictionary. At the same time the evaluation system generates three computer codes: semantic, information, and object ones.
4. Proactive dictionary as a part of the evaluation system Only the experts who are able to develop new indicators may be proactive dictionary users. The proactive dictionary is used as a tool for indicator development to describe the dynamics of expert knowledge about developed indicators. Each state of any developed indicator is interpreted by several experts. Experts fix their personal introspection results as proactive dictionary descriptors. At discrete points in time (ti, i = 1,2,...), each expert may create proactive dictionary descriptors according to the time‐dependent model.
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Figure 4: The time‐dependent model of new developed indicator for two stages Experts can access existing descriptors of the proactive dictionary through the expert console software of the evaluation system and receive information about any frozen‐state of each developed indicator. In the proactive dictionary the following events of indicator development are fixed:
Descriptor creation for each uninterpreted or interpreted frozen state of any developed indicator (with indication of their authors from experts group);
Links modifications between descriptors;
The modification of descriptor references to the computer programs that calculate values of developed indicators, and of descriptor references to their source data.
Thus, each descriptor of the proactive dictionary contains a indicator frozen‐state definition, its name, three computer codes, and links to other descriptors, as well as references to source data, which are evaluation system information resources (see references to information resources at t1 and tn on Fig. 5) and computer programs1) which assess values of developed indicators (see references to program resources at t1 and tn). Any indicator development includes two main phases: personal (one expert creates an indicator) and collective (an indicator is coordinated by several experts of the group). In case the experts refuse an indicator it is considered as a non‐actual, but still resides in the proactive dictionary. Besides the proactive dictionary, which is periodically enriched by new frozen states of developed indicators, evaluation system linguistic resources include a semantic dictionary, which is accessed by all types of users of the evaluation system (Zatsman and Kozhunova, 2009). All semantic dictionary descriptors do not change in time. While the proactive dictionary deals with time‐ dependent indicators, the semantic dictionary describes the stable indicators and has stable links and references (Fig. 5). Each time‐dependent indicator can potentially turn into a stable one and thus can be incorporated into the semantic dictionary. For such transformation this indicator has to be coordinated between the experts and confirmed by decision‐makers. The proactive dictionary supports the possibility to show any frozen state of the developed indicators, because it keeps all their frozen states that have ever existed. Operations of descriptors modifying or deletion are invalid for the proactive dictionary: the descriptor modification leads just to the generation of a new descriptor, while maintaining the old one. So, it is more convenient to consider a new descriptor as the successor of the original one. In the proactive dictionary such inheritance can be multiple, because a new descriptor may be the heir to a number of descriptors.
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Figure 5: Semantic and proactive dictionaries of the evaluation system
5. Two developed indicator variants We are performing an experiment in which we develop variants of an indicator, which describes the article authors’ age distribution. These authors participate in the R&D programme of the Russian Academy of Sciences (RAS) named “Basic research for medicine”. One of this programme’s goals is to attract young scientists to participate in its projects. In total, the programme held 130 projects in 2007 and 146 in 2008. The total number of its participants was 1,334 in 2007 and 1,504 in 2008. One of the approved indicators of the program represents a share of participants under the age of 39 years. But this indicator says nothing about the productivity of young scientists. Therefore, we proposed to develop a set of indicators to calculate the authors’ age distribution by dividing them into 14 age groups (20 – 24, 25 – 29 and so on up to 85 – 89 years). The calculated results of two developed indicator variants are given in the form of graphs (Fig. 6). Here we describe the first five stages of the experiment. Five collaborating experts take part in the indicator development: A, B, C, D and E. First stage: Expert A creates the first variant of the developed indicator with the following characteristics:
To calculate the developed indicator values, expert A uses all articles of the R&D programme “Basic research for medicine” participants entered into the evaluation system database;
If an article has been written by co‐authors, corresponding age groups receive 1 point for each author;
The normalization procedure using the size of age groups is not applied.
At the same time (at the first stage) expert B creates the second variant of the developed indicator with the same first and third characteristics, meanwhile the second one has a different value: corresponding age groups receive 1/N point for an article having N co‐authors; the expert B also coordinates the second variant with expert C at the first stage. Second stage: The expert C changes his mind and agrees with the expert A. In other words, the expert C refuses to coordinate the second indicator variant, thinking it is right to add just 1 point to corresponding age groups, thus coordinating the first variant of the indicator. So, the first variant becomes a collective one, while the second variant becomes personal. The modifications of two variants themselves are identical to those of the first stage (these modifications are not shown on Fig. 6). Third stage: Experts A, B and C decide to take into account the number of participants in each age group when calculating values of their two indicator variants. It is reflected in corresponding algorithms changes – the normalization procedure is used in the calculations. Fourth stage: Experts A, B and C decide to take into account only articles published in the journals from the Nation Certification Commission of Russia rating list, entered into the evaluation system database.
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Igor Zatsman and Pavel Buntman Fifth stage: Now the experts leave their indicator variants unchanged. Yet the points of view of some of them change. The expert C refuses the expert A variant, so this concept becomes a personal concept of the expert A. Two other experts, D and E, agree with the second variant of the indicator (these modifications are not shown on Fig. 6).
Figure 6: Results of calculation of the developed indicator values Our experiment has clearly demonstrated that different experts can interpret a new indicator denotatum state in various ways. Moreover, this interpretation can change in time. Traditional approach is to develop unified technique and use it for indicator value calculation and comparing different R&D programmes. However this approach doesn’t guarantee that the same indicator calculation programs will be used for different R&D programmes evaluation during their comparison according to the unified technique. The reason of it is that different experts and/or programmers interpret the same technique of indicator value calculation in various ways. It is not obvious that the different programs are used. Therefore unified technique usage doesn't provide a unified evaluation of different R&D programmes. The unified software and comparable information resources should be also used for indicator value calculation. Meanwhile all the disagreements between experts should be resolved when discussing the unified technique for indicator value calculation in advance, simultaneously and iteratively developing a unified program and a unified technology of informational resources’ forming in order to calculate indicator values.
6. Concluding remarks Aiming to create a tool for developing sets of indicators for verified monitoring and evaluating R&D activities, we have introduced and applied the proactive dictionary as a part of the evaluation system. The obtained results, considered in the paper, are:
Two semiotic models of the processes of indicators development;
The proactive dictionary, created on the base of these models;
The calculated results of two developed indicator variants.
These models of knowledge creation and the proactive dictionary show new possibilities in indicators’ developing for verified monitoring and evaluating, specifying the states of developed indicators as descriptors. Indicators’ development is not the only goal of the proactive dictionary design. Its implementation provides the solution of other important tasks. Firstly, proactive dictionary descriptors set a one‐to‐one correspondence between indicator denotata, including their computing programs, and indicator names. In other words, each indicator name corresponds to a single program calculating its values. Secondly, each descriptor fixes a rule of selecting source data, which are used in calculating values of the indicator. That is why the confirmation process of any new indicator is a
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Igor Zatsman and Pavel Buntman simultaneous approval of the following entities: its name, its definition, the calculating program and the rule of selecting source data used in calculating indicator values.
Acknowledgements This research is funded by RFH grant No. 12‐02‐00407. Computer programs were developed by V. Kosarik.
References Boisot, M. (1998) Knowledge Assets: Securing Competitive Advantage in the Information Economy, Oxford University Press, New York. Mitchell, R. and Boyle, B. (2010) “Knowledge creation measurement methods”, Journal of Knowledge Management, Vol 14, No. 1, pp 67‐82. Nonaka, I. (1991) “The knowledge‐creating company”, Harvard Business Review, Vol 69, No. 6, pp 96–104. Nonaka, I. and Nishiguchi, T. (Eds.) (2001) Knowledge emergence, Oxford University Press, New York. Nonaka, I. and Takeuchi, H. (1995) The Knowledge‐Creating Company, Oxford University Press, New York. Spender, J.‐C. (1996) “Making knowledge the basis of a dynamic theory of the firm”, Strategic Management Journal, Vol 17 (Winter Special issue), pp 45‐62. Ren, H., Tian, J., Nakamori, Y. and Wierzbicki, A. (2007) “Electronic support for knowledge creation in a research institute”, Journal of Systems Science and Systems Engineering, Vol. 16, No. 2, pp 235–253. Wierzbicki, A. and Nakamori, Y. (2006) “Basic dimensions of creative space”, In Creative Space: Models of Creative Processes for Knowledge Civilization Age, Wierzbicki, A. and Nakamori Y. (Eds.), Springer, Heidelberg, pp 59‐90. Wierzbicki, A. and Nakamori, Y. (2007) “Knowledge sciences: some new developments”, Zeitschrift für Betriebswirtschaft, Vol 77, No. 3, pp 271‐295. Zatsman, I. (2003) Concept Retrieval and Information Quality, Publishing House “Nauka”, Moscow (in Russian). Zatsman, I. (2009a) “A semiotic model of correlations between concepts, information objects and computer codes”, Informatics and its Applications, Vol. 3, No. 2, pp 65–81 (in Russian; abstract in English from: http://www.ipiran.ru/journal/issues/2009_02_eng/annot.asp). Zatsman, I. (2009b) “Time‐dependent semiotic model of computer coding of concepts, information objects and denotata”, Informatics and its Applications, Vol 3, No. 4, pp 87–101 (in Russian; abstract in English from: http://www.ipiran.ru/journal/issues/2009_04_eng/annot.asp). Zatsman, I. (2012a) “Tracing Emerging Meanings by Computer: Semiotic Framework”, In 13th European Conference on Knowledge Management Proceedings, Vol. 2, Academic Publishing International Limited, Reading, pp 1298–1307. Zatsman, I. (2012b) “Denotatum‐Based Models of Knowledge Creation for Monitoring and Evaluating R&D Program Implementation”, In 11th IEEE International Conference on Cognitive Informatics & Cognitive Computing Proceedings, IEEE Computer Society Press, Los Alamitos, pp 27–34. Zatsman, I. and Durnovo, A. (2010) “Incompleteness problem for indicators system of research programme”, In 11th International Conference on Science and Technology Indicators (Book of Abstracts), Universiteit Leiden, Leiden, pp 309–311 [online], http://www.cwts.nl/pdf/ BookofAbstracts2010_version_15072010.pdf. Zatsman, I. and Durnovo, A. (2011) “Modelling of processes for creation of expert knowledge for monitoring of goal‐ oriented programme activities”, Informatics and its Applications, Vol 5, No. 4, pp 84–98 (in Russian; abstract in English from: http://www.ipiran.ru/journal/issues/ 2011_04_eng/annot.asp). Zatsman, I. and Durnovo, A. (2012) “Proactive Dictionary of Evaluation System as a Tool for Science and Technology Indicator Development”, In 17th International Conference on Science and Technology Indicators Proceedings, Vol. 2, Science‐Metrix and OST, Montréal, pp 905–906. Zatsman, I. and Kozhunova, O. (2009) “Evaluation system for the Russian Academy of Sciences: objectives‐resources‐results approach and R&D indicators”, In E‐print Proceedings of the International Conference ATLC’2009 “Atlanta Conference on Science and Innovation Policy 2009, [online], http://smartech.gatech.edu/bitstream/1853/32300/1/104‐674‐1‐PB.pdf.
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Knowledge‐Intensive Business Services (KIBS) and Their Role in the Knowledge‐Based Economy Malgorzata Zieba Gdansk University of Technology, Gdansk, Poland mz@zie.pg.gda.pl Abstract: The development of knowledge‐intensive business services in recent decades can be interpreted as one of the indicators of a transformation from an industrial economy into a knowledge‐based one. Not only do quantitative measures, whether in the form of sales or employment figures (e.g. Chadwick, Glasson and Lawton Smith, 2008), undoubtedly show the expansion of these services; but also their characteristics make it clear that they significantly affect the formation and spread of knowledge throughout the economy. The importance of the knowledge‐intensive business services sector for the development of the economy is growing. In the 80s and 90s this sector became the fastest growing sector in the OECD countries (OECD, 2001). Many publications stress its close relationship to the levels of innovation and performance of the whole economy (e.g. Hipp, 1999; Tomlinson, 1999; Aslesen and Isaksen, 2007). It is an increasingly common belief that KIBS not only perform innovation activities at the service of the manufacturing sector, but they are also "bridges of knowledge" or "innovation bridges" connecting the manufacturing sector, science and customers (Czarnitzki and Spielkamp, 2003). The reasons for this phenomenon include the fact that KIBS play an active role in creating and spreading knowledge and increasing production capacity. In addition, KIBS provide access to the scientific and technological information distributed within a system. They act as holders of proprietary, quasi‐generic knowledge, implemented by interacting with customers and the scientific community. They also act as an active transmitter between the codified knowledge stored in universities and research laboratories and its hidden counterpart, located in the practices of companies (den Hertog, 2000). In other words, companies offering KIBS are the point of contact between the more general scientific and technological information distributed in the economy and the local requirements and problems of their clients (Muller and Doloreux, 2009). By creating an appropriate infrastructure, KIBS affect the ability of the economy to innovate activities that are of much importance for the development of a competitive knowledge‐based economy. This article presents an analysis of the KIBS sector based on a literature review. In the first section, it presents the issues connected with defining and categorizing KIBS. In the second one, it shows the significance of this type of service, highlighting its influence on the innovativeness of the companies which it serves. The third section consists of a detailed review of the literature devoted to research on KIBS. Keywords: knowledge‐intensive business services (KIBS), innovation, knowledge.
1. Introduction Knowledge‐intensive business services (KIBS) are an important part of knowledge‐intensive sectors in every economy. The development of KIBS in recent decades can be interpreted as one of the major factors in a transition from an industrial economy into a knowledge‐based one. Not only do quantitative measures, whether in the form of sales or employment figures (e.g. Chadwick, Glasson and Lawton Smith, 2008), undoubtedly show the expansion of these services, but also their characteristics seem to confirm their significant effect on the formation and spread of knowledge throughout the economy. This results in a growing interest on the part of researchers in the analysis of KIBS. This article presents an overview of the literature on these services and highlights their significance for economic development.
2. Defining and categorizing KIBS The problems associated with defining and characterizing KIBS stem from the fact that it is difficult to define and measure the knowledge‐intensity of these services. One possible indicator for defining the knowledge‐ intensity of KIBS could be the structure of the formal education of the employees working for such companies (Miles, 2005). However, despite the fact that this indicator is relatively easy to measure, it has a major drawback: it does not include the non‐formal education and work experience of employees, which is crucial for KIBS activity. Without specialized experience, companies offering KIBS have no chances in the struggle against market competition. In addition, this indicator does not take into account other forms of knowledge, such as tacit knowledge within the company, the organization's ability to learn, or its ability to acquire knowledge from the wider environment. Another disadvantage of this indicator is underestimation of the performance of KIBS sector companies, such as service innovation. To include the value of KIBS production, one should collect information on R&D spending, or the number of patents obtained by KIBS companies. Information on patents, however, may not be meaningful because, due to the characteristics of KIBS, reporting patents in this sector is relatively
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Malgorzata Zieba uncommon. The same applies to expenditure on R&D, which might instead take an informal form. In addition, an indicator based only on R&D expenditure does not take into account the fact that knowledge which has been generated in one sector can be applied in another. In this way, a sector with low R&D spending may be the primary user of knowledge generated in other sectors. Knowledge‐intensity might also mean a non‐routine character of services (Muller and Doloreux, 2009). If that is the case, the meaning of "routine” has to be determined, and this is not an easy issue. Another approach is to define knowledge‐intensity as the ability to integrate different sources of information and knowledge in innovation processes within the company. According to this definition, KIBS are characterized by an ability to retrieve information from outside the company and transform this information, combined with knowledge about the company, into a service useful to their clients. In other words, KIBS are intermediaries between the producers of knowledge and its users (Hipp, 1999). An important feature of KIBS indicated by many researchers is that they are addressed to companies or organizations, rather than to households (Toivonen, 2004). KIBS are largely based on professional knowledge (expertise) associated with a specialized field or discipline, and provide intermediate (not final) products (den Hertog, 2000). Due to the fact that KIBS companies offer intangible services with a high degree of adaptation to the needs of individual customers, the "production" of such services requires close and intensive collaboration between the given company and its customers. Without the collaboration of customers with a company offering KIBS, it is impossible to obtain tacit knowledge located in the client organisation, which is an important component of knowledge‐intensive services. Below are gathered the most important definitions and characteristics of KIBS identified in the literature. Table 1: Definitions and characteristics of KIBS according to various authors Author Miles et al. (1995)
KIBS definition “services that involved economic activities which are intended to result in the creation, accumulation or dissemination of knowledge”
Den Hertog (2000)
Toivonen (2004)
“those services provided by businesses to other businesses or to the public sector in which expertise plays an especially important role”
Pardos, Gomex‐Loscos and Rubiera‐Morollon (2007)
“personalized services that offer a relatively diversified range with high quality provision”
Koch and Strotmann (2008)
“highly application‐oriented services (in which) tacit knowledge plays an important role” “intermediary firms which specialise in knowledge screening, assessment and evaluation, and trade professional consultancy services”
Consoli and Elche‐Hortelano (2010)
Source: Own, based on literature review.
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KIBS characteristics ‐ they rely on professional knowledge to a high extend; ‐ they either are themselves primary sources of information/knowledge or they use knowledge to produce intermediate services for their clients’ production processes; ‐ they are of competitive importance and supplied primarily to business. ‐ private companies/ organisations; ‐ they rely on knowledge or expertise related to a specific (technical) discipline or (technical) functional domain; ‐ they supply intermediate products and services that are knowledge based. ‐ they have numerous and versitile contacts with different stakeholders; ‐ they form a node in a system of customers, cooperation partners, public institutions and R&D establishments. ‐ they imply an important connection with information, new technologies, new management, production/sales techniques, to new markets. ‐ they require specialized knowledge and cumulative learning processes
Malgorzata Zieba The main problem in classifying KIBS is the lack of compatibility of official statistical categories of industries and services with reality. To define KIBS properly, a detailed description of company activities is often required, rather than the statistical data on which international classifications are based. Taking into account the pace of changes in the KIBS sector and the often blurred boundaries of its sub‐sectors, it is difficult to determine which business services should be classified as KIBS and which should not. Another problem is that some services included in KIBS are provided not only on a business‐to‐business basis, but also to individuals. Examples of such services include computer and legal services. At the same time, knowledge‐intensive services are provided in some sectors not classified as KIBS and therefore they should be included in the KIBS category. It must be remembered that these services are not only offered by companies specializing in providing KIBS. Despite the many disadvantages of classifying KIBS on the basis of official statistical classifications, many scientists apply them in their research. According to Baláž (2004), typical examples of KIBS are: accounting, management consultancy, technical engineering, R&D activities, design, services related to computer and information technology, and financial services. This author includes NACE (Classification of Economic Activities in the European Community) sectors 64‐74 (see the table below) in KIBS and highlights the particular importance of information and communication KIBS. In the opinion of Baláž (2004), communication services enhance the transfer of KIBS to all users in the economy, thus enabling the spread of KIBS to other sectors of the economy. As a result, technological and organizational innovations are produced and applied. Another classification of KIBS is proposed by Miles et al. (1995). They divide them into traditional professional services (KIBS I) and businesses using new technologies and new knowledge‐intensive services (KIBS II) creating new technologies. The first group of these services includes marketing, advertising, etc., while the second group covers services such as software design (Table 2). Later, the division between KIBS I and KIBS II turned into a division between a) consulting services, such as legal services, accounting, auditing, market research and management; and b) technical services, which include activities related to computer services or engineering/construction services. The first of these exist in the literature as P‐KIBS (Professional KIBS) and the second as T‐KIBS (Technical KIBS). Sample classifications of KIBS proposed by various authors are presented in Table 2. Table 2: Definitions and characteristics of KIBS according to various authors Author Baláž (2004)
Miles I. et al. (1995)
Knowledge‐intensive business services 64 Post and telecommunications 65 Financial intermediation services, except insurance and pension funding services 66 Insurance and pension funding services, except compulsory social security services 67 Services auxiliary to financial intermediation 70 Real estate services 71 Renting services of machinery and equipment without operator and 72 Computer activities and software supply 73 Research and development 74 Other business services (Legal activities, accountancy, advertising) KIBS I: Traditional Professional Services, liable to be intensive users of new technology Marketing/advertising; Training (other than in new technologies); Design (other than that involving new technologies); some Financial services (e.g. securities and stock‐market‐related activities); Office services (other than those involving new office equipment, and excluding “physical” services like cleaning); Building services (e.g. architecture; surveying; construction engineering, but excluding services involving new IT equipment such as Building Energy Management Systems)); Management Consultancy (other than that involving new technology); Accounting and bookkeeping; Legal services; Environmental services (not involving new technology, e.g. environmental law; and not based on old technology e.g. elementary waste disposal services). KIBS II: New Technology‐Based KIBS Computer networks/telematics (e.g. VANs, on‐line databases); some Telecommunications (especially new business services);
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Malgorzata Zieba Author
Koch and Strotmann (2008)
Knowledge‐intensive business services Software; Other Computer‐related services ‐ e.g. Facilities Management; Training in new technologies; Design involving new technologies; Office services involving new office equipment); Building services (centrally involving new IT equipment such a Building Energy Management Systems)); Management Consultancy involving new technology; Technical engineering; Environmental services involving new technology; e.g. remediation; monitoring; Scientific/laboratory services; R&D Consultancy and "high‐tech boutiques". Technical KIBS 72.1 Hardware consultancy 72.2 Software consultancy and supply 72.3 Data processing 72.4 Data base activities 72.5 Maintenance and repair of office, accounting and computing machinery 72.6 Other computer related activities 73.1 Research and experimental development on natural sciences and engineering 74.2 Architectural and engineering activities and related technical consultancy 74.3 Technical testing and analysis Professional KIBS 73.2 Research and experimental development on social sciences and humanities 74.1 Legal, accounting, book‐keeping and auditing activities / tax consultancy / market research etc. 74.4 Advertising
Based on: Baláž (2004), Miles et al. (1995); Koch and Strotmann (2008). Koch and Strotmann (2008) identify fewer service types as KIBS than other authors. They list among KIBS those belonging to NACE sectors 72, 73 and 741‐744, excluding certain sub‐sectors from 744, e.g. the activities of holding companies . The classifications used in many works devoted to KIBS frequently follow the NACE scheme, which has become popular for identifying KIBS and is used by official bureaus in many countries, especially in the European Union. Sometimes other industry classifications are also applied, e.g. the International Standard Industry Classification (ISIC), which is a United Nations system for classifying economic data.
3. Significance of the KIBS sector Many publications stress the close relationship between KIBS and the levels of innovation and performance of the whole economy (e.g. Hipp, 1999; Tomlinson, 1999; Aslesen and Isaksen, 2007). It is an increasingly common belief that KIBS not only perform innovation activities in the service of the manufacturing sector, but they are also "bridges of knowledge" or "innovation bridges", connecting the manufacturing sector, science and customers (Czarnitzki and Spielkamp, 2003). Works devoted to KIBS are often based on Schumpeter's concept, according to which new combinations of existing means of production drive economic growth in the economic system (Baláž, 2004). With an increase in the importance of intangible resources in the modern economy, the generation and diffusion of knowledge are the main drivers of innovation. The knowledge at the base of innovations might be tacit or explicit, but innovations are usually the result of interaction between these two types of knowledge. Both tacit and explicit knowledge can be created in a company or acquired from external sources. One of the main external sources of knowledge for companies are KIBS. The growing demand for KIBS results inter alia from the rapidly changing technological environment and the increasing complexity of science. Companies are more and more often unable to cope with the challenges of the environment by using their own resources. Therefore they use KIBS to avoid the cost of acquiring and maintaining professional knowledge internally. As Miles et al. (1995) note, companies nowadays apply two processes as they aim to achieve better results. The first of these processes is specialization – organizations
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Malgorzata Zieba focus on their core competencies and outsource other activities. In this way, they eliminate problems associated with management and the integration of the different aspects of their business. The second process – called flattening or de‐layering – is characterized by a reduction in the number of organisational layers by compressing the hierarchy of management and eliminating middle management. These two processes both result in an increased demand for KIBS. Moreover, organizations have realized that if they want to deal with local and international competition, they must invest not only in human and material resources, but also in intangible resources, such as abilities, skills, organizational processes, intellectual property, and, finally, the information and knowledge possessed by the organization. These and other assets are complex and difficult for other organizations to copy, allowing the creation of a long‐term competitive advantage. Companies offering KIBS create and take care to preserve their intangible resources, and through their services they also help other companies create such resources. The reasons for this phenomenon include the fact that KIBS play an active role in creating and spreading knowledge and increasing output capacity (Baláž, 2004). They provide access to scientific and technological information and they act as holders of proprietary, quasi‐generic knowledge, implemented by interacting with customers and the scientific community. Moreover, they act as an active link between the codified knowledge stored in universities and research laboratories and the tacit knowledge located in the practices of companies (den Hertog, 2000). Because of their numerous and broad contacts with various partners, such as clients, public institutions, companies engaged in R&D, research institutes and universities, KIBS are seen as integrators of different parts of the innovation system (Toivonen, 2004). By creating an appropriate infrastructure, KIBS affect the ability of the economy to perform the innovative activities that are so important for the development of a competitive knowledge‐based economy. The importance of KIBS might also be confirmed indirectly by the growing interest of researchers in this area. The next section of this article presents a brief overview of the major research areas on KIBS.
4. Research on KIBS Research on KIBS has a relatively short history. Scholars only began carrying out studies in this area in the 1990s. Although more than two decades of KIBS investigation have now taken place, there are still many unanswered questions concerning this type of service and its influence on the whole economy. Three main phases of KIBS research can be identified in the literature. The first of these mainly concerned the identification of KIBS and the theoretical aspects connected with them. Miles et al. (1995) were the first to identify the detailed characteristics of KIBS and they offered a list of the sectors that fall into this category. The idiosyncratic features of KIBS were a high level of innovativeness and a contribution to the development of many economic sectors and the economy as a whole. The second phase abounded with more empirical evidence on the uniqueness of KIBS. Studies were conducted on their innovation patterns (Freel, 2006) and interactions with clients (den Hertog, 2000). Special attention was given to their influence on regional and national innovation systems (Muller and Zenker, 2001; Muller and Zenker, 2001a; Thomi and Bohn, 2003; Baláž, 2004) and regional development (Wood, 2006). The current third phase of KIBS research involves in‐ depth analysis of the various factors and determinants connected with the operations of such companies and their interactions with the environment. The literature on KIBS can also be analysed in terms of the various aspects of these services concerned. Such an overview is presented in Table 3. The first area of analysis is innovativeness. In analyses of KIBS and their innovativeness, the following streams can be identified: the innovativeness of KIBS themselves, that of the customers they serve (e.g. manufacturing companies), innovation patterns and types. The issue of innovativeness also arises within the second area: interactions between KIBS and the environment. Here one can find analyses of KIBS‐related regional development, spacial proximity, clustering and regional innovation systems.
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Malgorzata Zieba The third area concerns knowledge and all the processes related to it, e.g. R&D activities, knowledge management, knowledge absorptive capacity, knowledge spillovers, expert knowledge, etc. The fourth area puts emphasis on the human aspects of KIBS – the people who perform KIBS. Companies offering such services search for well‐educated and experienced employees (Pardos, Gomex‐Loscos and Rubiera‐Morollon, 2007; Czarnitzki and Spielkamp, 2000). The human aspects of KIBS have therefore engaged the attention of researchers. Sample topics here are: skills requirements and associated educational levels, HR practices, interpersonal relations, the role of entrepreneurs and social capital, and ‘knowledge angels’. There are also some other areas which have not been classified and explained here, such as the sources of the competitiveness of KIBS and their productivity. All the above‐mentioned areas together with articles covering them are collected in the table below. Table 3: Research areas on KIBS Research area
Authors KIBS and their innovativeness innovativeness of KIBS themselves (Czarnitzki and Spielkamp, 2000) innovativeness of customers they serve (Aarikka‐Stenroos and Jaakkola, 2012); (Yam, 2011); (Aslesen and Isaksen, 2007); (Toivonen, 2004) innovation patterns and types (Amara, Landry and Doloreux, 2009), (den Hertog, 2000); (Freel, 2006); (Corrocher, Cusmano and Morrison, 2009) KIBS and their interactions with the environment sectoral growth (Evangelista, 2012); spacial proximity (Doloreux, Freel and Shearmur, 2010); (Koch and Stahlecker, 2006) regional innovation systems (Thomi and Bohn, 2003); (Stahlecker and Koch, 2004); (Koch and Stahlecker, 2006); (Bishop, 2007) clustering (Shearmur and Doloreux, 2012) Knowledge and the related processes R&D activities (Hipp, 1999) knowledge management (Andreeva and Kianto, 2011) knowledge absorptive capacity (Tseng, Pai and Hung, 2011); (Koch and Strotman, 2008) knowledge spillovers (Bishop, 2007); (Fernandes and Ferreira, 2011) expert knowledge (Aslesen and Isaksen, 2007) Human aspects of KIBS skills requirements/associated educational levels (Consoli and Elche‐Hortelano, 2010) role of entrepreneur and social capital (Gianecchini and Gubitta, 2012) knowledge angels (Muller and Doloreux, 2009); (Muller, Zenker and Ramos, 2012) Other areas sources of KIBS competitiveness (Corrocher, Cusmano and Morrison, 2012) KIBS productivity (Musolesi and Huiban, 2010)
Source: Own, based on literature review. It can be concluded that research on KIBS has been evolving from that based simply on identifying such services and highlighting their innovativeness to an in‐depth examination of the myriad factors connected with them, their interactions with other companies, and with the environment. It is also worth mentioning that many studies on KIBS are devoted not to a single area from those mentioned above, but interlink two or three of them and aim to identify the correlations between them. Two examples are the study of the relationship between the sources of knowledge, innovation and productivity by Musolesi and Huiban (2010) and that on expert knowledge as an input in innovation processes by Aslesen and Isaksen (2007).
5. Conclusion The importance of the knowledge‐intensive business services sector for the development of the economy is growing. In the 80s and 90s this sector became the fastest growing sector in the OECD countries (OECD, 2001). The development of KIBS in recent decades can be interpreted as one of the indicators of a transformation from industrial economies into knowledge‐based ones. Quantitative measures, in the form of sales or employment figures (e.g. Chadwick, Glasson and Lawton Smith, 2008), undoubtedly show the expansion of
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Malgorzata Zieba these services; but their characteristics also make it clear that they significantly affect the formation and spread of knowledge throughout the economy. This is also confirmed by the literature presented in this article. On the basis of its analysis, the following phases of research on KIBS have been identified:
Phase one – identification and selection of KIBS & the theoretical aspects of these services (e.g. Miles et al., 1995; Hipp, 1999).
Phase two – uniqueness of KIBS, e.g. cooperation with clients (den Hertog, 2000; Czarnitzki and Spielkamp, 2003), innovation patterns (Freel, 2006), influence on innovation systems (Muller and Zenker, 2001; Thomi and Bohn, 2003; Baláž, 2004; Wood, 2006).
Phase three – in‐depth analysis of factors & determinants of KIBS & multiple analysis of a combination of these factors (Aarikka‐Stenroos and Jaakkola, 2012; Castaldi et al., 2010; Consoli and Elche‐Hortelano, 2010; Tseng at al., 2011; Evangelista et al., 2012).
As far as the topics of articles on KIBS are concerned, these have evolved along the phases mentioned above. First the KIBS sector was identified in the literature. Then its influence on the innovativeness of companies, sectors, regions and nations was determined. A broad area of KIBS examination is connected with their interactions with their clients, knowledge exchange and spillovers, absorptive capacity and knowledge management. A relatively new research area is devoted to human resources management in such companies. To conclude, knowledge‐intensive business services are examined on micro and macro scale. In the literature one can find articles on their inside characteristics (e.g. innovativeness, management etc.), as well as on their interactions with the outside environment and their influence. Despite all the above, the topic of KIBS still remains underexplored.
Acknowledgements This literature review was performed within the research project “Knowledge management in small and medium enterprises (SMEs) offering knowledge intensive business services”. The project was funded by the National Science Centre on the basis of Decision No. DEC/2011/01/D/HS4/04111.
References Aarikka‐Stenroos, L. & Jaakkola, E. (2012) “Value co‐creation in knowledge intensive business services: A dyadic perspective on the joint problem solving process”, Industrial Marketing Management, Vol. 41, No. 1, pp. 15–26. Amara, N., Landry, R. & Doloreux, D. (2009) “Patterns of innovation in knowledge‐intensive business service”s, The Service Industries Journal, Vol. 29, No.4, pp.407–430. Andreeva, T. & Kianto, A. (2011) “Knowledge processes, knowledge‐intensity and innovation: a moderated mediation analysis”, Journal of Knowledge Management, Vol. 15, No.6, pp. 1016–1034. Aslesen, H.‐W.; Isaksen, A. (2007) “New Perspectives on knowledge‐intensive services and innovation, Geografiska Annaler: Series B, Human Geography”, Vol. 89, pp. 45–58. Baláž, V. (2004), “Knowledge‐intensive business services in transition economies”, The Service Industries Journal, Vol. 24, No. 4, pp. 83–100. Castaldi, C., Faber, J. and Kishna, M. (2010) “Co‐innovation by KIBS in Environmental Services: A Resource‐based View”, No 10‐05, Eindhoven Center for Innovation Studies (ECIS) working paper series, Eindhoven Center for Innovation Studies (ECIS), http://EconPapers.repec.org/RePEc:dgr:tuecis:wpaper:1005. Chadwick, A., Glasson, J. and Lawton Smith, H. (2008) “Employment Growth in Knowledge‐Intensive Business Services in Great Britain during the 1990s ‐ Variations at the Regional and Sub‐Regional Level”, Local Economy, Vol. 23, No. 1, pp. 6‐18. Consoli, D., & Elche‐Hortelano, D. (2010) “Variety in the knowledge base of Knowledge Intensive Business Services”, Research Policy, Vol. 39, No. 10, pp. 1303–1310. Corrocher, N., Cusmano, L. and Morrison, A. (2009) “Modes of innovation in knowledge‐intensive business services evidence from Lombardy”, Journal of Evolutionary Economics, Vol. 19, pp. 173‐196. Corrocher, N., Cusmano, L. and Morrison, A. (2012) “Competitive strategies in knowledge‐intensive business services: evidence from Lombardy”, in: Exploring knowledge‐intensive business services: knowledge management strategies, Basingstoke, Palgrave Macmillan, pp. 120‐136. Czarnitzki, D. and Spielkamp, A. (2003) “Business services in Germany: bridges for innovation”, The Service Industries Journal, vol. 23 (2), pp. 1‐30. den Hertog, P. (2000) “Knowledge‐intensive business services as co‐producers of innovation”, International Journal of Innovation Management, Vol. 4, No. 4, pp. 491–528. Doloreux, D., Freel, M. and Shearmur, R. (2010) “Knowledge‐Intensive Business Services: Geography and Innovation”, Farnham: Ashgate.
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Malgorzata Zieba Evangelista, R., Lucchese, M. & Meliciani, V. (2012) “Business services, innovation and sectoral growth”, Structural Change and Economic Dynamics. doi:10.1016/j.strueco.2012.02.005 Fernandes, C. & Ferreira, J. (2011) “Knowledge Spillovers and Knowledge Intensive Business Services: An Empirical Study”, MPRA Paper 34751, University Library of Munich, Germany. Freel, M. (2006) “Patterns of Technological Innovation in Knowledge‐Intensive Business Services”, Industry and Innovation, Vol. 13, Iss. 3, pp. 335‐358. Gianecchini, M. & Gubitta, P. (2012), “The role of entrepreneur’s human and social capital in knowledge‐intensive business services”, in: Exploring knowledge‐intensive business services: knowledge management strategies, Basingstoke, Palgrave Macmillan, ISBN 0230358594, pp. 120‐136. Hipp, Ch. (1999) “Knowledge‐intensive business services in the new mode of knowledge production”, AI & Society, No. 13, pp.88‐106. Koch, A. & Strotmann, H. (2008) “Absorptive capacity and innovation in the knowledge‐intensive business service sector”, Economics of Innovation and New Technology, Vol. 17, No. 6, pp. 511‐531. Koch, A., Stahlecker, T. (2006) “Regional innovation systems and the foundation of knowledge intensive business services. A comparative study in Bremen, Munich, and Stuttgart, Germany”, European Planning Studies, Vol. 14, Iss. 2, pp. 123‐ 145. Miles, I. (2005) “Knowledge intensive business services: prospects and policies. Foresight”, Journal of Future Studies, Strategic Thinking and Policy, Vol.7, No. 6, pp. 39‐63. Miles, I., Kastrinos, N., Flanagan, K., Bilderbeek, R., Den Hertog, P., Hutink, W., Bouman, M. (1995) Knowledge intensive business services: their roles as users, carriers and sources of innovation, PREST, Manchester Muller, E. and Doloreux, D. (2009) “What we should know about knowledge‐intensive business services”, Technology in Society, Vol. 31, Iss. 1, pp. 64‐72. Muller, E. and Zenker, A. (2001) “Business services as actors of knowledge transformation: the role of KIBS in regional and national innovation systems”, Research Policy, Vol.30, pp. 1501‐1516. Muller, E., Zenker, A. (2001a) “Business services as actors of knowledge transformation and diffusion: some empirical findings on the role of KIBS in regional and national innovation systems”, Working Papers Firm and Region, Karlsruhe, no. R2. Muller, E., Zenker, A., Héraud, J.‐A. (2009) “Entering the KIBS' black box. there must be an angel! (or is there something like a knowledge angel?)”, Arbeitspapiere "Unternehmen und Region", t. 72009, available at: http://nbn‐ resolving.de/urn:nbn:de:0011‐n‐1021704; http://publica.fraunhofer.de/documents/N‐102170.html. Musolesi, A., Huiban, J.‐P. (2010) “Innovation and productivity in knowledge intensive business services”, Journal of Productivity Analysis, Vol. 34(1), pp. 63‐81. OECD Science (2001), Technology and Industry Scoreboard. Towards a knowledge‐based economy, Paris. Pardos, E., Gomex‐Loscos, A. and Rubiera‐Morollon, F. (2007) “’Do versus buy’ decisions in the demand for knowledge intensive business services”, The Service Industries Journal, Vol. 27, No. 3, pp. 233‐249. Shearmur, R., Doloreux, D. (2012) “Is there a connection between geographic clustering and KIBS innovation?”, in: Exploring knowledge‐intensive business services: knowledge management strategies, Basingstoke, Palgrave Macmillan, ISBN 0230358594, p. 120‐136. Stahlecker, T. and Koch, A. (2004) “On the significance of economic structure and regional innovation systems for the foundation of knowledge‐intensive business services”, Arbeitspapiere Unternehmen und Region, No. R1/2004. Strotmann, H. & Koch, A. (2006) “The Impact of Functional Integration and Spatial Proximity on the Post‐entry Performance of Knowledge Intensive Business Service Firms”, International Small Business Journal, Vol. 24, No. 6, pp. 610‐634. Thomi, W., Bohn, T. (2003) “Knowledge Intensive Business Services in Regional Systems of Innovation ‐ Initial Results from the Case of Southeast‐Finland”, ERSA conference papers, European Regional Science Association. Toivonen, M. (2004) “Foresight in services: possibilities and special challenges”, The Service Industries Journal, Vol. 24, No. 1, pp. 79‐98. Tomlinson, M. (1999) “The learning economy and embodied knowledge flows in Great Britain”, Journal of Evolutionary Economics, Vol. 9, No. 4, pp. 431‐451. Tseng, Ch.‐Y., Pai, D. Ch., Hung, Ch.‐H. (2011) “Knowledge absorptive capacity and innovation performance in KIBS”, Journal of Knowledge Management, Vol. 15, No. 6, pp. 971–983. Wood, P. (2006) “Urban Development and Knowledge‐Intensive Business Services: Too Many Unanswered Questions?”, Growth and Change, Vol.37, No. 3, pp.335‐361. Yam, R. C., Lo, W., Tang, E. P., & Lau, A. K. (2011) “Analysis of sources of innovation, technological innovation capabilities, and performance: An empirical study of Hong Kong manufacturing industries”, Research Policy, Vol. 40, No. 3, pp. 391–402.
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Characteristics of Decision Problems In Innovation Process Planning Magdalena Jurczyk – Bunkowska Opole University of Technology, Opole, Poland m.jurczyk@po.opole.pl
Abstract: The article presents the issue of innovation process planning. The discussed problem concerns identifying decisions which may be supported by DSS (Decision Support Systems). The paper discusses the original model of innovation process planning that assumes three hierarchical levels of making decisions. This enables to constraint the decisive field, not to break the creative nature of innovation processes, to coordinate actions and to direct them in accordance with the company’s strategy. The research on elaborating the innovation process planning model was conducted in 20 Polish enterprises selected from the innovation leaders. All the data was collected basing on the semistructured interviews with managers strictly engaged in the projects. Indicating different levels of innovation leads to classifying the decisions related to innovation process planning. The applied methods and tools supporting the managerial decisions have to comply with the problem’s issue. The use of identical approach in managing processes that lead to creating both incremental and radical innovation would be unreasonable. However the distinction between innovation processes concerning novelty degree of their effects is not sufficient enough. The importance of the process itself should also be considered and is related to its expected influence on the company’s development. The bigger the importance the better is the identification of innovation process connections with the company’s functioning system. Nevertheless it is related to the increase of risk and uncertainty. The knowledge gap was defined as a parameter characterizing innovation processes. It describes the difference between knowledge that an enterprise has before starting any innovation process and knowledge that has to be created and implemented during the innovation process. A fuzzy linguistic evaluation system of this parameter was elaborated together with innovation process decision map. Understanding the context of managerial decision-making is important because it defines both the potential for and the limits to decision support The map was created basing on matching decisions to proper innovation process model levels and on evaluating knowledge gap. It enabled indicating those decisions which main manager should focus on. It involves the problems of delegating authorities and defining proper inputs (including time) required for analysis that precedes decision-making. Keywords: innovation process, planning, decision-making, uncertainty, classification, fuzzy logic
1. Introduction Planning is one of the most important manager's function. Plans are used to set directions, to reduce uncertainty, to minimize waste and redundancy, to establish goals or standards applied in controlling. Properly elaborated plans are goal-oriented and possible to complete, they therefore constitute the basis for efficient process realization. Can one plan processes of creative nature? This is the dilemma in the context of innovation processes. The issue of disorder in innovation processes is often risen, especially in early phases, and uncertainty connected with goal formation (Mumford, 2008). It is colloquially said that innovation processes are like a journey into the unknown. However it is not a lonely journey of an innovator working in his laboratory, it is rather a complex set of actions where many people are involved from both inside and outside of an organization. Therefore achieving the desired results requires management, thus planning. According to observations, managers are aware of the fact that plans are used to navigate rather than to set paths. Hence they use other approach in planning, different from the rest of business processes. The article does not emphasize the description of the model but the characteristics and classification of decisions related to innovation process planning. It leads to separating those decisions which are structured well enough to be programmed. Even though innovation processes are avoided due to their nature, there are planning decisions which are more typical than others, for instance the choice of laboratory for the tests or the manner for new product market research. In these types of decisions the combination of alternatives as well as the criteria of choice are known and the decision can be made almost automatically. When programming, the manager can delegate this kind of decision to lower levels in order to gain time for solving problems in unusual situations, where the decision requires thorough judgment basing on the gathered information. The questions considered in the article are formulated as follows:
How to separate those innovation process management decisions, where it is reasonable to create DSS (Decision Support System)?
What meaning will the above-mentioned classification have in terms of innovation process planning?
The paper is organized in the following sections. The next chapter presents the background for discussions conducted in the article and shortly describes the problem of innovation process management. Chapter 3
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Magdalena Jurczyk – Bunkowska presents the methodology of conducted research. Chapter 4 illustrates the effect of the research which is the elaborated model of innovation process planning. The last chapter concentrates on answering the abovementioned questions. Knowledge gap accompanying innovation processes was evaluated basing on the Mamdani-type fuzzy inference system and the example of calculating such evaluation of this parameter was presented. Finally decision planning map was elaborated which enables classification of decisions in terms of their vulnerability to programming. Conclusions relate to the meaning of decision programming in innovation process management.
2. Innovation process management Innovations are the only factors which guarantee the survival on the dynamically and turbulently changing market. Managers pay a lot of attention and effort in order to create systems in which innovation processes can be efficiently conducted. Decision Support System should be the part of such systems. Organizations have to pick up signals about changes in internal and external environment and then transform them into information necessary for innovation process management (Utterback, 1994). Innovation process is used to encompass the whole process, from the idea generation to the commercialization. They consist of transition from loosely set goals and assumptions, through their realization and into elaborating a form possible to implement and diffuse (Van de Ven, 1989). It requires proper management structures and procedures for limited resources within the phases of process realization progress. The problem illustrated in the following article requires the perception on innovation as a process. It was assumed that this process is conducted in three phases: 'front-end', development and commercialization. Each phase is characterized by different aims, requires time and resources in order to be completed (Koen, 2001). The purpose of innovation process is to compete and differentiate on the market in a successful mode (Baregheh et al., 2009). Despite the creative nature of innovation process, it should be managed. As Drucker (1998) noted “innovation can be systematically managed if one knows where and how to look”. Innovation management is the invention and implementation of management practices, structures, processes which are novel and may assist business organizations to effectively attain their goals (Birkinshaw et al., 2008). Innovation management encompasses all the key areas that need to be mastered to develop successful products and services, efficiently and continuously. The capacity of a firm to implement innovation management revolves around its success in dealing with these two main challenges: top-line growth and bottom-line efficiency (Liyanage and Poon, 2002). The research described in the next chapter intended to recognize the reality in terms of innovation process planning. The chapter also carries the answer to the question of how innovation processes are planned and if it is possible to generalize the applied practices by building innovation process planning model.
3. Research methodology The researched problem demanded open and interactive observations thus direct interview was chosen as a research method. During the research innovation processes were analyzed in 26 enterprises of different sizes and branches. Their common trait was being in the lead of Polish innovation rankings. Several innovation processes were analyzed in every enterprise due to the research hypothesis. Only successful processes were analyzed in terms of management practices. The idea was to identify the so-called “good practices” in innovation process planning. The interviews were preceded by preliminary research. It consisted not only of literature analysis but also of detailed analysis of several innovation processes in a medium-sized IT company. It led to elaboration of a questionnaire consisting of open questions concerning the discussed innovation processes. On the one hand this tool enabled casual conversation within certain limits, and on the other hand data gathering and arrangement. In most cases the interviews were conducted with a number of people directly responsible for the analyzed innovation processes. Interviews were of variable length but with a mean of approximately 2 hours. Every interview was previously settled also in the context of discussed problematic aspects and research goals. The conclusions were prepared within a few days in a form of notes and then sent to verification. In primary assumptions, all the data from the interviews was supposed to be supplemented with data from written procedures or documents. This however occurred to be impossible in most cases because companies did not store information about innovation process management.
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4. Innovation process planning model In all the enterprises that took part in the research, innovation processes were planned, however the scope of planning and degree of formalizing were different. The basis for every innovation process is the necessity. Created plans were supposed to guarantee its fulfillment within the limits of the entrepreneurâ&#x20AC;&#x2122;s possibilities. The question 'is it worth to search for an innovative solution in this particular case?' was the first problem of planning. Only positive answer to the previous question activated further stages of innovation process planning.
4.1 Gradual itemization of innovation process plans Innovation process planning happens by stages (Jurczyk-Bunkowska, 2012). After gathering enough knowledge about solving a problem, one can formulate plans for further phases and actions (figure 1).
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While standing in spot A, that is before commencing any innovation process, one can merely see its scope basing on the commonly formulated aim. Therefore only process limits can be planned. After this one can continue to spot B, where issues concerning first phase plans are considered. Next steps of formulating innovation process plans happen analogically. Decisions related to general issues of phase realization are the basis for itemizing action plans- moving on to spot C. While going further down the path it can be observed that the effects from the first phase give knowledge enabling planning the limits of the next phase - spot D perspective. Together with the work progress, innovation process goal is becoming visible. The area of possibilities is getting narrower with the knowledge created in the following process stages. Therefore it can be stated that planning happens iteratively during innovation process realization.
4.2 The essence of innovation process planning model The suggested innovation process planning model bases on the structure of innovation process which distinguishes phases and actions. A phase consists of a set of logically related actions leading to achieving certain effect. Innovation process phases happen one after another in particular order. Their number and scope depend on the assumed model (Eveleens, 2010). Phases enable to arrange the process by setting intermediate effects. The essence of the model presented on figure 2 is gradual innovation process planning.
Figure 2: The model of innovation process planning The need to compare present and forecasting state of the environment is the reference for all decisions. Planning takes place in three layers; every layer identifies and balances constraints and requirements.
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Magdalena Jurczyk â&#x20AC;&#x201C; Bunkowska Constraints describe the entrepreneurâ&#x20AC;&#x2122;s possibilities whereas requirements refer to expected effects in the context of the need. It can be stated that requirements constitute quality constraints of a given innovation process fragment. Within these set assumptions the admissible plan in constructed. The frames of the whole process are planned during the first layer and they are the basis for planning process phases. Phase plans determine action plans. Planning is gradual therefore the plan of the next phase is always constructed after finishing the previous phase. Action details are planned only after making the plan of the phase in which they are conducted. Their elaboration demands access to knowledge about constraints and requirements towards particular action.
5. Characteristics of decisions in innovation process planning The meaning of decisions was presented in previous sections. They were compared with each other in the context of the suggested model of innovation process planning. In reality, however, several innovation processes are conducted simultaneously. The manager has to decide about his engagement in particular decisions related to different innovation processes. Which ones require his bigger attention?
5.1 Knowledge gap in innovation processes While characterizing planning decisions, it is not sufficient to only refer to the model's layer. The peculiarity of the process itself is also important. It is intuitively known that there are processes more and less complicated in terms of management. The differences result from the gap in knowledge that enterprise possesses while commencing a process and knowledge that is necessary to its completion. This gap is described by the uniqueness of knowledge which an enterprise has to gain and scope to implement it. Therefore it can be observed that knowledge gap is gradable, it is illustrated in figure 3. scope of creating and implementing the 'new knowledge' in the enterprise the whole system process activity improvement novelty in the sector
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Figure 3: Knowledge gap size depending on innovation process novelty degree and new knowledge implementation scope The size of knowledge gap depends on two factors and needs to be filled with the help of innovation process. The factors can be defined as innovation process novelty and its scope within the perspective of enterprise's functional space. The first parameter refers thus to knowledge uniqueness, whereas the second concerns the amount of cooperating units which create and implement new knowledge. As an example let's take the innovation process scope whose effect is machine upgrade. Such scope is smaller than the one whose effect is the change of distribution manner. Figure 3 represents the relation between the gap size and its factors in a form of a well which needs to be filled with completely new knowledge. Innovation process which concerns small scope changes in the enterprise's scale and is characterized by low novelty degree, will require little implementation of knowledge. The well's diameter and depth will consequently be little. Nevertheless in case of a bigger scope solution, with a higher novelty degree, the well will be deeper and knowledge gap bigger. The scope of changes and the novelty degree of the process are subjective and evaluative figures. The proximate character of knowledge gap evaluation was the reason for choosing fuzzy inference method basing on fuzzy logic (Zadeh, 1975).
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5.2 The use of fuzzy inference system to evaluate knowledge gap size Evaluation of knowledge gap size has an important meaning for the manager in terms of process management. Efficient planning of resource management in innovation process can be possible after setting the size of the gap. Time is a significant resource together with expenditure and personal involvement in the realization of decisive processes. The following linguistic variables were used to evaluate knowledge gap: n - assessment of novelty, s - assessment of scope, g - assessment of knowledge gap. A linguistic variable is decomposed into a set of linguistic terms L: L(n) = {N1, N2, N3} = {low, average, high}, L(s) = {S1, S2, S3} = {narrow, medium, wide} L(g) = {G1, G2, G3, G4, G5,} = {tiny, small, medium, big, huge} The evaluation of novelty and scope of change is established basing on the expert's knowledge concerning particular enterprise. All the variables describing the novelty and scope of a given process are presented in scale 0-10, which constitutes the variable's space. Exemplary evaluations are shown in tab 1 and tab 2. They represent differences between such values as: “improvement”, “national scale novelty”, “branch scale novelty”. As it was mentioned before, this evaluation is subjective therefore it will be related with the specificity of an enterprise. The same innovation in a small company of low innovative culture, for instance, will require overcoming bigger knowledge gap than in a medium company frequently implementing innovations. Table 1: The example quantifying the novelty of innovation process score (n)
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Table 2: The example quantifying the scope of innovation process
Characteristics of the process improvement advanced improvement the novelty in the enterprise scale
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the novelty in the region scale the novelty in the country scale the novelty in the market scale the novelty in the sector scale radical innovation
Characteristics of the process single task activity (a group of logically related tasks) a set of logically related activities department process a set of related processes the whole system
Innovation process scope may also be treated as a percentage definition of enterprise's part which will cooperate during the creation and implementation of new knowledge. The points of evaluation constitute a crisp set of input data and is later transformed into membership degree of linguistic variables n and s to fuzzy sets. Figure 4 presents the system's data base containing definitions of fuzzy sets. Functions μ(n) and μ(s) were elaborated during the consultation in 4 of the researched enterprises, 2 small companies and 2 medium. μn 1
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Magdalena Jurczyk – Bunkowska The typical fuzzy model consists of three units: fuzzification unit, inference unit and defuzzification unit. The discussed case uses model type 2IN/1OU. The module consists of rules, linguistic variable's membership function and inference mechanism. The following rules were applied in the inference module of knowledge gap evaluation: R1: if (n= N1) and (s = S1) then (g = G1), R2: if (n = N1) and (s = S2) then (g = G2), R3: if (n = N2) and (s = S1) then (g = G2), R4: if (n = N2) and (s = S2) then (g = G3), R5: if (n = N3) and (s = S1) then (g = G3), R6: if (n = N1) and (s = S3) then (g = G3), R7: if (n = N2) and (s = S3) then (g = G4), R8: if (n = N3) and (s = S2) then (g = G4), R9: if (n = N3) and (s = S3) then (g = G5). Membership functions of fuzzy result g were created through a division of numerical space of result g into four separate sections. It enabled to formulate five triangular fuzzy sets (figure 5). This time knowledge from the enterprise was not used to build the function.
Figure 5: Fuzzy sets of linguistic variable g Inference mechanism of the elaborated fuzzy model comes down to following the three steps described below. 1. Calculation of rules strength (R1,..,R9). Membership degree to particular fuzzy set is assigned for every variable within the rule premises and it fits in the range [0,1]. If the rule strength is zero then the rule is considered inactivated. PROD operator was used to establish the degree of rule strength due to its good reaction to changes in model's inputs. Determination of premises' degree hr (r=1,..,9): h1=PROD [μN1(n*), μS1(s*)] = μN1(n*) μS1(s*) h2= PROD [μN1(n*), μS2(s*)] = μN1(n*) μS2(s*) h8= PROD [μN3(n*), μS2(s*)] = μN3(n*) μS2(s*) h9= PROD [μN3(n*), μS3(s*)] = μN3(n*) μS3(s*) where n*, s* are values of model's input sizes. 2. The degree of premises fulfillment (h) is the basis for establishing the fuzzy set μGx(g), which is the result of activating the rule. This operation can be conducted only for activated rules. MIN operator is used to create * modified membership functions μGx (g). μG1̇*(g) = MIN(h1, μG1̇(g)),
h1 > 0,
μG5*(g) = MIN(h9, μG5 (g)),
h9 > 0.
3. Aggregation of active rules and creation of result membership function μres(g) consist of adding up fuzzy * result sets of all the rules μGx (g).
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Magdalena Jurczyk – Bunkowska Result function becomes the basis for calculating the value of model's output index g. Thus sharp input values also gain sharp result value on the output. In order to defuse the height method was applied. In the method the fuzzy set of result variable is being replaced by a singleton placed in the modal value of this set (figure 6). μg 1
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Figure 6: Replacement of variable's g fuzzy sets by singleton sets The modified center of gravity method (COG) is used to obtain the final result (defuzzyfication):
Where: designations, as previously.
5.3 The example of setting knowledge gap in an innovation process Let's assume that an entrepreneur is willing to find an innovative solution in terms of products distribution. Due to big similarity of his products, he decided that the change should distinguish his company and determine his competitive advantage. These decisions helped him rate the novelty degree of n=6 and scope s=8 (tab.1 and tab.2). In order to evaluate the involvement degree in decisions related to this innovation process management, knowledge gap will be estimated with the use of fuzzy inference as it is presented on figure 7. crisp value
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Figure 7: The structure of fuzzy linguistic system for evaluating knowledge gap in the innovation process Fuzzification values parameters n=6 and s=8. μnovelty low (n)= μN1(n)=0 μnovelty average (n)= μN2(n) = 0,75 μnovelty high (n)= μN2(n) = 0,25
μscope narrow (s)= μS1(s) = 0 μscope medium (s)= μS2(s) = 0,4 μscope wide (s)= μS3(s) = 0,6
Activation of rules and determination of premises fulfillment degree (rules strength) : h4=PROD [μN2(6), μS2(8)]= 0,75 0,4 = 0,3 (G3) h7=PROD [μN2(6), μS3(8)]= 0,75 0,6 = 0,45 (G4) h8=PROD [μN3(6), μS2(8)]= 0,25 0,4 = 0,1 (G4) h9=PROD [μN3(6), μS3(8)]= 0,25 0,6 = 0,15 (G5) Result functin μres(g)was obtained due to the conclusion of all the rules and was presented on figure 8 by a bold line. In order to get sharp value describing the knowledge gap, height method was applied. The final result of defuzzification procedure was obtained by the following transformation:
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Îźg
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Figure 8: Îźres(g) function as a conclusion of activated rules It can thus be stated that knowledge gap related with this process will be rather significant. However even in such unique processes decisions on lower planning layers are characterized by certain similarity. Therefore decision planning map of innovation processes was suggested so as to evaluate particular planning decisions. The map includes not only the knowledge gap but also planning layer where the decision is made. The map indicates possibilities of programming decisions in innovation process planning.
5.4 Classification of innovation processes considering their knowledge gap Management as well as managerial decisions on the one hand require accurate analysis of chances and threats causing changes in environment. On the other hand these type of analyses are expensive and the decisive process itself involves manager's time. In innovation management, as well as in case of other business processes, managers do not make all of their decisions as part of a deliberate, coherent, and continuous decision-making process. Innovation process planning decisions may be structured, semi-structured or illstructured. The use of identical approach in managing processes that lead to creating both incremental and radical innovation would be unreasonable. Managers are willing to control many innovation processes at the same time, they have to delegate their authorities to a certain extent. Understanding the context of managerial decision-making is important because it defines both the potential for and the limits to decision support. Even though knowledge share and gathering is the basis for innovation process realization, the practices of the researched companies indicate the difficulty of achieving this effect. The reluctance occurs in both the innovation process managers and organization's employees. In the first case the reluctance is related to frequently high financial expenditures for gathering knowledge. Managers worry about their evaluation as inadequate towards the needs. Employees, on the other hand, are reluctant to share knowledge. It results not only from their fear of losing position, but also from being unaware of the knowledge meaning. In both cases the manager has the tools to encourage sharing of tacit and hidden knowledge. Their use however is justified only in case of a part of decisive problems. The classification suggested below aims at elaborating coherent and rational, from the organization view, approach towards involvement of means in knowledge gathering for particular decision category.
the layer of planning
Classification of decisions in innovation process planning regarding their abilities to be programmed (structured) is also the second aim of the elaborated map (figure 8).
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Magdalena Jurczyk – Bunkowska Figure 8: Decision map in innovation processes with the classification according to their structural criteria The characteristics of particular categories were agreed during conducted research in the enterprises. The division of categories depends on the culture of an organization and its experience with innovation processes. It was also observed that the division is related to management style in the enterprise. Four decision categories were distinguished on the map.
Repeatable and easily programmable decisions. They concern the formulation of constraints and requirements for single actions in innovation processes. Such cases in processes with small knowledge gap do not require the necessity of “starting over”. This decision category does not require much involvement in knowledge gathering. One can base on the explicit knowledge of the enterprise.
Frequently made decisions. This category includes planning decisions made so often, that standard procedures are elaborated which describe the course of decisive processes. In case of this category, the decision maker will be basing on explicit knowledge, however the scope of its gathering will be wider. The knowledge of the organization will need to be supplemented by the outside knowledge.
Decisions difficult to program. They refer to new problems where there are no set procedures. This category contains decisions whose nature and structure are unknown, however they are little complicated and fragments of decisive processes can be programmed. This decision category requires the use of hidden inner and outer knowledge, nevertheless it should be limited according to the budget, time and scope.
Decisions that cannot be programmed. They concern new problems of big complexity and complication degree. These decisions refer to very important issues therefore they deserve a special individual approach. This category consists of decisions determining the success of the most crucial innovation processes realized in the enterprise. Their financial consequences are significant. Making such decisions requires all possible attempts to gather the needed knowledge. It includes nonstandard procedures to encourage tacit knowledge share from the inside and outside of the organization.
All decisions from the mentioned categories are made applying various techniques. For the first and second category these are: routine, standard procedures, operative research, models, computer simulations, and other techniques using electronic data processing. Third category decisions are made basing on evaluation and intuition supported by experience and expert knowledge. Fourth category decisions require considerable expenses for gathering information, they are made collectively often supported by analysis and outside experts.
6. Conclusions Innovation processes are accompanied by ever larger time pressure. On the other hand their realization is more often related to wide cooperation between various internal and external organization units. That is why coordination gains bigger meaning in innovation process management, it applies to actions, knowledge flow and decision-making process. Some decisions made too late become aimless. Planning is essential for coordination. One cannot assume that successful innovation process can be achieved only by stimulating creativity. Considering the above-mentioned aspects, satisfying effects cannot be expected depending only on traditional management techniques. Even though innovation processes are characterized by high uncertainty, DSS must be created and used commonly. This is the aim of the suggested classification of planning decisions presented in the article. Uncertainty in innovation process planning results from the dilemma what will be the effects of a particular planning decision and what has to be known to make it efficiently. Lack of information from the past is the problem which prevents making the probability evaluation of particular situation. Nevertheless in terms of some planning decisions, there exist archive data which can easily support current decisions. The lack of such knowledge archiving system was identified in all researched enterprises. The system could store knowledge concerning the already completed innovation processes and support managing the current ones. It proved, from the observation of successful innovation process, that management is the key element. The interviewed managers indicated that success mostly depends on the involvement of the supervisor who owes his results to proper knowledge. The knowledge, however, remains almost exclusively in his directive and has small chances of being disseminated by DSS. These types of systems facilitated also the allocation of limited resources in case of several simultaneously conducted processes.
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Magdalena Jurczyk – Bunkowska The suggested classification not only indicates the decisions vulnerable to programming, but also distinguishes exceptional decisions. Unburdening the supervisor from programmable decisive problems increased his engagement in more important issues of innovation process planning. To conclude, it is possible to learn with the current innovation process structure only with the support of systems basing on electronic data processing. The suggested decision classification together with previous work related to assumptions and scope of such systems, mind the achievement of this aim.
Acknowledgement The project funded by the National Science Center in the scope of the research project No. 4025/B/H03/2011/40 Fri: "Developing a model of planning and estimating the cost of innovation"
References Baregheh, A., Rowley, J. and Sambrook, S. (2009) "Towards a multidisciplinary definition of innovation", Management Decision, Vol.47, No.8, pp.1323–1339. Birkinshaw, J., Hamel, G. and Mol, M. J. (2008) "Management innovation", Academy of Management Review, Vol.33 No.4, pp.825-845. Drucker, P. F. (1998) The discipline of innovation. Harvard Business Review. Eveleens, C, (2010) Innovation management; a literature review of innovation process models and their implications. Nijmegen, NL, 1-16. Jurczyk-Bunkowska, M. (2012) "Model of innovation process planning based on research of polish enterprises", Proceedings of the 13th International CINet Conference, Roma, Italy, pp. 616 – 627. Koen P. and Ajamian G. et al. (2001) "Providing clarity and a common language to the 'fuzzy front end'", Research Technology Management, Vol. 44, pp. 46-55. Liyanage, S. and Poon, P.S. (2002) "Technology and innovation management learning in the knowledge economy", The Journal of Management Development, Vol.22, No.7/8, pp.579-602. Mumford, M. D., Bedell-Avers, K. E. and Hunter, S. T. (2008) "Planning for innovation: A multi-level perspective", [in:] Mumford M. et al Multi-Level Issues in Creativity and Innovation, Research in Multi Level Issues, Vol. 7, Emerald Group Publishing Limited, pp. 107-154. Utterback, J. (1994) Mastering the Dynamics of Innovation. Boston, MA.: Harvard Business School Press. Van de Ven, A., Angle, H. and Poole, M. (1989) Research on the Management of Innovation. New York: Harper and Row. Zadeh L. (1975) "The Concept of a Linguistic Variable and its Application to Approximate Reasoning-I" Information Sciences, No.8, American Elsevier Publishing Company, Inc., pp. 199-249.
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Can Knowledge be Reliably Measured? Rumniak Paweł University of Economics, Wroclaw, Poland pawel.rumniak@ue.wroc.pl
Abstract: Modern companies do not rely only on tangible assets but most of all on intangible ones, which are not necessarily presented in financial reports. Depending on the point of view the intangibles may be perceived individually or collectively. The knowledge is a conglomerate of intangibles which cooperate for better effectiveness of an entity. The knowledge, as an intangible asset, plays one of the most important role for each company and is regarded to be a factor which influences people’s creativity and as a product itself. It is that kind of resource, which is considered as imperfectly imitable, hard to substitute, rare and valuable. The strategists say that knowledge is the most valuable asset which provides sustainable competitive advantage to an entity. Sometimes, it is confused with an intellectual capital or restricted to a company’s know-how, what proves that defining knowledge asset and spreading around that definition will let the scholars speak the same language or at least take part in an international dispute on the role of knowledge assets. Moreover, defining these phenomena is also very important in classifying elements from which knowledge consists of. The taxonomy of knowledge helps the managers to manage the knowledge more effectively. Furthermore, the knowledge is subjected to the complex management, what influences its constant growth and complexity measure of relationship into which it is encompassed – and therefore the taxonomy keeps changing. However, the accountants doubt if the knowledge should be considered as an asset and are unable to disclose any information concerning the knowledge possessed or acquired during this sort of management. Their doubts are taken directly from (paradoxically) the lack of knowledge and generally accepted methods that can be used to measure value or report possessed knowledge concerning the entity. On the other hand, it is difficult to manage the knowledge, since we do not know how to measure or even express it. Is this measurement really needed? Do we have to disclose information on the knowledge in a form of reports and what for? The answer to these questions depends on the individual information needs of the entity but it is undisputed, each company requires the access to information on possessed knowledge. The measurement of knowledge is associated with dissemination of knowledge concerning the firm and indication of directions towards which the knowledge evolves. The author, by reviewing international literature, would like to answer the question whether measuring and reporting information on the knowledge assets is possible and how reliable can it be. Keywords: knowledge, measurement, reporting information on knowledge, value of knowledge, knowledge reports
1. Knowledge-based economies Economies based directly on production, distribution and utilization of knowledge and information can be defined as knowledge-based economies [The Knowledge, 1997, p.7]. Knowledge-based enterprise (KBE) can be described by four characteristics [Spender, 1996, p.56-57]: interpretative flexibility (strategic choice), boundary management (where the company starts and ends with reference to its environment and competition), identification of institutional influences (how the company reacts to interactions between internal and external factors), difference between methodical factors and knowledge ingredients (connections of general and individual knowledge, knowledge flow at the company). Nurmi [1998, p26-32] applies more pragmatic approach and defines KBE as an enterprise with knowledge deeply ingrained in company products and services. Gera and Masse [1996, p.59] define KBE as enterprises that utilize knowledge as a key factor in process of production and service or factor that vastly influences development of new knowledge being an effect of core processes at the company. In order to be classified as KBE, enterprise must develop skills and abilities of [Staples, p. 1-4]: acquiring and creating knowledge, maintaining and accumulating knowledge as well as developing and transfer knowledge.
2. Data, information and knowledge Enterprise knowledge is associated with human resources and technology which always were fundamental factors influencing company development [The knowledge, 1997, p.9]. Knowledge generation at the company is strictly connected with process of acquiring and processing data into specific information that consequently create or enrich company knowledge. Devenport and Prusak [2000, p. 1-6] define data as a set of discrete, objective facts about events, Sobol [2002, p. 119] describes data as things and facts that can become base for further analysis. In business contest, data is commonly described as structural record of transactions. The record can be centralized or decentralized. Sobol [2002, p. 273] identifies data with informing or communicating about something, conveying news. Information has its sender and recipient. Shapiro and Varian [2003, p.49 -53] define information broadly, i.e. information is anything that can be described by numbers (reduced to binary code).
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Rumniak Paweł Conversion from data to information by setting value (shape) can be achieved by many methods as a result of witch data become [Davenport, 2000, p. 1-6]:
contextualized – we know why we gather data,
categorized – we know what analytical units and key components are,
calculated – data can be analyzed by mathematic or statistic methods,
corrected – data errors are eliminated,
condensed – data can be a subject of mathematic operations (e.g. totting),
and, in this way, converted into information.
Data is converted into information and information into knowledge. The conversion can be achieved by:
comparison of information about an event and information about another event that we know,
consequences – impact that information has on decision process and further actions,
connections – how this field of knowledge is connected with or influences another,
conversation – what other people think about this information.
Data and, subsequently, information meet recipients and in this way influence on the level of their knowledge. Knowledge is attributed to people because when having acquired knowledge, man is described as wise or acquainted with something which is hardly attributed to things. Davenport and Prusak [2000, p. 5] describe knowledge as fluent mixture of experiences, values, information and expert approach, that deliver basis to acquire new experiences and information. Knowledge comes from and is applied by human mind. Enterprise knowledge is demonstrated not only by company documents but also organization itself, processes, applied practices and standards. Knowledge is also defined as set of convictions consistent with reality [Sobol, 2002, p. 1124]. Knowledge results from accumulation of information, experience, communication and deduction [Zack, 1999, p.46]. Knowledge can be perceived as a thing that can be stored (objective approach) as well as a process of using knowledge in action (processive approach) [Zack, 1999, p.46]. Rouse [2002, p. 283] defines data, information and knowledge as follows:
data is a result of measuring different variables(i.e. electric voltage, answering time) or opinions,
information is aggregated data, presented in understandable form in order to be utilized, i.e. tables or charts of statistic data or trends,
knowledge is valuable (in terms of utilization, usefulness) information, organized in human mind in order to be used in a purposeful way, i.e. to draw conclusions or to explain phenomena.
3. Knowledge classification Emergence of knowledge is a result of measuring data that, after properprocessing, become information which is basis to create enterprise knowledge. The most common classification of elements of knowledge described widely in professional literature distinguishes two types of knowledge tacit (undisclosed) and explicit (disclosed). Undisclosed knowledge is understood and applied subconsciously, difficult to articulate, results from direct experiences and unorganized knowledge exchange. Disclosed knowledge is more definite, possible to articulate and describe. Further partition of disclosed knowledge is based on two criteria: generic criterion and level of minuteness. According to generic criterion, disclosed knowledge can be divided into:
declarative, i.e. describing phenomena, that gives basis to effective communication and exchange of knowledge at the company,
procesal, i.e. relating to how phenomena arise or how they are executed, that gives basis to effective coordination of activities,
informal, i.e. relating to why phenomena arise, that gives possibilities to coordinate strategy and to achieve company’s goals.
On the grounds of the level of minuteness, knowledge can be divided to:
overall knowledge – wide, often with unrestricted access,
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specific knowledge (detailed) – narrow, limited access.
Fu, Chui and Helander [Fu, 2006, p.52-53] distinguish the following types of knowledge:
undisclosed and disclosed,
procedural and declarative,
esoteric and exoteric,
deep and shallow,
what-who-why-how.
The same authors [Fu, 2006, p.52-53] distinguished four types of knowledge for units engaged in manufacturing process planning:
market knowledge (MK),
knowledge possessed by individuals (human knowledge – HK),
technical and technological knowledge (TTK),
procedural knowledge (PK).
Relations between above mentioned types of knowledge are presented in picture no. 1. Technology knowledge Patents
H u m a n k n o w l e d g e
Safety
Standards
Materials
Packaging
Costs
Vision
Quality
Aesthetics
Ergonomics
HOW
Skill
W H O
Experience Creativity
W H Y
Knowledge
P r o c e Design process d Decision making process u r Case reasoning process a l Knowledge transfer
Reliability
process
Team structure External structure
Heuristic
Management structure
WHAT
Knowledge structure Mission Market constrains Culture Ratio of market share
Credit standing
Politics
k n o w l e d g e
Customer acceptance
Customer relationship
Market knowledge
Customer requirements
Figure 1: Types of knowledge Source: [Fu, 2006, p. 53] OECD [The knowledge, 1996, p.15], in its report on knowledge management, distinguish the following types of knowledge:
know-what,
know-why,
know-how,
know-who.
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Rumniak Paweł Scharmer [2000, p. 38-40] distinguished types of knowledge:
disclosed,
undisclosed personified,
undisclosed not yet personified.
Chua [p.70-74], Matusik and Hill [1998, p. 683 -685] divide knowledge into:
private and public,
component and architectural,
individual and collective,
disclosed and undisclosed.
4. Knowledge as an asset Assets, in accountancy meaning, are property resources possessed by company, having clearly determined value, arising due to past events and, in opinion of management, contributing to economical profit for the company in future. In principle, the economical profit should take shape of material goods, financial profit i.e. higher income and/or positive cash flow for enterprise. However, there is no condition on what exact form a component of assets should take, d.h. it doesn’t need to be materialized and, thus set of assets can be easily extended to non-material elements such as knowledge. Knowledge assets deliver three essential factors deciding of company value: (1) discover knowledge as value in use at the company, (2) have value and therefore can be a subject of trade (sales, purchase), (3) contribute to development of intellectual capital at the company. Company powers are determined, to a high degree, by knowledge assets, i.e. possessing and proper management on this field and thus companies that aim to improve and strengthen their position (their powers) must identify and name possessed knowledge assets at first place. Boisot [1998, p.3] defines knowledge assets as knowledge stock which is expected to turn into activities in form of services (products) within indefinite time. As opposed to material assets, knowledge is not a subject of wear and has no definite lifetime and that is why, in theory, knowledge assets can exist perpetually, in contrast to material assets whose lifetime is limited and their value falls. Lev uses knowledge assets without determining any definition, thus allows unconstrained approach to knowledge assets. Edvinsson and Malone [1997, p.34-39] describe knowledge assets as a mixture of cognitive processes, contextual comprehension and experiences. They relate to sources of intellectual capital, that is continually gathered at the company. Enterprises create knowledge assets on their own and if they gain knowledge assets from outside, they need to adapt them to specific character of the company before first use. Knowledge assets operate not only at the company but also are connected with clients, suppliers and other company business partners. Knowledge assets are characterized by the following features:
they are coefficient of possessed knowledge and information (always result from disclosed as well as undisclosed knowledge of the company), in this scope, they refer to possessed intellectual capital,
are produced on one’s own and if gained from outside, require adjusting to specific character of the company,
are integrally bound to company and even if they are sold, they are the subject of adjusting process which changes their original shape (new type of knowledge assets arises),
they are difficult to multiply (duplicate),
they can arise at each level of cognitive process at the company and are connected with external as well as internal factors that contribute to their formation,
they result from materialized (codified) as well as non-materialized knowledge,
they have huge impact on formation, maintenance or reduction of competitive advantage of the company,
they facilitate adjusting to new challenges and circumstances at the company – they are very flexible.
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Table 1: Overview on knowledge assets definitions author Wilkins Nonaka
Housel and Bell Debowski Li i Chang
Overview on knowledge assets Knowledge assets consist of facts, believes and acts connected with cognition of new truth that deliver economic values (advantages) to their owners knowledge assets are firm specific, necessary to create value (economic profit) for the company; at the same time, they become: contribution, effect and semifinished product of process creating knowledge at the company (knowledge management system) knowledge assets contain patents, copyrights, data bases, employees’ “brains”, processes and information coming from information system company knowledge assets are gathered due to applying and interpreting information, experiences, mistakes and other disturbances knowledge assets are company intangible assets connected with knowledge, know-how, experience, best practices, intellectual property relating to possessed potential to create property and welfare
Source: [Wilkins, 1997, p. 62 – 63]; [NonakaIkujiro, 2000, p. 20 – 22]; [Housel, 2001, p. 14]; [Debowski, 2006, p.21]; [Li Sheng-Tun, 2009, p. 767 – 769] Li and Tsai [2009, p.288-294] proposed the following knowledge assets systematics 1:
principal knowledge assets – relating to knowledge existing at the company, that has essential impact on permanent competitive advantage as well as accessibility of knowledge (or its effect in form of knowledge assets),
dynamic knowledge assets – relating to those ingredients that has slight impact on permanent competitive advantage while having essential impact on accessibility of definite knowledge assets,
supporting knowledge assets - relating to assets that have essential impact on permanent competitive advantage while having slight impact on accessibility of knowledge,
low-grade knowledge assets – with no considerable impact either on permanent competitive advantage or accessibility of knowledge.
Rodgers [2003, p.182-185] and Friz [1997] proposed knowledge assets division into three principle groups:
human: approaches, perception and workers’ skills; their motivation, commitment in company. That is knowledge that is possessed and created by every single man,
organization: intellectual property such as: copyrights, patents, trademarks, infrastructure; they consist in factors connected with organization culture and other processes related to knowledge,
relations: knowledge on environment i.e. competitors, society, clients, managers, suppliers with whom company interacts.
On the other hand, basing on FASB definition concerning capital reserves made for future losses, knowledge assets can be divided into three principle groups:
probable: chance of reaching cash flows belonging to that group in the future is high,
quite possible: chance of reaching cash flows belonging to that group is higher than small and lower than probable,
improbable: chance of reaching cash flows belonging to that group in the future is very small.
1
Starting point for Li and Tsai to determine systematics of knowledge assets, was an assumption that this group of assets is firmly bound with permanent competitive advantage of the company as well as accessibility to specific knowledge (appropriability). Resources that have potential to gain permanent competitive advantage must be characterized by the following features: (1) they have value, (2) they are rare, (3) they are difficult to forge, (4) they do not have substitutes. In order to settle accessibility to certain knowledge, one needs to identify: (1) patents preventing from duplicating innovations, (2) patents or constraints assuring refunding of invested finances, (3) trade secrets, (4) lifespan (from the moment of creation to expiration of component protection), (5) pace of absorbing innovations by the market, (6) additional activities or services connected with innovation. In order to achieve systematics of knowledge assets, the authors analyzed following issues: (1) do assets have value, (2) are knowledge assets rare goods, (3) can competitors easily reproduce knowledge assets, (4) can knowledge assets be substituted, (5) can knowledge assets be effectively protected by certain intellectual capital rights, (6) can knowledge assets be easily imitated and improved due to their properties, (7) are there numerous complementary assets connected with knowledge assets that can be controlled by company.
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Rumniak Paweł Mentzas, Apostolou, Abecker and Young [2003, p. 27] divide knowledge assets into three categories: human, structural and market. Marr, Schiuma and Neely [2001, p.C4B/15] divide knowledge assets into:
stakeholders’ resources, consisting in: relations with stakeholders and human resources,
structural resources, consisting in: physical infrastructure and virtual infrastructure. Virtual infrastructure consists in: culture, routine activities and practices and intellectual property.
5. Knowledge measurement Knowledge possessed by companies is not uniform and therefore possibility of measuring is determined by type of enterprise knowledge. Knowledge measurement can take place in quantified or descriptive (qualitative) depiction and refer to those part of knowledge that can be identified, managed and controlled. In quantified depiction, knowledge can be measured by quantitative or neutral (qualitative) units. In its nature, descriptive knowledge measurement is subjective and is usually used to reflect level of knowledge that is difficult to classify – knowledge that is undisclosed but function at the company. Albino, Garavelli and Schiuma[2001, p.415-419], applying systematic approach to characterize knowledge, distinguished four important fields in process of knowledge measurement (coding) forming together the whole process of knowledge acquisition: type of knowledge, input factor, processes and affects. Knowledge was divided into:
intuitive knowledge,
undisclosed knowledge,
knowledge described by qualitative parameters,
knowledge described by quantitative parameters,
scientific knowledge.
Table no. 2 shows process of coding knowledge. Table 2: Five codification levels of knowledge Knowledge Intuitive Tacit
Input factor Partially known Not accurately measurable Almost accurately measurable
Processes Intuitive Experience-based
Quantitative
Accurately measurable
Quantitative
Scientific
Accurately measurable
Scientific
Qualitative
Qualitative
Output characteristics Partially measurable, not controllable Not accurately measurable, pratially controllable Almost accurately measurable, qualitatively controllable Accurately measurable, quantitatively controllable Accurately measurable, scientifically controllable
Source: [Albino, 2001, p. 419]
Income factors are not well known at the level of intuitive knowledge, processes used by units using this type of knowledge are mainly intuitive or based on (own) experiences. Effects of these processes are partly measurable and can be observed but cannot be controlled at the level of company. Income factors at the level of undisclosed knowledge are measurable but the level of accuracy of such measurement is very low, most of the processes base on experiences (acquired knowledge) and effects of these processes are measurable (imprecisely) and partly controlled. Income factors for qualitative knowledge are quite precisely measurable, procedures connected with utilization of these factors concern qualitative relations (casual connection) between income factors and effects of the processes. Income factors for quantitative knowledge are measured precisely, processes are distinguishable at the angle of income factors, processes and effects and therefore at every level there is a possibility of precise measurement and establishment of processes. That is strictly controlled knowledge. Boudreau [2002, p.8-11], in order to measure knowledge, applies approach consisting in three fields: resources, flows and factors supporting knowledge development. Table no. 3 presents characteristics of these elements.
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Rumniak Paweł Table 3: Knowledge measures Stocks accounting, augmenting financial statements, patents or publications and their citation patterns, organization experience and competitive rivalry, learning curves, unit-level education, experience and job requirements, “high-performance” work systems
Flows performance chan ges between units Or firms, type of alliance reorganization, perceived knowledge flows between units and alliance partners, movement of routines, tools and ideas, including patents, perceived information exchanged or awareness of knowledge available in other units, collaboration and information sharing between colleagues, analysis of work products for sources of ideas and information
Enables geographic and political proximity, international and domestic organizational and allance design, R&D expenditures, Absorptive capacity, Network attributes (strength, intensity, structure, communication, individual movement), tacitness
Source: [Boudreau, 2002, p. 10]
Marr, Schiuma and Neely [2002, p. 289] basing on knowledge assets map, show example measures of knowledge assets. Marr and Schhiuma [2001, p. C4B/19-C4B/20] describe indexes as a set of balanced knowledge assets measures (key indexes) used to measure knowledge at the company. Shannak [2009, p. 250-251], considering problem of selection of indexes that reflect actions in the scope of knowledge management system, indicates two principle ways of acquiring data, i.e. from databases or as a result of analysis. Using this approach require previous gathering data in database (designed especially to manage knowledge). Data necessary to evaluate knowledge field described by qualitative indexes are acquired from any types of analysis, surveys, expert appraisements, and so on. Danish Agency of Trade and Industry [A guideline, 2003] developed guidelines to prepare intellectual capital report, which, among others, enclosed indexes characterizing that capital in the following configuration: management challenges, activities and initiatives, resources indexes, activity indexes and effects indexes. The purpose of such a report is to disclose unit’s resources base and activities connected with development of these resources. Principle division of analyzed fields involves challenges taken up by managers, i.e. recruitment and maintaining employees and competences, development of processes at the company, market recognition, tightening partnership relations with clients, accumulation of information about users’ needs, development of access to outer knowledge resources. All of the indexes used in this model (concerning resources, activities and effects) have non-financial character and any types of intellectual capital report based on these guidelines are numbered among narrative accounting – without valuing subject of report. Akerblom, Bloch, Foyn, Mortenssen, Mansson, Nilsson, Nas, Pettersson and Salte [2008, p.8] divide main indexes applied in economy (based on knowledge) as follows:
income indexes (human and financial resources introduced to company activities),
processes indexes (describing factors that influences processes),
effects indexes (direct results of activities on enterprise level),
impact indexes (outer effects on other companies).
However, these are indexes measured on macroeconomic level and refer to specific economy, showing overall picture of development against the background of other economies (countries). Above indicated examples refer to qualitative knowledge measurement and they valuate neither knowledge nor knowledge resources possessed by unit. Valuating knowledge at the company can be based on methods of intellectual capital measurement, i.e. by the following methods:
IC-index approach [1997, p.88-89] based on four capital indexes categories:
Relations, including: qualitative increase od relations, trust increase, clients maintenance, effectiveness and quality of distribution channels,
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People, including: fulfillment of key factors of success, value created accrue to an employee, efficiency and effectiveness of training,
Infrastructure, including: efficiency, effectiveness, utilization of key factors of success, effectiveness of distribution,
Innovations, including: possibilities of creating new fields of operation, possibilities of creating good products, growth, productivity.
Approach proposed by Sveiby and Lloyd [1987, p.75-76] connected with know-how capital measurement 2 with help of the following indexes: turn of capital invested in know-how, indexes of effectiveness and productivity (income per client, administrative costs as income percentage, index of productivity), index of risk/stability of know-how capital (e.g. relations of personal costs to value-added),
Intangible assets monitor [Marr, Schiuma, Nelly, p. 24], in which indexes of growth, effectiveness and stability are analyzed with reference to human competences, internal as well as external company structure. Table no. 7 presents approach proposed by Sveiby.
Technology Broker Model [Marr, 2001, C4B/10-C4B/11] proposed by Annie Brooking allows valuating calculation of intellectual capital. In this model division of intellectual capital into four fields of indexes is applied: market assets, human capital bound assets, intellectual property, structural assets.
Other models supporting process of knowledge assets evaluation can derive from [Bose, 2004, p. 457-468]:
ICM Group,
Canadian Management Accountant,
Universal Intellectual Capital Report, etc.
6. In conclusions Problem of knowledge measurement is, for the time being, one of the most essential research areas. Scientists try to find out the way to unify process of identification, measurement and knowledge reporting at the unit. At the same time, analogous activities are executed on the level of enterprises that pay special attention to factors creating value and proper management of these factors. Knowledge and knowledge assets can be subject of reliable measurement based on one of measuring methods. However, the prerequisite of getting proper measurement is precise definition of what type of knowledge the company possesses. Settlement of knowledge structure will facilitate selection of proper measurement methods thus optimize measurement itself and knowledge measurement results. However, it is impossible to state that, in this field, scientific and practical achievements are advanced enough to make knowledge measurement process standardized, based on generally accepted methods and procedures and thus discredit reliability of knowledge valuation to some degree. Therefore, accountants should feel discomfort to entirely trust values being effect of knowledge or knowledge assets valuation.
Acknowledgement "This article was founded by The National Science Center allocated on the basis of a decision DEC2011/03/hs4/01299"
References A guideline for intellectual capital statements – a key to knowledge management, Danish Agency for Trade and Industry, Ministry of Trade and Industry, November 2000, Akerblom M., Bloch C., Leppalahti A., Mortenssen P., Mansson H., Nilsso R., Nas S-O., Pettersson I., Salte O.: Policy relevant Nordic innovation indicators, summary report of the NIND working group of the Policy Relevant Nordic Innovation Indicators (NIND) project 2008, Albino V., Garavelli A. C., Schiuma G.: A metric for measuring knowledge codification in organization learning, Technovation 2001, Vol. 21, Boisot M. H.: Knowledge assets, New York, Oxford University Press 1998, Bose Ranjit: Knowledge management metrics, Industrial Management & Data Systems 2004, Vol. 104, No. 6,
2
Authors define know-how capital as summarized years of experience of all people at the organization. The level of this capital is increased with every following year while not all of the experiences are elements of the capital.
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Rumniak Paweł Boudreau John W.: Strategic knowledge measurement and management, CAHRS Working Papers Series 2002 no. 02-17, http://www.ilr.cornell.edu.cahrs, Chua Alton: Taxonomy of organizational knowledge, Singapore Management Review, Vol. 24, No. 2, Davenport Thomas H., Prusak Laurence: Working Knowledge. How organizations manager what they know, Harvard Business School Press 2000, Debowski S.: Knowledge management, Wiley 2006, Edvinsson L., Malone M., S.: Intellectual capital. Realizing your company’s true value by finding its hidden brainpower, Harper Collins Publishers 1997, New York, Friz-enz J.: The 8 practices of exceptional companies, Anacom Books, New York 1997. Fu Qiu Yuan, Chui Yoon Ping, Helander Martin G.: Knowledge identification and management In product design, Journal of Knowledge Management 2006, Vol. 10, No. 6, Gera S., Masse P: Employment performance in the knowledge-based economy, Human Resource Development Canada 1996, Paper No 14, Housel T., Bell A.H.: Measuring and managing knowledge, 2001, McGraw-Hill Boston, Intellectual capital statement – the new guideline, Danish Ministry of Science, Technology and Innovation, February 2003, Li Sheng-Tun, Chang Won-Chen: Exploiting and transferring presentational knowledge assets in R&D organizations, Expert Systems with Applications 2009 Vol. 36, Li Sheng-Tun, Tasi Ming-Hong: A dynamic taxonomy for managing knowledge assets, Technovation 2009 Vol. 29, Marr B., Schiuma G.: Measuring and managing intellectual capital and knowledge assets in a new economy organizations, in: Handbook of performance measurement, GEE, London 2001, Marr Bernard, Schiuma Gianni, Neely Andy: Assessing and managing knowledge In today’s businesses: review of the state of art. In measuring and managing intellectual capital, Centre for Business Performance Cranfield School of Management, Cranfield, Great Britan, Marr Bernard, Schiuma Giovanni, Neely Andy: Assesing strategic knowledge assets in e-business, International Journal of Business Performance Management 2002, Vol. 4, No. 2/3/4, Matusik Charon F., Hill Charles W. L.: The utilization of contingent work, knowledge creation, and competitive advantage, Academy of Management Review 1998, Vol. 23, No. 4, Mentzas Gregoris, Apostolou Dimitris, Abecker Andreas, Young Ron: Knowledge asset management. Beyond the processcentred and product-centred approaches, Springer, London 2003, NonakaIkujiro, Toyama Ryoko, Konno Noboru: SECI, Ba and leadership: a unified model of dynamic knowledge creation, Long Range Planning 2000, Vol. 33 No. 33, Nurmi R.: Knowledge-intensive firms, Business Horizons 1998 Vol. 41 No. 3, Roos Johan, RoosGoran, Dragonetti Nicola C., Edvinsson Leif: Intellectual capital. Navigating the New business landscape, MacMillan Business, London 1997, Rouse William B.: Need to know – information, knowledge, and decision making, IDEE Transactions on systems, man, and cybernetics – Part C: Applications and Reviews, Vol. 32, No. 4, November 2002, Scharmer Claus Otto: Organizing around not-yet-embodied knowledge in: von Krogh Georg, NonakaIkujiro, Nishiguchi Toshihiro: Knowledge creation. A source of value, Macmillan Pres Ltd, London 2000, Shannak Rifat O.: Measuring knowledge management performance, European Journal of Scientific Research 2009, Vol. 35, No. 2, Shapiro Carl, Varian Hal R.: The information economy, in: Hand John, Lev Baruch: Intangibles assets. Values, measures and risks, Oxford University Press 2003, Sobol Elżbieta red.: Nowy słownik języka polskiego, Wydawnictwo Naukowe PWN, Warszawa 2002, Spender J. C.: Making knowledge the basis of a dynamic theory of the firm, Strategic Management Journal 1996 Vol. 17, Staples Sandy D., Greenaway Kathleen, McKeen James D: Opportunities for research about managing the knowledge-based enterprise, International Journal of Management Review March 2001 Vol. 3 Issue 1, Sveiby Kalr Erik: The intangible asset monitor, www.sveiby.com/articles. Sveiby Karl Erik, Lloyd Tom: Managing knowhow. Add value ….. by valuing creativity, Bloomsbury, London 1987, The knowledge-based economy, Organisation for Economic Co-Operation and Development, Paris 1997, www.oecd.com (acquired on March, 2010), Waymond Rodgers: Measurement and reporting of knowledge-based assets, Journal of Intellectual Capital 2003 Vol. 4 No. 2, Wilkins Jeff, Van Wegen Bert, De Hoog Robert: Understanding and valuing knowledge assets: overview and metod, Expert Systems with Applications 1997, Vol. 13 No. 1, Zack Michael H.: Managing codified knowledge, Solan Management Review, Summer 1999,
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Insights into Knowledge Sharing in the Dubai Police Force Ibrahim Seba1 Jennifer Rowley2 and Rachel Delbridge2 1 Dubai Police Force, Dubai, UAE 2 Manchester Metropolitan University, Manchester, UK Ibrahim.a.seba@gmail.com j.rowley@mmu.ac.uk r.delbridge@mmu.ac.uk
Abstract: Policing has always been heavily dependent on information, intelligence and knowledge, but previous research into knowledge management and knowledge sharing in police forces specifically, and the public sector, more generally is limited. Accordingly, a study, within the context of one organization, the Dubai Police Force, was conducted to generate insights that would not only be of benefit to this organization, but also raise some issues that might have resonance for other police forces and public sector organizations. A questionnaire-based survey of employees’ attitudes and intentions towards knowledge sharing and the factors that affect them was conducted with staff in key departments in the Dubai Police Force. 319 usable questionnaires were collected. Overall, respondents show a high level of commitment to knowledge sharing through positive attitudes and intentions. Over 80% of respondents either agreed or strongly agreed that they enjoy knowledge sharing, that they feel that their knowledge sharing is of value to them, and that knowledge sharing is a wise move. In general, respondents are also positive about manager’s encouragement to knowledge share, the IT systems that support knowledge sharing, and trust between colleagues. Time availability, in terms of time to discuss with colleagues, and to attend workshops and training courses emerges as the biggest barrier to knowledge sharing; there is a sense that in terms of time, knowledge sharing competes with ‘getting the job done’. In addition, organizational structure appears to impact on inter-departmental knowledge sharing. Interestingly, rewards, often considered as an important motivator, emerge as not being relevant in this study. On the basis of the findings of this study, managers in police forces and other similar organisations should acknowledge, and be aware of the nature of any embedded knowledge sharing culture in their organisations. Further, they should seek to cultivate it, not only through the provision of information technologies and adoption of appropriate leadership styles, but also through understanding and managing organisational structures, job design and experiences and rewards. Keywords: police forces; knowledge sharing; knowledge exchange; public sector; Middle East
1. Introduction and background In recent years, with shifts towards a community problem-orientated philosophy, with a more proactive and preventive approach to crime reduction, police forces have increasingly relied on information and knowledge and their associated technologies to improve their performance (Brown and Brudney, 2003). This has meant that the effective management of intelligence and knowledge has become even more crucial, such that there is a strong prerogative for police forces to be proactive in managing both explicit and implicit knowledge. In addition, they need to develop their competencies in knowledge management and in promoting and facilitating knowledge sharing (Berg et al., 2008; Dean and Gottschalk, 2007). In support of such initiatives, it is important to build a body of research to inform best practice in knowledge management in policing. To this end, some studies have been conducted, but most of these focus on the technologies associated with information and knowledge management (e.g. Adderley and Musgrove, 2001; Gottschalk and Holgersson, 2006) or on the types of knowledge and intelligence used by police officer in investigations (e.g. Holgersson et al., 2008; Gravelle and Rogers, 2009). The crucial social and human dimension of knowledge management, which is typically associated with knowledge sharing has been somewhat overlooked. Most of the research that has been conducted in this area has been conducted by one extended team of researchers in Norway, who adopt a particular perspective on policing as a value shop (Berg et al., 2008; Dean et al, 2006; Glomseth and Gottschalk 2007. In addition, Hughes and Jackson (2004) consider the impact of social factors on information sharing in a policing environment, and Lahneman (2004) discusses knowledge sharing in the specific context of the intelligence community after 9/11. In the absence of a significant body of research into knowledge sharing and its facilitation in it is appropriate to look to the growing body of work on knowledge management and, more specifically knowledge sharing in the public sector. This reveals a tension between those researchers who suggest that there is an embedded public service culture of working together and knowledge sharing in public sector organisations (Chiem, 2001), and those who have identified potential barriers and facilitators to knowledge sharing in the public sector (e.g. Cong, et al, 2007; Sandhu et al, 2011).
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Ibrahim Seb, Jennifer Rowley and Rachel Delbridge This research also focuses on knowledge sharing in Middle Eastern countries, a region that has received relatively limited attention from knowledge management researchers. In the only two studies on knowledge sharing in the Middle East, Sabri (2005) and Weir and Hutchings (2005) suggest that in order to implement successful knowledge management and promote knowledge sharing in Arab cultures it is necessary to understand and accommodate the associated cultural values and cultural approaches to organization. This introduction has identified the key prior research and defines the point of departure for this research. To minimize duplication, further elaboration of the literature is embedded in the discussion and conclusions section. The next section of this paper outlines the survey-based methodology adopted in this research. Then, the findings section reports on attitudes and intentions towards knowledge sharing, and offers insights into the role of specific factors in influencing knowledge sharing. Next, the discussion and conclusions section discusses the findings with respect to earlier research, and summarizes the contribution of this research. Finally, recommendations are offered for practice and further research.
2. Methodology This paper profiles attitudes and intentions towards knowledge sharing and some of the key factors that affect it in the Dubai Police Force. The insights reported in this paper derive from a questionnaire-based survey of officers in the Dubai Police Force. This survey is the third stage in a three-stage study, and as such its design has been informed by the two earlier stages.
2.1 The Dubai Police Force The Dubai Police Force (DPF) employs over seventeen thousand officers It views itself as forward thinking and progressive and seeks to take an innovative and strategic approach to the use of new techniques and technologies in pursuit of enhanced organizational performance. It was the first Arab police force to make use of, for example, DNA testing in criminal investingations, electronic finger printing, and, GPS systems to locate Police Patrols via satellite (http://www.dubai.ae/en.portal; accessed on 7/02/2012).
2.2 Questionnaire design A questionnaire-based survey was used in this study because the intention was to profile attitudes across a relatively large number of people in the Dubai Police Force. In addition, it was relatively easy to frame the questions or items for attitude measurement based on prior research in knowledge sharing (Easterby-Smith, Golden-Biddle and Locke, 2008). This study measured eight constructs: intention to share knowledge, attitude towards knowledge sharing, leadership, organizational structure, rewards, trust, time, and information technology. Respondents’ attitudes were measured using 5-point Likert scale questions (5 = ‘strongly agree’, 4 = ‘partly agree’, 3= ‘neither agree not disagree’, 2= ‘partly disagree’ and 1= ‘strongly disagree’). Table 1: Sources of measurement items Variable Leadership Organisational Structure Attitude Trust Intention Reward Time Information Technology
Items 5 items 5 items 5 items 3 items 2 items 4 items 5 items 5 items 5 items
References Lu et al (2006) Van den Hooff and Huysman (2009) Bock et al. (2005) Chow and Chan (2008). Developed items from interviews Bock et al.(2005) Barreto. (2003) Developed items from interviews Van den Hooff and Huysman (2009)
2.3 Questionnaire distribution, respondents and data analysis The questionnaire was distributed to staff working within three large operational departments in the Dubai Police Force. 600 questionnaires were distributed, with the support of managers, and 519 (86.5%) completed questionnaires were received. Questionnaires were reviewed for completeness and other indicators that suggested that the respondents had not read the questionnaire, leaving 319 usable questionnaires, which were used as the basis for the data analysis.
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Ibrahim Seba, Jennifer Rowley and Rachel Delbridge Table 2: Descriptive statistics FACTORS
ITEMS
Attitude
My practice is relation to knowledge sharing is appropriate and effective My knowledge sharing with other department members is an enjoyable experience. My knowledge sharing with other department members is valuable to me. My knowledge sharing with other department members is a wise move I intend to share my knowledge with more departmental members. I intend to share my knowledge with anyone in the department if it is helpful to the department. I intend to share my knowledge with other department members more frequently in the future. I intend to share my knowledge with other department members in an effective way My manager always sets a good example in sharing his knowledge with others. My manager supports me in sharing knowledge with colleagues in other departments. My manager allows me to share my knowledge with my colleagues even though it may influence the present job process. My manager instructs us on how to share our personal knowledge within the department. *My manager does not care about my knowledge and does not encourage me to share my knowledge with other colleagues. The structure of this department promotes collective rather than individualistic behaviour The department designs processes to facilitate knowledge exchange across departmental boundaries The department encourages people to go wherever they need to for knowledge regardless of structure. I know my department members will always try and help me out if I need to know something I can always trust my department members to lend me a hand if I need it I can always rely on my department members to make my job easier by sharing their knowledge I can talk freely to my department members about my personal knowledge.
Intention
Leadership
Organization structure
Trust
ITEM MEAN 4.40
5
4
3
2
1
50.8
33.4
13.5
1.9
0.3
4.59
56.6
30.9
9.3
2.3
1.0
4.55
63.3
32.8
3.5
0.3
0.0
4.30
61.7
32.2
5.1
1.0
0.0
4.29
44.7
41.5
12.5
1.0
0.3
4.42
55.6
31.2
12.5
12.5
0.0
4.41
54.0
34.7
10.0
0.6
0.6
4.38
44.7
41.5
12.5
1.0
0.3
3.99
34.4
37.6
19.6
5.8
2.6
3.97
30.9
35.7
22.8
7.4
3.2
3.54
16.1
26.7
33.1
13.2
10.9
3.98
30.5
33.8
23.8
7.7
4.2
3.52
8.0
20.6
21.9
18.6
30.9
3.59
26.7
29.6
25.1
12.9
5.8
3.44
17.4
32.5
33.4
10.3
6.4
3.09
15.1
23.5
32.2
13.8
15.4
4.03
40.5
32.2
19.3
6.1
1.9
3.97
38.9
30.9
20.6
7.4
2.3
3.91
35.4
36.0
16.1
9.3
3.2
3.85
33.4
34.7
20.6
6.1
5.1
816
FACTOR MEAN 4.46
4.38
3.80
3.37
3.94
Ibrahim Seb, Jennifer Rowley and Rachel Delbridge Reward system
Time
IT
I will receive a higher reward in return for my knowledge sharing within this department. I am more likely to receive increased promotion opportunities in return for my knowledge sharing This department offers attractive rewards to employees for their knowledge sharing *There is no time to share my knowledge with my colleagues due to pressure of work in this organization *This organization does not create time for discussion with our colleagues *I am too busy to attend training courses or workshops in my department Our IT facilities make it easier to cooperate with others within our department Our IT facilities make it easier to cooperate with others outside our department The IT facilities within this department provide a positive contribution to the development of my knowledge The IT facilities within this department provide important support for knowledge sharing. IT makes it is easier for me to get in contact with employees who have knowledge that is important to me.
2.99
12.2
19.0
40.8
11.3
16.7
3.08
15.4
20.3
35.4
14.5
14.5
2.70
10.3
14.5
33.8
18.0
23.5
2.51
10.3
10.0
25.1
30.2
24.4
2.65
12.9
12.2
23.2
30.2
21.5
2.79
14.8
16.7
24.4
21.2
22.8
3.90
33.4
38.3
18.6
4.2
5.5
3.89
28.9
31.5
25.1
7.4
7.1
3.95
31.8
40.8
20.3
3.9
3.2
3.87
28.3
42.1
21.5
4.5
3.5
3.89
32.5
37.9
20.9
3.5
5.1
2.92
2.65
3.90
Table 2 shows the sub-set of the items that were retained after Confirmatory Factor Analysis, which form the basis for reporting in the Findings section of this paper. Data analysis was conducted using SPSS and AMOS. AMOS was used to conduct Confirmatory Factor Analysis to establish the measurement model, and to test for reliability and validity. This led to the elimination of a few items. Descriptive statistics for the remaining items are presented in this paper as a basis for profiling respondentsâ&#x20AC;&#x2122; views on knowledge sharing and related factors.
3. Findings 3.1 Introduction This section presents the descriptive statistics for each of the retained items in the questionnaire. The data provide useful insights into respondentsâ&#x20AC;&#x2122; views regarding various factors. Table 2 contains the percentages for each of the responses to the questions.
3.2 Attitudes and intentions towards knowledge sharing The first and most important insight to emerge from the responses in relation to attitude towards knowledge sharing and intention to knowledge sharing is that most of the respondents show a strong positive attitude towards knowledge sharing (mean 4.46) and also to intention to knowledge share (mean 4.38). Over 80% of respondents either agreed or strongly agreed that they enjoy knowledge sharing, that they feel that their knowledge sharing is of value to them, and that knowledge sharing is a wise move. Many (84.2%) also agreed or strongly agreed that their knowledge sharing was effective. In relation to their responses on their knowledge sharing intentions, most respondents either agreed or strongly agreed that they would seek to extend their knowledge sharing by engaging in knowledge sharing more frequently (88.7%), and with more members of the department (84.2%). Most were also aware of the need to share knowledge in an effective way (86.2%), and to make judgements regarding when it would be helpful to do so (86.8%).
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Ibrahim Seb, Jennifer Rowley and Rachel Delbridge
3.3 Key influencers of knowledge sharing The remainder of the questions relate to the various factors that other researchers have identified as possible barriers or facilitators to knowledge sharing. First, comparing the overall means of these factors, the factors that seem to be regarded as the most important in respect of their impact on knowledge sharing are, respectively, trust (mean 3.94), information technology (mean 3.90), and leadership (3.80). With regard to the items under these factors, around two-thirds of the respondents either agree of strongly agree with the statements. This is important, but it is also important to acknowledge that there are some people who are either neutral or disagree with the statements. In other words, there is a minority who have reservations regarding whether IT facilities support cooperation either inside or outside their department. Similarly, there is a significant minority who can not agree with the statement that their department members will always try to help them if they need to know something.
3.4 Findings on specific factors Taking the factors in their order of significant on the basis of their mean score, each of the factors will now be considered in turn. Starting with trust, there is strong evidence of a supportive departmental culture, with for example, 69.8% either agreeing or strongly agreeing that they could trust colleagues in their department to help them out when they needed assistance. Such strong relationships also extend to knowledge sharing, with 71.4% either agreeing or strongly agreeing that their colleagues could be relied upon to share their knowledge, and 68.1% either agreeing or strongly agreeing that that they were comfortable sharing their personal knowledge with colleagues. Similarly, the responses on Information technology generally endorse the importance of a sound information technology infrastructure to support knowledge sharing. The means of the responses on IT are very consistent, suggesting that in each instance around two thirds either agreed or strongly agreed that the IT facilities available to them support their knowledge sharirng. They generally agreed that IT facilities supported cooperation both inside the department (with 71.7% either agreeing or strongly agreeing) and with other departments (60.4% either agreeing or strongly agreeing). Also, 71.4% either agreed or strongly agreed that the facilities provided a positive contribution to the development of their knowledge. Leadership has long been recognised as important in promoting knowledge sharing. With a mean of 3.80 across all items, there is a reasonable level of agreement that managers within the Dubai Police Force are encouraging knowledge sharing. Respondents are most strongly in agreement that the manager sets a good example in sharing his knowledge with others (72.0% either agreeing or strongly agreeing). However, only 42.8% agree or strongly agree that their manager allows them to share their knowledge even though it may influence the present job process. In other words, there is a hint here of tension between knowledge sharing and ‘getting the day job done’. Intentions to knowledge share may be strong, but can be undermined by a busy workload, as discussed further under ‘time’ below. There is ambivalence as to the effect of the organizational structure of the Dubai Police Force on knowledge sharing. The means for all items are relatively low, due to something of a cluster of responses in the neither agree or disagree middle position. For example, whilst some either agree or disagree (56.3%) that the structure of the department promotes collective rather than individualistic behaviour, that leaves a significant group of people who either do not have an opinion or disagree with the statement. There is an even lower level of support for the statement that processes are designed to facilitate knowledge exchange across departmental boundaries, and very little support (only 38.6% either agree or strongly agree) that people are encouraged to go wherever they need for knowledge. This could be seen to contradict the responses to the question on leaders and encouraging knowledge sharing with other departments. Presumably, knowledge sharing is encouraged across departmental boundaries, but only through channels that are endorsed by managers. In terms of rewards, respondents were neutral as regards rewards for knowledge sharing. For example, 75.3% were either neutral or disagreed with the statement that the department offered attractive rewards for their knowledge sharing. Indeed, a similar percentage number of respondents felt that their knowledge sharing was likely to affect their promotion, to those who did not think that it would affect their promotion. Most probably explicit rewards are not offered – but even if they are, they are not valued by respondents. It is important, however to note that this does not seem to have led to a negative attitude towards knowledge sharing.
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Ibrahim Seb, Jennifer Rowley and Rachel Delbridge Finally, the responses on time yield some interesting insights. First, it is important to observe that all of the statements that remain in this section after CFA are negative statements, and therefore need careful interpretation. Overall, once the responses have been inverted to align with positive questions there is a suggestion that time is an issue as regard knowledge sharing. For example, 54.6% of the respondents either agree of strongly agreed that their time for knowledge sharing was restricted by pressure of work, and 51.7% either agreed or strongly agreed that the organisation does not create time for discussion with colleagues. Similarly, 44.0% either agreed or strongly agreed that they were too busy to attend training course or workshops.
4. Discussion and conclusions This study has profiled attitudes towards knowledge sharing and intentions to knowledge share, and investigated response to questions on a number of the factors that may act as barriers or facilitators to knowledge sharing. Overall, in keeping with a number of commentators and researchers on knowledge sharing in the public sector (e.g. McAdam and Reid, 2000; Gorry, 2008), and consistent with Arabic culture (Weir and Hutchings, 2005), the employees in the Dubai Police had positive attitudes towards knowledge sharing and strong intentions to share their knowledge. This is arguably one of the most important findings from this study. On the other hand, it is important not to ignore the minority who do not share such positive attitudes and intentions towards knowledge sharing, because they have potential to impact on the effectiveness and efficiency of others. Three key factors were identified as the most effective in promoting knowledge sharing in the Dubai Police Force, trust, information technology, and leadership. In other words, in their responses to statements on these topics employees agreed that all three of these factors worked reasonably successfully in supporting knowledge sharing. Given that other research has commented on the importance of these factors to successful knowledge sharing (e.g. Sandhu et al., 2011; Ipe, 2003; Cong et al, 2007), it would appear that the Dubai Police Force has a strong foundation from which to further promote effective knowledge sharing. Most importantly, it seems, arguably in common with other police forces to have a strong team culture with strong trust-based relationships between colleagues in the same department that has been shown to enhance knowledge sharing and performance in police investigations (Glamseth and Gottschalk, 2007). In addition, the investments that the Dubai Police Force has made in information technology are generally perceived to support communication, co-operation and the development of the knowledge of individual officers. Arguably, whilst it may overlook the very important social aspects of knowledge management, the preoccupation of knowledge management researchers and practitioners in police forces with information technology has not been mis-placed; a good IT infrastructure has an important role to play. However, returning to the social aspects of knowledge sharing, this study has shown that officers generally feel that leaders promote and support knowledge sharing, except, and this could be a very important exception, when it conflicts with ‘getting the job done’. Also, there was a sizeable minority, who suggested that their manager did not ‘care’ about their knowledge, and did not ‘encourage’ them to share it with others – even though there was a more positive response to questions including the words ‘sets a good example’, ‘supports’, ‘allows and ‘instructs’. There is a considerable body of research that suggests that leadership is pivotal to effective knowledge sharing (e.g. Bircham-Connolly et al, 2005; Sandhu et al., 2011). Accordingly, although respondents where generally positive regarding leaderships’ role in knowledge sharing, it is likely that there is some variability of practice and experience. The three factors that in this study were either poorly aligned with knowledge sharing, or acted as a barrier to knowledge sharing were organisational structure, rewards, and time. Responses on organisational structure were somewhat ambivalent. Whilst organisational structure is seen to some extent to support knowledge sharing within departments, but there is limited and controlled communication between departments. This is consistent with a predominantly buearacratic culture such as is often associated with public sector organisations (Chiem, 2001). As other authors have discussed, it may be the case that organisational structures (and cultures) do not in themselves pose a barrier to knowledge sharing, but that the practice of knowledge sharing may need adaptation to suit the individual circumstances (Gorry, 2008; Jennex, 2005; Willem and Buelens, 2009). It would probably be worthwhile for Dubai Police Force, and other organisations to further develop their understanding of the relationships between organisational structures and cultures on knowledge sharing practices across the organisation, and make modest changes to both the structure and knowledge sharing practices in order to arrive at an optimal solution.
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Ibrahim Seb, Jennifer Rowley and Rachel Delbridge Responses on rewards suggest that respondents overall do not believe that there are any rewards for knowledge sharing, although, as with all questions it is important to observe that there is a small minority that do think that knowledge sharing might, for instance improve their promotion prospects. Previous commentators and researchers do not agree on whether rewards are appropriate in the public sector. Some suggest that rewards are important for promoting performance (Fathi et al, 2011; Yao et al, 2007), whilst others suggest that rewards can undermine team culture (Holman, 2005). Finally, the responses on time suggest that a significant proportion of respondents experienced some frustration in finding time for knowledge sharing with their colleagues, and for attending training courses and workshops. This is consistent with the comments under leadership where respondents generally feel that leadership are supportive of knowledge sharing, expect when it conflict with ‘getting the job done’. This is consistent with findings elsewhere (e.g. Lee and Ahn, 2007; Sandhu et al, 2011). Indeed, Lee and Ahn (2007) make a link between time for knowledge sharing and public sector manager’s tendency to view knowledge sharing as an additional and supplementary procedure.
5. Recommendations for practice and further research
There is evidence that public sector employees have a relatively positive attitude towards knowledge sharing. Hence, the focus for managers must be on how to cultivate these inclinations, and direct them in such a way that they can enhance performance.
Initiatives to support and enhance knowledge management, and specifically knowledge sharing need to work with both the technology and the social and human factors that may support knowledge sharing and learning and development, including team building.
Managers need to address the ‘time’ issue, and to developing a working culture that values knowledge sharing as an integral aspect of work processes.
More broadly, given the limited previous research on knowledge management and knowledge sharing in police forces, there is considerable scope for further research in this area. It would be beneficial to explore further:
The differences and similarities between different police forces regarding attitudes and intentions to knowledge sharing and the factors that affect knowledge sharing.
The impact of a variety of aspects of organisational and national culture on knowledge sharing.
The impact of different organisational structures on knowledge sharing, and indeed, whether structure is significant, or whether it is over-ridden by cultures.
How perceptions of time availability for knowledge sharing are formulated and how knowledge sharing can be embedded in working practices, rather than being seen as supplementary.
Whether rewards for knowledge sharing are consistent with public sector cultures.
References Adderley, R.W. and Musgrove, P (2001), “Police crime recording and investigation systems – a user’s view”. Policing: an International Journal of Police Strategies and Management,Vol. 24 No.1 , pp. 100-114. Barreto, C. (2003), “The motivators and effects of formalized knowledge-sharing between employees through knowledge management initiatives: a multi-case study approach”, iSchool Information Science and Technology - Dissertations and Theses. Paper 27. (retrieved from : http://surface.syr.edu/it_etd/27) Berg, M., Dean, G., Gottschalk, P. and Karlsen, J. (2008), “Police management roles as determinants of knowledge sharing attitude in criminal investigations”, International Journal of Public Sector Management, Vol. 21 No. 3, pp. 271-284. Bircham-Connolly, H., Corner, J. and Bowden, S. (2005), “An empirical study of the impact of question structure on recipient attitude during knowledge sharing”, Electronic Journal of Knowledge Management, Vol 32 No 1, pp.1-10. Bock, G.W., Zmud, R.W., Kim, Y.G. and Lee, J.N. (2005), “Behavioural intention formation in knowledge sharing: examining the roles of extrinsic motivators, social-psychological forces, and organizational climate”, MIS Quarterly, Vol.29 No.1 pp. 87–111. Brown, M. and Brudney, J. (2003), “Learning organizations in the public sector? A study of police agencies employing information and technology to advance knowledge”, Public Administration Review, Vol 63 No 1, pp.30-43. Chiem, P.X. (2001), “In the public interest: government employees also need incentives to share what they know”, Knowledge Management Magazine. (retrieved from http://www.destinationkm.com/articles/default.asp?ArticleID=301&KeyWords=federal
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Ibrahim Seb, Jennifer Rowley and Rachel Delbridge Chow, W.S. and Chan, L.S. (2008), “Social networking, social trust and shared goals in organizational knowledge sharing”, Information & Management, Vol 45 No 7, pp.458-465. Cong, X., Li-Hua, R. and Stonehouse, G. (2007), “Knowledge management in the Chinese public sector: empirical investigation”, Journal of Technology Management in China, Vol.2, No. 3, pp. 250-263. Dean, G., Filstad, C. and Gottschalk, P. (2006), “Knowledge sharing in criminal investigation: an empirical study of Norwegian Police as value shop”, Criminal Justice Studies, Vol.19 No.4, pp. 423-437. Dean, G, and Gottschalk, P. (2007), Knowledge Management in Policing and Law Enforcement: Foundations, Structures, Applications. Oxford University Press, Oxford. Easterby-Smith, M., Golden-Biddle, K. and Locke, K. (2008), “Working with pluralism: determining quality in qualitative research”, Organizational Research Methods, Vol. 11 No 3, pp.419–429. Fathi, N., Eze, U. and Goh, G. (2011), “Key determinants of knowledge sharing in an electronics manufacturing firm in Malaysia”, Library Review, Vol. 60 No.1, pp. 53-67. Glomseth, R. and Gottschalk, P. (2007), “Occupational culture as a determinant of knowledge sharing in police investigations”, International Journal of the Sociology of Law, Vol. 35 No. 2, pp. 96-107. Gorry, G. (2008), “Sharing knowledge in the public sector: two case studies”, Knowledge Management Research and Practice, Vol.6, pp.105-111. Gottschalk, P. and Holgersson, S. (2006), “Stages of knowledge management technology in the value shop: the case of police investigation performance”, Expert Systems Vol.23 No.4, pp 183-193. Gravelle, J. and Rogers, C, (2009), “Knowledge management: a key ingredient in tackling terrorism”, The Police Journal, Vol. 82, pp.1-8. Holgersson, S., Gottschalk, P. and Dean, G. (2008), “Knowledge management in law enforcement: knowledge views for patrolling police officers”, International Journal of Police Science and Management, Vol. 10 No. 1, pp. 76-88. Holman, D. (2005), The new workplace: a guide to the human impact of modern working practices, Wiley, New York. Hughes, V and Jackson, P (2004), “The Influence of technical, social and structural factors on the effective use of information in a policing environment”, The Electronic Journal of Knowledge Management, Vol 2 No 1, pp. 65-76 Ipe, M. (2003), “Knowledge sharing in organizations: a conceptual framework”, Human Resource Development Review, Vol 2 No 4, pp. 337-359 Jennex, M. (2005), Case studies in knowledge management, Idea Group Inc, New York. Lahneman, W. J. (2004), “Knowledge-sharing in the intelligence community after 9/11”, International Journal of Intelligence and Counterintelligence, Vol 17 No 4, pp.614–633 Lee, D. and Ahn, J. (2007), “Reward systems for intra-organizational knowledge sharing”, European Journal of Operational Research, Vol 180 No 2, pp. 938-956. Lu, L., Leung, K. and Koch, P.T. (2006), “Managerial knowledge sharing: the role of individual, interpersonal and organizational factors”, Management & Organization Review,Vol 2 No 1, pp.15-41. Luen, T. W. and Al-Hawamdeh, S. (2001), “Knowledge management in the public sector: principles and practices in police work”, Journal of Information Science, Vol 27 No 5, pp.311-318. McAdam, R. and Reid, R. (2000), “A comparison of public and private sector perceptions and use of knowledge management”, Journal of European Industrial Training, Vol 24 No 6, pp.317-329. Rowley, J, Seba, I and Delbridge, R (2012), “Knowledge sharing in the Dubai Police Force”, Journal of Knowledge Management, Vol 16 No 1, pp.114-128. Sabri, H. (2005), “Knowledge management in its context: adapting structure to a knowledge creating culture, International Journal of Commerce and Management, Vol 15, No 2, pp. 113-128. Sandhu, M., Jain, K. and Ahmad, I. (2011), “Knowledge sharing among public sector employees: evidence from Malaysia”, International Journal of Public Sector Management, Vol 24 No 3, pp.206-226. Van den Hooff, B. and Huysman, M. (2009), “Managing knowledge sharing: emergent and engineering approaches”, Information & Management, Vol 46 No 1, pp.1-8. Weir, D. and Hutchings K. (2005), “Cultural embeddedness and contextual constraints: knowledge sharing in Chinese and Arab cultures”, Knowledge and Process Management, Vol 12 No 2, pp. 89–98. Willem, A. and Buelens, M. (2009), “Knowledge sharing in inter-unit cooperative episodes: the impact of organizational structure dimensions”, International Journal of Information Management, Vol 29 No 2, pp. 151-60. Yao, L.J., Kam, T.H.Y. and Chan, S.H. (2007), “Knowledge sharing in the Asian public administration sector: the case of Hong Kong”, Journal of Enterprise Information Management, Vol 20 No 1, pp. 51-69.
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Knowledge Management and Creative Thinking Framework Integrated in Training of Future Students Andra Badea, Gabriela Prostean, Adrian Adam and Olivia Giuca Department of Management, Faculty of Management in Production and Transport, Politehnica University of Timisoara, Timisoara, Romania, badeaandra.upt@gmail.com gabriela.prostean@mpt.upt.ro mihaela.adrian.adam@gmail.com oliviagiuca@yahoo.com Abstract: The prior work of the paper illustrates a wide variety of different assertions as regards integrating future students in continuous training courses. The performance in the learning field through knowledge transfer is increasingly debated. To understand this approaches the research team proposes to select and test student skills and competence using creative methods to carry out and improve the knowledge transfer. These tests are helpful for identify the learning style appropriate every student integrated in continuous training courses. Further work is proposing to designing a solution plan for identifying the level of student training, personal preferences that should be developed in accordance with the educational structure that the student identifies or he is already assigned to a potential class. The implication of the study is to improve the strategy of training students using creation techniques for helping students to learn. The procedure includes six sequences of testing designed on specific process topics of evaluation on the level and the configuration of knowledge transfer that matches the future student. Each sequence is carried out by designing peculiar tests on domain of interest to evaluate the potential of the student by his learning style. The improvement brought to framework is based on methodology Win‐Win and Clarity of Communication (WWCC), strategy build on the Theory of Constraints Thinking Process (TOCTP) combined with Lotus Blossom Techniques. The paper demonstrates how WWCC‐TOCTP combined with creative techniques applies the cause‐and‐effect thinking process to understand and improve all systems and organizations, but particularity a research situation for identify the learning style of student in continuous training courses. Keywords: competence, knowledge, student, skills, tests
1. Introduction Institutions to keep up with new technological trends in the educational system of higher education have developed new communication platform and are showing a tremendous interest in implementing knowledge management processes and technologies for increasing the learning strategies. Exponential growth of information developed new thinking (targeted selection of subjects) which refers to how we acquire knowledge and skills, and how we develop learning resources (Sirkemaa S. 2001). Education cannot be traded or purchased by students, learning is rather a process that must be performed by students in question. Multimedia technologies enable learners to be active in the process of communication, and the impact and assimilation of knowledge transmitted is higher (Ahmad, A., Raji, S., Tafila, J. 2010). Education system, specifically continuing education courses must satisfy the requirements of more and more numerous and various needs of the individualistic society which is in a continuous transformation. New recruitment methods employed in continuos training students allowing them to become an integral part of the learning process, new systems can be experience rather than reading or hearing, and in addition can pretend to work and learn in different situations without risk. Learning created in this direction is an active, self‐regulated, constructive process (Bransford 2000), as well as a social one, has a procedural nature which should lead to the construction of individual student learning (Mandl 2002). Using an individual's own methods of learning is one of many ways to adapt to individual differences that are found in a group of students. Currently recruiting methods training courses do not develop selection criteria to be based on individualistic features (Meyer, K., A., McNeal, L. 1987). They are presented in general and are transmitted through formal or informal recruiting techniques to develop a plan without differentiating the target segment. Most often recruiting students for further training courses is internally using its own database by promoting education institutions in those courses that are designed to encourage improvement in a disciplinary field. In recruiting students abroad for further training is accomplished by promoting activities in the field sites. Following these criteria, we note that much of the inefficiency of continuing training programs are based on the fact that they follow the same plan of instruction for all students. This paper proposes a solution to
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Andra Badea et al. increase the impact of training programs, designing a solution plan for identifying the level of student training, personal preferences that should be developed in accordance with the educational structure that the student identifies or he is already assigned to a potential class. Creative thinking framework integrated in training of future students The purpose of developing an integrated framework in creative thinking training of future students is that of establishing criteria by which the student identifies with a particular learning style. Learning styles differ from one student to another and develop distinct features to achieve the level of performance in a particular area (Table 1). We can assume that after these selection criteria students will know how to use the information in autonomously or will know how to operate the transfer of knowledge that can later apply their results in various practical and theoretical contexts. Table 1: Learning styles
We can affirm that learning styles shows special particularities for those students who have digital skills as well background information from the human sciences specializations or those of real science. A student with skills from real science is attracted to practical activities, technological and logical, instead a student who has the skills of human sciences is guided to interpret fixed data. Therefore the manner of using knowledge acquired are charged according to the selection filter of one's own learning style, students are able independently to optimize the operation in completing learning tasks. The table 2 includes the 6 sequences with different topics considered necessary to identify the type of knowledge transfer in the learning process. These sequences are designed in the form of tests underlying assessment of student knowledge level and is the essential foundation for framing in a particular category of learning. Finally all the data extracted from the tests are put together in order to take the decision of selection for personalized learning style. To achieve this, continued work highlights the connections created between these sequences.Through these connections is developing characteristics essential for individualized learning, connections identified after applying the methodology Win‐Win and Clarity of Communication (WWCC), which is based on TOCTP ‐ Theory of Constrints Thinking Process and to help identify logical solutions to achieve data transfer, information transfer respectively knowledge transfer for the future student. TOCTP method can be developed to brings improvements in the educational system, on how students should be evaluated according to learning styles to fit within a certain educational level (Goldratt, Eliyahu M. 1990). TOCTP method must complete the three questions, whose answer to explain the need of a specific individual learning style among the students. TOCTP method can identify the skills of the students, their responsibility toward learning, framing them into a learning style in the educational level. The three questions are:
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What to change?
To what to change to?
How to make the change happen?
Table 2: Distinct topics of procedural sequences
1.1 The question „What to change?” The question „What to change?” refers to the current situation that needs improvement. Essence of the problem is presented or the reasons for the conflict analysis and evaluation required for future decision In this context, the method involves developing "The Current Reality Tree" to see the connection between unwanted effects and their root causes. (Figure 1). The current situation is represented by the conflict created between traditional methods and new technologies for future active involvement of students in the learning process. For solving the conflict is achieved The Current Reality Tree that tries to identify the causes of conflict, reasons that slowing down and do not allow the development of an educational system personalized according to the learner's educational level.
Figure1: The current reality tree The Current Reality Tree shows the basic problem and the symptoms that arise from it. The basic problem is rendered as causes sequence as follows:
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to obtain objective 1, must achieve the requirements of 2 and 3
to obtain objective 2, must achieve the essential requirement 4
to obtain the requirement 3 must achieve the essential requirement 4 '
But the 4 'and 4 are in conflict. More specifically, the conflict can be expressed, for example, from 4' by the following statements:
Are not achieved personalized strategies for identifying the learning styles..
There are no used techniques to motivate students to learn independently.
It is not developed the idea of student self‐assessment.
There are not formulated educational offers customized for active involvement of students in the learning process.
Applying traditional methods, namely 4', the information that students receive during lessons and seminars are explained in general. Most times the information transmitted by the teacher are theoretically explained and applied without in terms of practice. For a student who identifies with kinesthetic learning style (learning through practical application of knowledge learned) this is not useful and do not represent interest. . Students are able to learn new things if are directed to use this information according to their individual abilities, because not everyone knows how they should learn or apply the information learned in solving application logical.
1.2 The answer to second question of the method TOCTP – “To what to change to?” To remove the conflict solution is to change the original requirements based on individual input to turn obstacles into an implementation plan. This is an alternative solution that can eliminate the conflict identified by its first question in the method TOCTP. The solution includes the following goals:
Students learn without thinking that they do so only for proper assessment.
Students to use their learning potential corresponding with custom style.
Students have the opportunity to learn according to their specializations (human science, real science).
To remove the conflict creatively, the research team proposes Electre method as an effective method for identifying framing personalized learning for every student, in situations where there are several variants Vi (i = 1, m) possible to achieve a goal, assessment is made on several criteria Cj (j = 1, n) by comparing variations two by two. The Electre method was developed by a french researcher team led by Bernard Roy, designating it a method of choice in the presence of tables and views. This method involves choosing an optimal variant that can surpass other types and can be applied when the criteria of cardinal preferences are known (Bernard Roy 1968). Thus the test results presented in Table 2 are used as raw data, identifying the level of knowledge, the background that each prospective student has. These results are translated into grades, that the first matrix of the Electre method will be completed (Table 3). Qualifications have the following meaning: VB – very good, G – good, S – satisfactory, N – unsatisfactory. These qualifications will be replaced with notes, calculated after determining the scoring of the scales personalized for every criterion Cj (Table 3). Through the establishment custom scoring of these scales can highlight the impact on the future of student learning style. In this regard, each prospective student is interviewed again in a creative manner applying the method the Lotus Blossom Technique. The Lotus Blossom Technique can be approached from a central theme, which is divided into smaller themes until it reaches several sub‐themes, each with a certain angle of approach. The central theme is represented as a flower bud with the petals collected. The petals are sub‐themes derived from the central theme. Each petal (sub‐themes) develop other sub‐themes to get all the details required to develop creative ideas that provide a solution to the issue analyzed (Michael Michalko 2001). The central theme is based on the learner's educational level around which are represent the 6 sequences with different topics considered to be necessary to identify the type of knowledge transfer in learning. To highlight the impact of learning style of each student properly we apply tests represented in Table 2.
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Andra Badea et al. The 6 sequences with different themes are linked to data transfer information transfer and knowledge transfer so that data extracted from each sequence of tests helps determine the learning style of each personalized characteristic of the student.
Figure 2: The chart Lotus Blossom Technique Thus the sequence 1 (data transfer) test is applied to identify the level of student knowledge. This test is designed in the form of applications that attempt to identify the student experience in practice situations, theoretical situations, combined theory and practice, individually theory and practice, team theory and practice (Figure 3). After this test learner can adapt more effectively to new learning experiences according to theoretical and practical skills in team or individual, for that, the learning process will have a personalized character. Essential foundation in the student testing is the fact that some of them develop preference for practical activities and collaborate on projects more effectively as a team. The tests represent the confirmation of the answer around each sequence.
Figure 3: The chart Lotus Blossom Technique To solve this problem the research team propose the necessity and usefulness of decision‐making methods in selecting the future students in continuous training courses by their learning style (The Lotus Blossom method and Electre method). This method was used to detect the weight of each criteria in decision making.
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To identify the dominant relationship between learning styles presented we use weights to rank them. Using the method Lotus Blossom Technique we identified the following elements:
C1=0,1
C2=0,3
C3=0,2
C4=0,1
C5=0,3
In the next step we calculate the matrix of concordance indices (Table 4) and the matrix of discordance indices (Table 5). For combining the concordance and discordance indices has been insert a threshold (relatively large) of concordance c * and a threshold (relatively small) of discordance d *. With the help of these can be defined specific concept of upgrademay. Sequence variants are evaluated according to the following relationship: cij > cji, then Vi >> Vj, or dij < dji, then Vi >> Vj. Table 4: The matrix of concordance indices
Table 5: The matrix of discordance indices
Relative concept of learning styles upgrademay following the results obtained are concluded as V5> V2> V4> V1> V3. The result of this relation can be translated into the following statement: Students who participate in the training courses are attracted to get involved in the learning process only if the information transmitted fulfills the visual‐kinesthetic learning style. The sequences have shown a high percentage of good answers for practice situations and team theory and practice. More specifically the student is attracted to new learning experiences only if his work is practical and is guided to evaluate his theoretical and practical knowledge within a team (Figure 4).
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Figure 4: The chart Lotus Blossom Technique Practical and theoretical activities carried out by student in continuous training courses should him facilitate its ability to understand what is learned rather than memorize answers, to succeed to make logical connections that can be applied later in the projects developed by him.
1.3 Question „how to make the change happen?” – implementing the solution To take account of the proposed objective to identify the level of student education we further use The Prerequisite Tree considered to be the most important element of the method TOCTP ‐ Theory of Constrints Thinking Process. Practically, is developing a "Prerequisite Tree" for each learning style so that the structure of the course modules will be placed on the student will be designed so that each student receive instruction both theoretical and practical, as well as team counterbalancing the individual variables, taking into account of identified the learning style approach. Finally, each student must reach the same level of knowledge appropriate to the learning cycle. The Prerequisite Tree contains a number of potential obstacles that student might encounter during the process of applying the model to eliminate the constraints. For each intermediate objective (IO) encountered has been identified an obstacle (OBS) that it can be overcome (Figure 5).
Figure 5 : The Prerequisite Tree for visual‐kinesthetic learning style
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2. Conclusions The improvement brought to framework integrated in training of future students is based on methodology Win‐Win and Clarity of Communication (WWCC), strategy build on the Theory of Constraints Thinking Process (TOCTP) combined with Lotus Blossom Techniques which helped to identify alternatives to improve learning among students employed in continuing education courses. Use of this method has identified the conflict between modern techniques, wich tend, to be personalized and traditional teaching methods by providing a framework for structuring and implementing solutions. The Electre method integrated with creative thinking The Lotus Blossom Technique helped to improve this process by identifying personalized student learning style framed in training courses. The utility of those two methods is based on finding customized solutions for the future student. With the aid of decisional method of learning styles as well as the identification of the procedure of constraints we eliminate unproductive solutions in the process learning. Implementation of this method can be developed to make improvements in the educational system, on how students should be evaluated in order to put more emphasis on learning styles for the new learning experiences to be more attractive and practical.This approach stimulates teachers to design, development and structurate the training modules compatible with every learning style that eventually develop all the skills and competencies needed for each educational level.
References Ahmad, A., Raji, S., Tafila, J. (2010) The Student's Attitude toward Use Platform as Learning Resources, University of Granada. Bransford, J., Brown, L., & Cocking, R. C. (2000) How people learn: Brain, mind, experience, and school, Washington, DC: National Academy Press. Goldratt, Eliyahu M. (1990) What is this thing called Theory of Constraints and how should it be implemented, [Croton‐on‐ Hudson, NY]: North River Press. pp. 161. ISBN 0‐88427‐166‐8 Roy, Bernard (1968) "Classement et choix en présence de points de vue multiples (la méthode ELECTRE)", La Revue d'Informatique et de Recherche Opérationelle (RIRO) (8): pp 57–75. Meyer, K., A., McNeal, L. (1987), How Online Faculty Improve Student Learning Productivity, University of Memphis ‐ Chickering, A.W. & Gamson, Z.F. Seven principles for good practice în undergraduate education. Washington, DC: Washington Center News. Weinberger, A., Fischer, F., & Mandl, H. (2002) “Fostering computer supported collaborative learning with cooperation scripts and scaffolds”, Proceedings of the Conference on Computer Support for Collaborative Learning: Foundations for a CSCL Community, CSCL '02. International Society of the Learning Sciences. Michael Michalko (2001) Cracking Creativity. The Secrets of Creative Genius, Ten Speed Press; First Edition, First Printing edition Sirkemaa S. (2001) Information technology în developing a meta‐learning environment, European Journal of Open, Distance and E‐Learning.
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The Importance of Play in Overcoming Fears of Entrepreneurial Failure Ramona Cantaragiu and Shahrazad Hadad The Bucharest University of Economic Studies, Bucharest, Romania ramona_cantaragiu@yahoo.com shahrazada1988@yahoo.com Abstract: Entrepreneurship is a key tool in achieving EU’s plans regarding economic development but, for the most part, Romania has been slow to keep up the pace. Besides problems in obtaining financing and receiving legal support, a recent Ernst and Young report (2013) furnishes a bleak picture: more than half of the local entrepreneurs interviewed regard failure either as a barrier to future business projects or as a lack of necessary skills, while only 12% see it as a learning opportunity. One possible explanation for the high levels of fear inspired by entrepreneurship could be connected with the absence of true models in the popular culture. Although the media has covered the story of successful new businesses, most Romanian business owners bring to the market products that are not innovative and they don’t use any technological advances (Matiș et al., 2011), barely fitting the description of an entrepreneur and thus representing non‐models for entrepreneurial innovation. This paper argues that teaching entrepreneurship should be first of all about choosing the right pedagogical philosophy and that this choice is contingent on the characteristics of the cultural environment. In a society clearly unprepared to foster new entrepreneurs, the emphasis has to be placed on creating the right emotional response to entrepreneurial opportunities through methods that use play as a strategy to engage students in entrepreneurial games created in bubble environments in which different rules apply and failure is not followed by any harmful consequences (Huizinga, 1955). Thus, we propose a pedagogical strategy that would provide students with experiential activities perceived as opportunities for them to actually embody the persona of the entrepreneur. This way they get the much needed habitus (Bourdieu, 1990) that allows them to embrace failure as a learning opportunity. Keywords: entrepreneurship, education, play, emotional knowledge, teaching, higher education
1. The state of entrepreneurship in Romania From its inception, the European Union has regarded entrepreneurship as a key tool in achieving knowledge based economy, an idea expressly present in its strategies. Romania joined the European Union in 2007 and since then the governments that have been in power attempted to increase the level of business creation through several policies such as: facilitating access to funding, integrating entrepreneurship education in the national curricula or in life‐long learning programmes. Today, Romania has a pro‐entrepreneurship culture in which the business owner career choice is seen as a desirable one and entrepreneurs are highly regarded (Matiș et al., 2011). However, Romanians have not been able to construct a coherent environment in which people with entrepreneurial drive would feel safe enough to make the step from identifying an opportunity to actually exploiting it. Besides problems of obtaining financing and receiving legal support, a recent Ernst and Young report (2013) furnishes a bleak picture: more than half of the local entrepreneurs interviewed regard failure either as a barrier to future business projects or as a lack of necessary skills, while only 12% see it as a learning opportunity. One possible explanation for the high levels of fear inspired by entrepreneurship could be the absence of true models in the Romanian popular culture. This is a problem which was also recognized by the European Commission (2012), which devised a strategy to popularize the successes of European entrepreneurs because: “there is also a widespread culture that does not recognize or reward entrepreneurial endeavours enough and does not celebrate successful entrepreneurs, as role models who create jobs and income” (p. 4). Although the media has covered the story of successful new businesses, most Romanian business owners bring to the market products that are not innovative and they do not use any technological advances (Matiș et al., 2011), barely fitting the description of a true entrepreneur and thus representing non‐models. As a means to decrease the levels of fear of entrepreneurial failure among the next generations that are about to enter the Romanian and hence European job market, we focus on the ways in which higher education should be modelled based on the national culture in order to provide the right environment for acquiring an effective entrepreneurial habitus (Bourdieu, 1990). We contribute to the field of educational research in entrepreneurship by proposing a modification of the universities’ curriculum so that it emphasizes teaching
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Ramona Cantaragiu and Shahrazad Hadad methods that focus on the emotional responses that students experience when faced with an entrepreneurial opportunity.
2. Current European entrepreneurship education and fear of failure Entrepreneurship education is at the core of several major EU strategic plans for 2014‐2020 which aim to transform the region into an innovation driven economy where entrepreneurs are leading agents of change. In this view, business schools play an important role as prominent providers of stand‐alone entrepreneurship courses (Wilson, 2004), and recent trends show that technical, medical and liberal arts universities are also infusing their curricula with entrepreneurship theory leading to a rise in the number of interdisciplinary programmes (Gunes, 2012; Roberts, 2013). The European Commission (2012b) has recently modelled the desired outcomes of these types of programmes based on the European Competencies Framework. In terms of knowledge, they are looking for an understanding of basic entrepreneurship theory and processes, whereas the skills sought are creativity, analysis, networking, team work etc. Lastly, the EC seeks to encourage certain kinds of attitudes that have been previously linked to entrepreneurial intentions: risk propensity, need for achievement, self‐efficacy, motivation, curiosity, tolerance to failure etc. We borrow the three‐tiered model for outcomes proposed by Kozlinska (2012) which places in the centre education “to become entrepreneurial”, then the middle layer regarding education “to understand entrepreneurship” and the outer layer refers to education “to become an entrepreneur”. She states that each educational program should prioritise the development of the entrepreneurial spirit: the need for achievement, the curiosity, and also the tolerance for failure and risk propensity of each student. In what regards the best practices in the field, several of the authors who have investigated the outcomes of entrepreneurship education construct a powerful case in favour of experiential learning, noting that students get the highest rewards when they are allowed to open real businesses and face authentic problems exactly like an entrepreneur would have to (Taatila, 2010; Römer‐Paakkanen and Pekkala, 2008). However, not all types of experiential learning are deemed equally efficient. For example, even though the business simulation game market seems to be booming, Bellotii et al. (2012) state that the internal logic of the program is hidden and it does not provide feedback on what the student has succeeded to accomplish and the areas in need of improvement, decreasing its efficiency as a teaching method. Thus, it seems that the jury is still out on the best method for entrepreneurship education and we take this as a suggestion that the method is not the critical factor, but the underlying pedagogical philosophy. In Romania, the vast majority of entrepreneurship courses are based on lectures, case studies and business plans, with the occasional guest speaker providing the opportunity to interact with entrepreneurs. The syllabi follow a logical sequence of events starting from opportunity recognition and moving through the theoretical stages of choosing a business model, finding an entrepreneurial team, seeking financing, protecting the intellectual property and ending with the challenges posed by growth. This sequential logic would fit the process world framework proposed by Neck and Greene (2010) in which students are taught that entrepreneurship is a linear activity with predictable outcomes. Moreover, they state that writing business plans distracts the students’ attention away from the necessity to act (to show initiative, a proof of concept, customer validity and simply be in the world) and that the case study method is easily mistaken as simple because in its nature it requires a lot of preparation on behalf of everyone involved and the failed results might be covered by a lively in‐class discussion that does not resemble a true case discussion. Regarding the focus of research in the field, Béchard and Grégoire (2002) conclude that most studies regarding entrepreneurship education focus on course content, on the interface with society or on the “technologies” of education, whereas the psychological and social dimensions are rather overlooked. One of the few papers that address these issues is concerned with the state of entrepreneurship courses in France, where people are said to be far more reluctant to become entrepreneurs than in other parts of Europe. According to Beranger et al. (1998) the French do not see any value in starting their own business; they would rather be doctors and lawyers and are also greatly penalized in case of entrepreneurial failure. Carayannis et al. (2003) argue that a successful learning strategy designed for French students would need to help them understand the beneficial effects that entrepreneurship has on society. They suggest that in order to overcome their fear of failure, French students should be given the opportunity to start their first business while in school. This is a period when failure is safe in regards to the consequences for the students’ future careers and when the environment is suited to learning from mistakes. Our paper joins this line of inquiry by
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Ramona Cantaragiu and Shahrazad Hadad arguing that in cultures with high fear of entrepreneurial failure entrepreneurship education should emphasize emotion management and overcoming fears, not cognitive reasoning. There has been at least one other important contribution to this discussion from Shepherd (2004) who also stressed that entrepreneurship courses should focus on how students feel, rather than their ability to think. However, his proposal borrows techniques from the literature on grief and bereavement, whereas we introduce the paradigm of playful learning inspired by sociological and psychological research on play and games. By fear we understand the emotional reaction to consciously recognized external dangers, fear of failure being brought about by the threat of a business project going bad. The uncertain outcome has been related to the fear of devaluing one’s self‐esteem, the fear of upsetting important others or the fear of having an uncertain future (Mitchell and Shepherd, 2011), but no matter what shape fear takes, research has shown that it has a strong negative impact on the willingness to become an entrepreneur (Arenius and Minniti, 2005; Weber and Milliman, 1997). This has countless implications for countries such as Romania where business failure is harshly sanctioned and widely feared (Brickman Elam, 2008). Kahneman supports the allegation that risk perception is contingent on the level of fear the individual is sensing and that this powerful emotion leads to errors in the decision making process. Undoubtedly, the phenomenon of fear further imprints on the decision maker (the entrepreneur) and emotions become dominant (Schrage, 2003). Researchers have shown that entrepreneurs who have been subjected to failure possess a different ability of spotting and assessing opportunities, than those that have not; therefore this article is against the belief that avoiding failure is good, and advocates that avoiding failure is detrimental to the learning process. Furthermore, Mitchell et al. (2008) have come up with the concept of anti‐failure bias which represents “the perception that risk taking that goes badly is undesirable” (p 228). According to Mitchell (1996), fear of failure can induce pre‐failure bias for those failing to admit having failed. The persons that fail and actually admit this fact are exposed to greater knowledge by enriching their expertise and enhancing their perception of new opportunities. On the other hand, the persons affected by pre‐failure bias tend also to be affected by the confirmation bias when new information is interpreted such as that preconceptions are confirmed and learning is suppressed (ibidem). We argue that by engaging in various activities that simulate instances of risk/fear, students will become more familiarised with the feeling of fear, they will get to acknowledge it, overcome it and finally manage this innate inner inhibition.
3. Creating a framework for learning based on play Play can be conceived as a non‐work activity or as a representation, these being the two most popular interpretations of the concept, or as a disposition (Malaby, 2009). Recent writings on play regard it as an attitude characterized by “the readiness to improvise in the face of an ever‐changing world that admits of no transcendently ordered account” (idem, p. 206). Thus, we take play to stand for a type of human experience regardless of the particular activity the individual is engaged in, and not a form of distinct human activity with clear boundaries. As guiding characteristics defining play as a disposition we cite Malaby (2009): an orientation towards the world based on contingency rather than patterned events, a readiness to improvise and to adapt and the power of the individual to act in social settings without having complete power over the result. This attitude is made possible by the existence of “a free space of movement within a more rigid structure” which means that “play exists both because of and also despite the more rigid structures of a system” (Zimmerman, 2004, p. 159). The studies concerned with play as a special type of activity state that pretend play, characterized by activities where objects, actions and persons are transformed or treated nonliterally, influences the ability to manage emotions (Galyer and Evans, 2001; Moore and Russ, 2006). Based on the literature reviewed and personal experience, a model has been devised for seminar activities that would foster playful learning. The seminar activities should to have the following characteristics: ‐> enabling students to directly relate to the content This characteristic is very important because it creates a sense of engagement for students as the activity builds linkages between entrepreneurship theory and students’ previous experiences, cultural background, passions and hobbies, people they know, plans for the future, fears and joys etc. However, the downside to this is that if the seminar content is too familiar for the students and the teacher does not manage to create a
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Ramona Cantaragiu and Shahrazad Hadad sense of the uncanny by presenting it in a different light, then students will disengage from the process and stop paying attention. We follow Royle’s (2003) definition of the uncanny as: “something familiar arising unexpectedly in a strange and unfamiliar context or [of] something strange and unfamiliar unexpectedly arising in a familiar context” (p. 1). Thus, the balance between familiarity and newness needs to be negotiated between the teacher and the students. Entrepreneurship is especially suitable for this kind of play on the strange and the ordinary because it is a term that has gained immense popularity in Romania due to the numerous study programmes and high TV coverage, but, at the same time, it seems something elusive and difficult to comprehend. In the literature it is stated that educational goals are supposed to be aligned with the learning audience (Niyonkuru, 2005), but in this framework the goal the same for everyone (gaining the necessary abilities to be able to act as an entrepreneur), whereas the content is adjustable to students’ characteristics. ‐> a basic set of rules that ensure every student knows the premises for action, which remain open ended and can also be initiated by students, sustaining a sense of ambiguity and unpredictability; The basic set of rules should be grounded in a certain suspension of disbelief and willingness to engage in the coming activity. This is achieved through multiple interactions between students and teachers in which they learn to trust each other and to be open to suggestions. As for the authoritative role, the teacher is responsible for presenting the setting, providing the materials and acting as the receiver of questions and suggestions. The students should be concerned with the process and thus the outcome can be left unmentioned until the end of the seminar when the results are made clear. In order for the process to take its course, the students have to learn to follow the basic rules of improvisation: never say no to something the teacher or the colleagues propose, always add content to what the others are saying and embrace failure and fail big and dramatically (Johnstone, 1987). Another two important features are the freedom to exit at any time and to skip different stages if they feel uncomfortable or the goals are hard to comply with and the chance to go back and redo previous tasks, allowing students to create their own individual logic of the process of the entrepreneurship seminar. However, just as in real life, certain tasks, such as group projects, require social coordination in case of a do‐over, in which case the students who wish to retry should be given the opportunity to do something similar or to make a self‐assessment of their initial behaviour and understand the factors which led them to make bad decisions and express what they would do in the future. ‐> involving the senses and the whole body The seminar activities should require body movements and taking different body stances because entrepreneurship is also about a certain habitus, a way to carry oneself that involves being confident and able to go passed the comfort zone without letting this fluster them. This requirement is in line with recent scholarly research which emphasizes the bodily dimension of entrepreneurship (Wallace, 2007) by seeing “entrepreneurs [as] uniquely embodied, differently‐able individuals whose bodily attributes and capabilities shape their identities and their experiences of starting and running a business” (Kašperová and Kitching, 2012, p. 3). Moreover involving the whole senses next to training implies increasing the chances of a specific activity, task or situation to be remembered, idea upheld by Yaro and Ward (2007) who stated that “synaesthesia plus training may lead to truly exceptional memory” (p. 11). ‐> it is based on sound social interaction guided by transparency and visibility It is widely accepted that social interaction among students should be the basis of any seminar activity. Students have to be able to respond to what the others are doing or saying, they should actively be asked to give feedback as well as react spontaneously, while the teacher has to make sure that everyone is participating and that the discussions are within common courtesy. Students should be able to see what their colleagues are doing because people learn through mimicking and also from other people’s mistakes. The fact that entrepreneurs are not lone adventurers has been a slow realization that has yet to infiltrate the entrepreneurship curricula. More than that, entrepreneurs need to gain social legitimacy in order to truly be able to call themselves entrepreneurs. The legitimization process involves a series of social interactions in which those that want to be seen as entrepreneurs follow certain social scripts and try to simultaneously prove that they not only represent something new, but also something familiar, or, in the words of De Clercq and Voronov (2009), they try to ‘fit in’ and ‘stand out’.
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Ramona Cantaragiu and Shahrazad Hadad ‐> it has a clear outcome and at the end it provides recognition The way in which play came into the attention of scholars was in the writings of Huizinga (1955) and Callois (2001), both of them defining play as an unproductive activity. In its traditional form, play would be of no use to a new framework for building more engaging learning experiences. However, with the rise of the gaming industry and the flood of research concerned with players’ in‐game behaviour, recent studies have shown that play has an actual productive character which allows the players to regain ownership over the activity and to put their mark on the experience (Pearce, 2006). By producing either material or intangible artefacts, students are involved in practically instantiating entrepreneurship, or making entrepreneurship “happen”. Moreover, the outcome is important for the assessment of the level of participation and the level of know‐how that each student has acquired during the semester, and it thus gains further weight in the playful seminar activities. These five characteristics lead students on a journey of self‐discovery as potential entrepreneurs, which is the main purpose of entrepreneurship programmes. Among scholars studying entrepreneurship education there is a consensus that the goals of the educational process have to fall somewhere along the lines of: (i) providing opportunities for students to learn about entrepreneurship opportunities, (ii) increasing students’ awareness of entrepreneurship as a career option and (iii) increasing their self‐efficacy (Lekoko et al., 2012, p. 12027). From the perspective of the proposed model, these goals can also be expressed in terms of enabling students to better handle their fear of failure. The classroom experiences that we describe expose students to moments of entrepreneurial failure through which they learn how failure feels and what it looks like. The beneficial factors provided by the classroom environment are that students receive feedback immediately and they are better able to understand why some entrepreneurial pursuits fail and others succeed. In terms of mitigating the fear of entrepreneurial failure, this framework offers three avenues: first, students experience for themselves failure and the possibility for redemption, learning that failure is a stigma only as long as one lets it define one’s self; second, students learn how to spot failures in the behaviour of others and how to sanction them, thus learning to encourage certain types of failure and to shun others (e. g. illegal or immoral activities); and third, students gain knowledge that will stir them away from most failures of naive entrepreneurs. We are convinced that an environment constructed based on these characteristics would enable a playful attitude in students and increase their self‐efficacy in what concerns emotion management, thus diminishing their fear of failure. Each requirement is particularly designed to relate to one or more aspects of fear: that of acting in uncertain conditions, that of being seen and allowing your persona to be challenged and that of disappointing fellow team members, the larger group and the teacher.
4. Conclusions The present article introduced a new pedagogical strategy based on the need to supplement students with enough emotional knowledge to help them narrow the gap between being a student, wanting to become an entrepreneur and actually becoming one. Honing (2004) has introduced two models of teaching entrepreneurship: the experiential and the contingency models. Our proposal matches his experiential model in that it seeks to provide students with a series of learning opportunities based on failure in order to create a disequilibrium that, according to Piaget, should motivate students to learn. However, we focused on designing a new pedagogical philosophy rather than changing the current teaching methods, because we consider that methods are vehicles for carrying messages and teachers should be concerned with sending the right message. In this paradigm, the message is to embrace failure while failure has minimal impact on the well‐being of the person, targeting the development of the entrepreneurial way of being the world (Kozlinska, 2012). We also consider that, because of their widespread presence in different institutions and fields and their broad age target, entrepreneurship courses represent a sound opportunity to change a nation’s attitude towards failure. Thus, besides the three initially stated goals of entrepreneurship education, we would add a broader one that refers to bringing change to national cultures. Therefore to play or not to play is a question that should very much be considered when teaching entrepreneurship because games offer the possibility of play, play offers the benefit of getting familiarized with certain things and also offers the possibility of failure, and failure offers the chance of learning from one’s mistakes and thus overcoming the fear of failure.
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The Role of Emotional Intelligence Efficiency in Multinational Financial Institutions Elizabeth Lorena Croitor (Tcaciuc), Cristian Valentin Hapenciuc, Livia Elena Blanariu (Vranciu) and Daniela Mihaela Sandu (Neamtu) University Stefan cel Mare of Suceava, Suceava, Romania lorenacroitor@yahoo.com expresedinte@gmail.com lvvranciu2002@yahoo.com dana_neamtu99@yahoo.com Abstract: This paper focuses on highlighting the importance of high emotional intelligence. It is planned to study and identify the importance granted by multinational financial institutions. Emotional intelligence has become the object of study and it was found that the development of this human side adds value to the individual and the company where he operates. Services institutions in general and financial institutions in particular focuses on the development of emotional intelligence. The way a person feels, it pours on behavior and on its performance. The financial system has produced an insignificant degree of differentiation, and when it comes to competition, the difference is the performance of staff in the system, focusing on competitive advantage, customer satisfaction and customer loyalty. Beyond its biological and psychological dimensions, intelligence is approached by specific social context both as a determinant and as a way of expression. Intelligence in all its aspects is widely regarded as an advantage in solving optimal problem faced by an individual's personal and professional life. Until recently, emotions were considered something that should not have to get rid inconvenience. Emotions are important but energy that helps us to face difficult situations we score there. Emotions can make us richer, knowing them, respecting them, educating them and giving them "intelligence", we can cut generously and liberally addressing human perspective. As we evolve from a professional perspective, intelligence acquires new meanings. Addressing emotional communication made in the context of work experiences, reported in dynamic perspective on continuous change that characterizes reality, requires an analysis of this phenomenon in order to bring the necessary restructuring to optimize emotional communication, knowing that the more communication is more elaborate so it becomes more efficient and productive. Acquisition of skills is necessary to achieve effective emotional communication and responsible persons, in work relationships and more. This paper is an attempt to reveal the awareness and perceptions of employees of multinational financial institutions on the territory of Romania, to the concept of emotional intelligence and to the factors that affect emotional intelligence and its effect on their performance. Research will be conducted based on a questionnaire developed by studying previous work and aims to identify various dimensions of emotional intelligence and its impact. The questionnaire will be multiplied in a number of 150 copies and will be applied to personnel in the various multinational financial institutions operating in Romania. Keywords: emotional intelligence, social intelligence, quality of service, multinational financial institutions, lifelong learning
1. Introduction Motto: "The secret of success is not assured of what you were taught at school or by a economic university diploma and not even the technical know‐how or years of experience. The only factor of importance for professional performance and promotion is emotional intelligence. "‐ Daniel Goleman The need for this research was given by the need to keep up with the changes that occur from one day to another, due to the increasing complexity of the activities, both in business and in the behavior. The way a person feels affects how it behaves and behavior reflect on personal performance, but also affecting the performance of others. Was considered necessary to study these components in Romania MFIs because it is extremely important that employees know the role of emotional intelligence system within the institution and to consider: identifying emotions, using emotions, understanding emotions, managing emotions, to accede to success. The study took place from January 15 to March 15, 2013. This study was performed to identify the awareness of emotional intelligence and to determine the extent to which employees of MFIs is to increase workplace performance. Based on information provided by this study will be to develop training objectives for non‐formal education and to stimulate the development of emotional intelligence with the aim of increasing professional performance in the MFI.
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Elizabeth Lorena Croitor (Tcaciuc) et al. The purpose of this paper is to evaluate the importance given to multinational financial institutions operating in Romania, developing emotional intelligence and awareness and perception of the employees of the MFI to the concept of emotional intelligence versus emotional intelligence as factors affecting and the effect of emotional intelligence on their performance. Addressed in the research methodology is quantitative paradigm, data collection taking place through the questionnaire that was developed and applied to employees of several MFIs card. The sample size was set to 150 employees without any preference among employees or MFIs. Random sampling technique and proper stratification was used to interview both sexes.
1.1 The objectives set out in pursuit of this work are:
Emotional Intelligence at Work.
Checking the awareness of employees about emotional intelligence.
Checking the importance of emotional intelligence among bank employees.
Identify factors that influence employees' emotional intelligence among MFIs.
Value afforded by the results of the work will be beneficial to the training and development of employees in Romania MFIs. The study results could lead MFIs to give greater importance to the area of non‐formal education of employees and establish activities or programs to develop emotional intelligence, leading to increased productivity and efficiency at work.
1.2 Instruments and statistical techniques The research on enhanced recovery results from a questionnaire on the level of emotional intelligence of employees from multinational financial institutions. The questionnaire included 34 questions and was divided into three parts: introduction, the descriptive part, which provided information about the research participants and the content itself. Closed questions were raised such as: dichotomous questions, introductory passage, control, identification, using Likert's scale, semantic differential, questions with one answer or multiple answers. And the results of the analysis were presented with the aid of tables.
2. Insights into the study of emotional intelligence Emotional intelligence includes the essentials: a better understanding of their emotions, effective management of emotions and quality of life, better understanding of others and comfort in relationships increase productivity, while improving personal image. According to statistical research, emotional competence is twice more important than technical skills or intellectual. Developing emotional intelligence is understanding and managing emotions to create harmonious relationships with others. In high school, emotional intelligence is the key to successful counseling. The benefits of increasing the degree of emotional intelligence include: increased performance, improved motivation, increased innovation, increased self‐ confidence, effective management and teamwork comfort. An understanding of this phenomenon involves identifying expressions of intelligence that are not independent of one another. In everyday life, both intrapersonal and interpersonal level and in career, we use social intelligence. Intelligence, understood as potential basic allows the acquisition of knowledge and cognitive skills essential for a person who develops lifelong interaction with the environment, has been called natural intelligence or potential. Academic intelligence is determined by intelligence tests and is a combination of an individual potentiality and knowledge acquired in the first years of life and during formal education. Located in close connection with the nonverbal expression of emotions, emotional intelligence (EI) is required to counter the old theory of IQ (IQ), until recently regarded as exhaustive in terms of intellectual skills possessed by an individual. As, however, many people with a high intelligence quotient (IQ) are exceeded other personal or professional with a coefficient weaker emotional intelligence has gained more ground in the focus of researchers and, more recently, the general public. As a general term, IE describes our level of personal and interpersonal competence. Research shows that good mastery of personal and interpersonal skills is the single most important factor for success in the workplace performances. It also represents an important determinant of how we feel, think and act. IE determines how well we know and manage our own emotions and how well they interact with other peers. Emotional
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Elizabeth Lorena Croitor (Tcaciuc) et al. intelligence includes a wide range of sub‐skills including how to monitor their own emotions and of others, as we discriminate and evaluate these comments and how we use that knowledge to guide our thinking and actions. This can include: assertiveness, impulse control, adaptability, motivation and optimism. Since antiquity, philosophers like Socrates and Aristotle recognized the importance of self‐knowledge and mastery of emotions but in a more abstract and general sense. In "Nicomachean Ethics," Aristotle said: "Anyone can become angry ‐ it's simple. But who should be angry, as should be when, for what you have and how ‐ is not easy. "Here, the ancient philosopher speaks of the rule of our emotional intelligence and the fact that the problem is not existence of emotions but in how they adapt and express. Emotional intelligence is the convergence of two historical trends: changing vision of changing vision of rationality and emotions that constitute intelligence skills. The traditional view argues that emotions are chaotic, random and irrational. The new vision supports the idea that emotions are adaptive, functional and can hold other cognitive abilities. D.Goleman (2001) believes that emotional intelligence includes the following constructs: self‐awareness, self‐control, motivation, empathy and social skills. He also established what is emotional intelligence: IQ is not reversed cognitive is not the same kind, is not to give vent to emotions, not genetically fixed and is distinct personality.
2.1 Company ‐ mobile – intelligence We know that people put different things as priorities based on several factors, but most important is that everyone is given the opportunity to reach so far as it allows the personality, energy, and especially the characteristics of intelligence. Crucial for a harmonious society is not equal results, but the abundance of opportunities. Some experts (R. Herrnstein, K. Murray) argue that the future society will be stratified by differences in intelligence and obvious social mobility will be influenced by these differences. Hence, the key factor in this type of social division is intelligent and not social status of a person or class of origin. The authors believe that this division will have important repercussions for future social dynamics, going to speculate that it might as cognitive elite to impose its rules as a centralized authority and cognitive subclasses will be under the strict control of it. Mobility in modern society is more about intelligence than social origin, which means that a person who grew up in a family social disadvantage but whose IQ is high has a high probability to escape the situation. Also, a person raised in a middle class family, but with a low IQ, is constantly threatened by a modest economic and social level. Some links between social class and intelligence are due to childhood socioeconomic circumstances affecting the development of intelligence. Many reports have shown that the level of intelligence required of learners to come to college is greater than another person who had a privileged environment to learn. A similar effect was observed in employment, where a person's socio‐economic disadvantage may need to show more intelligence to get a service to a person who is in a position with access to educational institutions, informal networks of knowledge and other forms of social advantage. Note that different studies and opinions of researchers, some confirming the hypothesis of socio‐economic influences, others rather consider this factor as having an insignificant advantage for individuals. Emotional intelligence is highly correlated with performance on the job (Stein, S. 2003) while cognitive intelligence has an insignificant correlation with it. Emotional intelligence is not the only predictor of success at work, career satisfaction or activity leader, but is only one of the most important components. Emotional Intelligence is an ability fluid, which explains how a person recognize, understand and manage personal emotions and in relation to others. Decades of research shows that emotional intelligence predicts occupational success beyond any ability, including intelligence and technical expertise.
2.2 A new type of management ‐ that of emotional intelligence In recent years increasingly speaks more than a new type of management, the emotional intelligence that an organization pays attention to emotional abilities of its members, ensuring their compatibility in terms of affective emotional relationships. Research in this area have shown that those managers and employees with a high emotional intelligence are more successful professionally, are intrinsically motivated, more positive, cooperative and ability to establish positive relationships with others.
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Elizabeth Lorena Croitor (Tcaciuc) et al. Emotional management skills has the following characteristics (Roco, 2001):
give ability to use emotions productively;
capacity to prevent and resolve conflicts through negotiation and use them as a source of feedback;
feelings organization members are seen as important variables in achieving success;
use techniques and knowledge through educational programs on emotional and relational difficulties removal experts;
creating an environment where employees are motivated to feel safe, important.
It is noted that emotional intelligence includes emotional awareness skills and specific skills they manifested at personal and social level. This model of organization skills can help a person both in his work and in the relationships we have with others, influencing them to have a better professional performance. The most active and conscientious people tend to be more productive, and those with a high level of încredrii itself may have a higher yield activity. According to recent studies, emotional intelligence can be developed over time (the term referred to Goleman's emotional intelligence maturity). According to Goleman stands the 4 components of emotional intelligence namely: Self‐knowledge:
emotional self‐knowledge;
realistic and accurate self‐knowledge;
confidence.
Self:
emotional self‐control;
maintaining transparency and integrity;
versatility and adaptability to change;
results orientation;
initiative;
optimism and perseverance.
Social "Awareness" (social awareness, group):
empathy (feeling feelings and a real interest in the concerns of others);
"awareness" within the organization;
service orientation.
Management of interpersonal relationships:
development of others, their skills, coaching;
inspirational leadership;
catalyst of change (initiation and management of change);
influence;
conflict management (related to negotiation and dispute resolution);
teamwork and collaboration (creating group synergy in pursuing group goals).
The first two components are regarded as personal components of emotional intelligence, while the other two are regarded as social components of emotional intelligence. Developed social skills and leadership skills, influence, communication, cooperation and teamwork skills are considered desirable. S. Campbell Clark (2003) emphasizes the parallelism between managerial skills and emotional intelligence dimensions providing suggestions so that individuals are able to improve their
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Elizabeth Lorena Croitor (Tcaciuc) et al. knowledge in these areas. Both movements promotes the idea that these skills can be developed and polished over the entire life.
3. Addressing emotional intelligence training as a lifelong learning concept Studies conducted in the past decade shows that vocational training is not enough for success in the workplace. Emotional intelligence is an indispensable ingredient of professional success and has become a criterion for selection of personnel in large companies. Emotional intelligence is the ability to perceive, identify and use emotions to make the best decisions and their practical application. Level is a good indicator of the balance between thought, emotion and action. Worldwide, this element has been shown to be a predictor of success in life better than the cognitive intelligence or knowledge and experience. Any training program, regardless of occupational field should blend a range of skills related to emotional intelligence, training stages, so that at the end of the training, job performance is enhanced properly managed. Here are some of these skills and their meanings:
Recognition of personal emotions and their effects allows people to know exactly what emotions you feel and to understand the links between their feelings and what they think, do and say and understand how feelings affect their performance.
Awareness of personal strengths and personal limits allows a self‐correct and cause learning and personal development.
Safety of value and show off their skills determined personalities, able to make clear decisions despite uncertainties and pressures.
Controlling explosive impulses and emotions determine the successful management of impulsive feelings and negative emotions, causing: self‐control, positivity and determination, even in times of pressure.
Honesty and integrity involves building relationships based on trust, recognizing their mistakes, signaling unethical actions of others, expressing strong views and principles.
Accountability refers to the commitments, organized labor and attention to achieving goals.
Flexibility is based on solving multiple applications easily and adaptation responses and tactics to fit the circumstances unstable.
innovative spirit is manifested in search of new ideas from a variety of sources, original solutions to various problems, addressing new perspectives in thinking and risk taking.
ongoing effort to improve and achieve a certain standard of excellence with focus on results determined setting competitive goals and deliberately taking risks to improve performance.
Aligning the goals of the organization involves finding a broader sense of the organization's mission, values using group decision‐making and identify opportunities to fulfill the group's mission.
Initiative is the ability to respond to opportunities, moving, sometimes across boundaries and breaking the rules, you need to complete an action.
perseverance in achieving goals despite obstacles and setbacks acting rather from hope and optimism rather than from fear of failure.
Therefore, the development of emotional intelligence allows us to exploit the personal skills and creativity and ensure success in life. There, on the other, an old saying that "rational intelligence is to be employed but emotional intelligence is to be promoted." In other words, while the rational intelligence parameters remain relatively constant from one period to another life, a book that managers can rely business development can be emotionally intelligent employees.
4. Description of research The sample consisted of a total of 138 people employed by multinational financial institutions that have branches in Suceava and will be presented according to various socio‐demographic characteristics, such as age, sex and occupation in repsondenților multinationals.
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Elizabeth Lorena Croitor (Tcaciuc) et al. Initially setting the sample was considered about 21 banks operating in the city Suceava and have about 290 employees. We wanted to be as accurate research and thus decided that population sample interviewed exceed 50% of the employees in the research. Thus we have reached a predetermined sample of 150 people employed in Suceava Multinational financial institutions, but the processing of the questionnaires were validated a total of 138 questionnaires that were accurate and complete. Table 1: Demographic profile of respondents SEX Man Woman TOTAL AGE 20‐29 YEARS 30‐49 YEARS 50‐59 YEARS Peste 60 YEARS TOTAL Job Cashier Sales‐contact with the client Manager (management function) Credits / approvals Corporate Administration Human Resources Products / Marketing TOTAL
NO OF RESPONDENTS 39 99 138
% OF TOTAL 28 72 100
48 82 8 ‐ 138 12 68 13 23 ‐ 11 6 5 138
35 59 6 100 9 49 9 17 ‐ 8 4 4 100
It can be seen that the majority of respondents were female and the predominant age is up to 29 years. The first conclusion we can draw is that MFIs employ young, newly licensed and can be easily molded and trained.
4.1 Understand the concept of emotional intelligence In developed countries emotional intelligence is a concept with great difficulty, recognized and highly regarded, but in our country has been given less importance later. This study tries to reveal the perceptions of employees in the banking sector on this very important concept. In order to reveal awareness about this concept is as follows: Table 2: Awareness regarding the concept of emotional intelligence Awareness of the concept of emotional intelligence Known Unknown TOTAL
NO OF RESPONDENTS
% OF TOTAL
138 ‐ 138
100 ‐ 100
The study showed that all respondents answered affirmatively regarding knowledge of the concept of emotional intelligence. This clearly describes the fact that the banking sector in Suceava market is not an exception, accepts and endorses its benefits to increase performance and satisfaction.
4.2 The importance attached to emotional intelligence Although both males and females responded affirmatively to the study of the concept of emotional intelligence, this question was to identify the importance given by them. Responses were:
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Elizabeth Lorena Croitor (Tcaciuc) et al. Table 3: The importance attached by respondents emotional intelligence The importance attached Very Important Important Indifferent Less Important Not At All Important TOTAL
NO OF RESPONDENTS 102 36 ‐ ‐ ‐ 138
% OF TOTAL 74 26 ‐ ‐ ‐ 100
Research shows that all respondents consider the concept of emotional intelligence important and very important, both for themselves and for the company where they work.
4.3 Factors influencing emotional intelligence We start from the fact that some people are able to handle extremely well and quickly certain situations, while others remain trapped in the situation. One purpose of the study was to identify which are the main factors affecting emotional intelligence among respondents. They found that: Table 4: Factors that have an influence on emotional intelligence Factors of influence Communication skills Assertiveness Empathy Optimism Problem solving ability Social Intelligence High interest TOTAL
SCORE 209 415 367 293 342 373 366 2365
% OF TOTAL 8,9 17,5 15,5 12,4 14,4 15,8 15,5 100
Influencing factors were scored from 1 to 5 by respondents (1 ‐ extremely important and 5 ‐ not important) according to importance for each. Communication skills is considered the most important factor that exerts the greatest influence on emotional intelligence, followed by optimism and problem solving ability.
4.4 Factors dominate the feelings of emotional intelligence employees It was very important for us to identify the situation of the employees to ascertain to what their emotional intelligence focus. Thus we found that: Table 5: Factors dominate the feelings of employees Factors of influence Blessed Satisfied Dissatisfied Bored Uncomfortable/Stressed Depressed Angry TOTAL
SCORE 391 363 522 612 245 528 624 3285
% OF TOTAL 12 11,1 15,9 18,6 7,4 16,1 18,9 100
These results clearly show that employees in the banking system are some people with high stress and said they are in an uncomfortable position. This is a very serious reason for concern for proper financial institutions would need to make efforts in this regard, to reduce stress levels among employees and bringing them to emotional stability, for employees to be able to use emotional intelligence as constructive, never destructive.
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4.5 Emotional factors possessed by the employees of Multinational Financial Institutions We individuals are different from one another by the nature of our behavior. The extent to which emotional factors are possessed differ from individual to individual, and emotional stability is important because it directly participating in the development of the organization and increase efficiency. In order to reveal these factors among respondents interviewed were obtained following answers: Table 6: Emotional factors possessed by respondents Emotional factors possessed Adaptability Assertiveness Emotional Expression Managing The Situation / Self‐ Possession Social Skills Stress Management Self Motivation TOTAL
NO OF RESPONDENTS 95 18 30 78 84 25 48 378*
% OF TOTAL 25,1 4,8 7,9 20,6 22,2 6,7 12,7 100
* Total sample size is not the same because there was a question with multiple answers The study shows that resilience and social skills were the two main qualities possessed by the majority (25.1% and 22.2%) of respondents, followed by managing the situation (20.6%). Emotional factors less employees were detained assertiveness (4.8%) and stress management (6.7%). Thus we note that stress management necessarily improved as employees of the banking system to make every day with high stress.
5. Conclusions It is normal for every organization to aim to reach an optimum level of productivity. Human skill and competence is one of the most important elements that can help the organization accomplish its objectives as human strength plays a noteworthy role in changing the level of productivity. The study was conducted to discover factors that influence perception and emotional intelligence among employees. It reveals that employees of multinational financial institutions were fully aware of this relatively new concept of emotional intelligence. The research is no need to impose measures because employees interviewed or shown to be emotionally unstable, and this is really a concern for banking authorities as it affects performance. However respondents surveyed felt resilience, social skills and managing the main features of emotional intelligence needed each. Rational human intelligence is fulfilled by the emotional and completes applied to understanding and problem‐ solving our emotional life, soul. It is manifested by the ability to identify and understand the emotional states of their proper expression, emotional self‐control and emotional energy optimally in a positive or constructive. The research helped us to propose improvement of MFI employees through training on emotional intelligence and organization of team building on "emotional intelligence" to increase the effectiveness and efficiency of both the organization and personnel as necessary to move from mind controlled mind control. Future of emotional intelligence could be represented by the following coordinates:
Concerns for its development will increase and will be increasingly used to create collaboration in organizations;
Will be widely used in recruitment and selection, appraisal and development of employees in the organization;
Theories and measurement tools to increase over time;
A direction for future research based on the need to increase emotional competence with other skills.
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Acknowledgements This paper has been financially supported within the project entitled “Doctorate: an Attractive Research Career”, contract number POSDRU/107/1.5/S/77946, co‐financed by European Social Fund through Sectoral Operational Programme for Human Resources Development 2007‐2013. Investing in people!”
References Bar‐On, R. (2005) The Bar‐On model of emotional‐social intelligence, Consortium for Research on Emotional Intelligence in Organization ‐ Issues in Emotional Intelligence, Psicothema. Campbell Clark, S., Callister, R., Wallace, R. (2003) Undergraduate management skills courses and students’ emotional intelligence, Journal of management education, vol. 27, no1. Chelcea, S. (2008) Psihologie, Editura Polirom Iaşi. Goleman, D. (2001) Inteligența emoțională. Editura Curtea Veche, București. Goleman, D. (2007) Inteligența socială. Editura Curtea Veche, București. Herrnstein, R., Murray, C. (1994) The Bell Curve: Intelligence and Class Structure in American Life. New York: Free Press. Inteligența emoțională în managementul modern.www.scribd.com. Lopes, P., Brackett, M., Nezlek, J., Schutz, A., Salovey, P. (2004) Emotional intelligence and social interaction, PSPB, vol. 30, no 8. Mayer, J.D., Salovey, P. (1995) Emotional Intelligence and the construction and regulation of feelings, Applied and Preventive Psychology, no 4. Mayer, J.D., Salovey, P., Caruso., 2002, Relation of an ability measure of emotional intelligence to personality, Journal of Personality Assessment. Roco, M. (2001) Creativitate și inteligența emoțională, Editura Polirom, Iași. Salovey, P., Mayer, J.D. (1990) Emotional intelligence. Imagination, Cognition and Personality, no 9. Stein, S., Book, H., (2003) Forța inteligenței emoționale, Editura ALLFA, Bucureşti.
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Knowledge Sharing and Channel Choice: Effects of the new way of Working Arjan de Kok, Bart Bellefroid and Remko Helms Department of Information and Computing Sciences, Utrecht University, Utrecht, The Netherlands dekok@uu.nl, b.e.w.bellefroid@uu.nl, r.w.helms@uu.nl. Abstract: The New Way of Working (NWOW) is changing the world in which we work today. The principles of NWOW are based on freedom of time and place to work, and steering on output (results) instead of input (presence). As NWOW is a relatively new phenomenon there is still little research, especially on the effects of NWOW on knowledge sharing channel choice in organizations. Based on the theories of Ipe, De Long & Fahey, and Snyder & Lee‐Partridge a unified model on knowledge sharing was developed with twelve scenarios. These scenarios were designed for sharing knowledge of general and sensitive information on different levels of the organization. A multi‐case research was performed at three companies that were all in the process of implementing NWOW. This provided the opportunity to compare within the same company the sharing of knowledge of NWOW workers with employees that still worked in the traditional way, Non‐NWOW workers. At each case company three NWOW workers and three Non‐NWOW workers were interviewed. This way 216 scenarios results were obtained and evaluated. The companies who were further in their implementation of NWOW showed a more distinct pattern of differences between NWOW and Non‐NWOW workers, but overall a number of differences were consistent. NWOW workers more often share information in an informal way compared to Non‐NWOW workers, especially when sharing knowledge on sensitive information. Though all workers had access to the same channels, NWOW workers used a broader palette of channels to share knowledge than Non‐NWOW workers. The differences are most clear when sharing knowledge on sensitive information; NWOW workers use multiple channels, while traditional non‐NWOW workers use less channels, e.g. face‐to‐face communication, in a more formal way. Organizations can benefit from the results of this research that identifies the direction of changes in knowledge sharing by the implementation of NWOW. Keywords: new way of working, NWOW, knowledge sharing, channel choice
1. Introduction The New Way of Working (NWOW) is a relatively new phenomenon that has a growing interest in organizations. NWOW focuses on the optimization of work and the work environment in order to improve employee productivity and job satisfaction (Bijl, 2011). Especially in The Netherlands the spread and impact of NWOW is increasing (Kluwer, 2011; PWC, 2011), but also in other countries there is an increased interest. As NWOW is an emerging phenomenon, scientific research on the effects of the concepts of NWOW on organizations, individuals and in particular knowledge sharing is still scarce. The New Way of Working may however have substantial effects on knowledge sharing, which organizations do not realize today. The result can be that important knowledge is lost because it is shared in ways that are not managed by today’s traditional methods for managing knowledge sharing. For this reason it is important to research the effect of NWOW on knowledge sharing in organizations. This multi‐case research focused on effects of NWOW on knowledge sharing of general and sensitive information at different levels in the organization. In particular the effect of NWOW on the channel choice and the choice for a formal or informal way of communication was researched by the use of scenarios that painted a certain knowledge sharing situation. The selected companies that were in the process of implementing NWOW were a 22,000 employee global multinational that is active in health, nutrition and materials, a 4,300 employee Dutch Energy DSO (Power Distribution System Operator) company, and a 15,500 employee international software development and IT support company. The following paragraphs describe the background of NWOW and the factors that influence knowledge sharing. The research method and scenarios are explained in chapter 2. Chapter 3 discusses the research results. Besides the channel choice the reason for this channel choice and the choice for formal or informal communication is evaluated. This leads to a number of conclusions and recommendations for future research in chapter 4.
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1.1 What is the new way of working? There is not yet a single definition for the New Way of Working in the literature. Bijl (2011) defines NWOW as ‘a vision for making work more effective, efficient, pleasurable and valuable for both the organization and the individual. This is achieved by placing people center‐stage and, within limits, giving them the space and freedom to determine how they work, where they work, when they work, what they work with and with whom they work. The New Way of Working aims to touch people’s intrinsic motivation and entice them into giving their best in their work.’ The New Way of Working is more than Teleworking; it embodies the redesign of offices to accommodate task‐based workplaces, and a result oriented way of working in which freedom and trust play an important role. Baane et al. (2010) observe four work principles in the New Way of Working: (1) Time and location free work: ‘Anytime, anywhere’; (2) Steering workers towards achieving results: ‘Manage your own work’; (3) Free access to and use of knowledge, experiences and ideas: Unlimited access and connectivity’; (4) Flexible work relations: ‘My size fits me’. They add: ‘These work principles give maximal freedom to employees, on the basis of mutual trust. This trust is expressed in the freedom that employees have for carrying out their work in ways, times and locations that suit them best. The employees are evaluated based on their personal or on the team contribution to the result, rather than their presence. Thus the employees can engage in a working relationship that suits them best in terms of ambition, skills, lifestyle or stage of life’.
1.2 Channel choice and opportunity to share In the choice of the communication channel e.g. a face‐to‐face meeting, an e‐mail or a phone call, Snyder & Lee‐Partridge (2009) state it is not clear what conditions lead an employee to use the phone instead of the intranet to share knowledge. They claim that employees nowadays have a wide array of information and communication technologies from which to choose, but may not make rational choices when determining what channel to use for sharing knowledge. Orlikowski (1992) adds that research has confirmed the notion that technologies are non‐deterministic, but that the employee’s selection and use of technologies emerge from situated practices. In other words: employees tend to choose the channel for knowledge sharing based on their experience of availability, usability, effectiveness and convenience. This means that new working methods and new technological opportunities will not affect the channel choice until the employee has gained sufficient satisfactory experience with this new channel. Carlson & Zmund (1999) identify the following experiences as being particularly relevant for the channel choice:
Experience with the channel.
Experience with the messaging topic.
Experience with the organizational context.
Experience with communication co‐participants.
The Media Richness Theory of Daft & Lengel (1984) describes organizational communication channels, possessing a set of objective characters that determine each channel’s capacity to carry rich information. According to the Media Richness Theory messages should be communicated on channels with sufficient and appropriate media richness capacities. Carlson & Zmund (1999) state that ‘messages communicated on channels that are inappropriate to the equivocally of a situation and richness of the information sought to be transmitted may be misinterpreted by recipients or may be otherwise ineffective with regard to their intended purpose.’ Ipe (2003) states the Opportunity to Share can be formal or informal. Rulke & Zaheer (2000) refer to the formal opportunities as purposive learning channels, designed to explicitly acquire and disseminate knowledge. Informal opportunities are relational learning channels, based on personal relationships. Okhuysen & Eisenhardt (2002) add: ‘Formal opportunities provide a structured environment, including instructions, to share knowledge. They not only create a context in which to share knowledge but also provide individuals with the tools necessary to do so’. Knowledge shared though formal channels tends to be mainly explicit in nature (Nonaka & Takeuchi, 1995). Most knowledge is shared in informal settings (Jones & Jordan, 1998; Pan &
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Arjan de Kok, Bart Bellefroid and Remko Helms Scarbrough, 1999; Truran, 1998). Face‐to‐face communication allows building of trust, which in turn is critical to sharing knowledge (Ipe, 2003).
1.3 Factors that influence knowledge sharing Davenport and Prusak (1998) defined knowledge as ‘a fluid mix of framed experience, values, contextual information, and expert insights that provides a framework for evaluating and incorporating new experiences and information. It originates in and is applied in the minds of knowers’. Ipe (2003) notes: ‘An organization’s ability to effectively leverage its knowledge is highly dependent on its people, who actually create, share, and use the knowledge. Leveraging knowledge is only possible when people can share the knowledge they have and build on the knowledge of others. Knowledge sharing is basically the act of making knowledge available to others within the organization. Knowledge sharing between individuals is the process by which knowledge held by an individual is converted into a form that can be understood, absorbed, and used by other individuals.’ King (2006) defines knowledge sharing as ‘the exchange of knowledge between and among individuals, and within and among teams, organizational units, and organizations.’ He adds there is a difference between knowledge transfer and knowledge sharing: ‘transfer implies focus, a clear objective, and unidirectionality, while knowledge may be shared in unintended ways multiple‐directionally without a specific objective.’ For the purpose of this research we combined three views from the literature on factors that influence knowledge sharing into a unified view or model on knowledge sharing. Each of the three researches is briefly presented after which we present our unified model on knowledge sharing. First of all, Snyder & Lee‐Partridge (2009) found that the level of sensitivity had a major influence on the channel choice for team knowledge sharing and they distinguished the following types of knowledge sharing: 1. Sharing general organizational information; 2. Sharing sensitive organizational information; 3. Sharing general project information; 4. Sharing sensitive project information. Secondly, De Long & Fahey (2000) found that culture impacts the way in which people share knowledge and identified the following types of knowledge sharing:
Vertical interaction with (senior) management,
Horizontal interaction with individuals at the same level in the organization, and
Special behaviour for teaching and dealing with mistakes.
Thirdly, Ipe (2003) noted that the motivation to share is influenced by the trust, power and status of the recipient. Trust evolves from the interpersonal relationship. This means that the level of familiarity with a superior or a colleague influences the motivation to share knowledge. The combination of the before mentioned views on knowledge sharing leads to the following unified model that describes the different types of knowledge sharing: 1,2 Sharing general or sensitive knowledge with the organization at large 3,4 Sharing general or sensitive knowledge with the project team or sub‐unit 5,6 Sharing general or sensitive knowledge with a familiar superior or team leader 7,8 Sharing general or sensitive knowledge with an unfamiliar superior or (senior) manager 9,10 Sharing general or sensitive knowledge with a familiar colleague or team member 11,12 Sharing general or sensitive knowledge with an unfamiliar colleague or co‐worker
2. Research method 2.1 Multiple‐case design In this research three case studies were conducted at large Dutch for‐profit organizations that were all in the transformation process towards NWOW. By choosing organizations that were in the process of implementing NWOW it is possible to make a comparison between employees from the same organization working
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Arjan de Kok, Bart Bellefroid and Remko Helms according to the concepts of NWOW (NWOW workers) and employees that were still working in the traditional way (Non‐NWOW workers). This approach enables both intra‐case (by company) and cross‐case (overall) analysis. In order to observe corresponding findings across the cases, an overall case study protocol was created with the same set of basic set of questions for both groups of workers in all cases (Yin, 2009). As the implementations of NWOW may vary in the emphasis on the different elements of NWOW, and as this may affect the way knowledge is shared, an additional context analysis was performed to gain insight in the way NWOW was implemented each individual case company. The selected organizations for the case studies were: Case 1: ProcessComp, a 22,000 employee multinational with 200 locations in five continents that is active in health, nutrition and materials, Case 2: EnergyComp, a 4,300 employee Dutch Energy DSO (Power Distribution System Operator) company, and Case 3: SoftwareComp, a 15,500 employee international software development and IT support company. Per case company three NWOW workers and three Non‐NWOW workers were interviewed. The case companies were asked to provide a list of participants with a comparable IT experience. Apart from the eighteen case research interviews, additional context interviews were conducted. Case 1
Case 2
3 Non‐NWOW workers
Case 3
3 Non‐NWOW workers
3 NWOW workers
3 Non‐NWOW workers
3 NWOW workers
3 NWOW workers
Embedded Units of Analysis
Embedded Units of Analysis
Embedded Units of Analysis
Context ProcessComp
Context EnergyComp
Context SoftwareComp
Figure 1: Multiple‐case design: 3 cases with 18 participants in total
2.2 Scenarios and channel choice To investigate the way in which knowledge was shared, scenarios were developed, based on our unified model for knowledge sharing. The combinations of the twelve scenarios are shown in Table 1. Table 1: Unified model for knowledge sharing with scenarios Scenarios
General information
Sensitive information
Level
Familiar
Unfamiliar
Familiar
Unfamiliar
Organization
1
2
Project team
3
4
Superior
5
7
6
8
Colleague
9
11
10
12
There were no scenarios defined for the unfamiliar organization and project team or sub‐unit as these are assumed to be familiar to the participant. The scenarios represent different (hypothetical) knowledge sharing situations. For example, the participants were asked: ‘Imagine you find out that on a regular basis items have been stolen from the stockroom where only you and your project members have access to. How would you share this knowledge with your project leader?’ In this scenario sensitive knowledge needs to be shared between an employee and a familiar superior (the project leader). This question was used for scenario 6. The other scenario questions are in the appendix.
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Arjan de Kok, Bart Bellefroid and Remko Helms In the interviews with each of the 18 participants the twelve scenarios were discussed. This means 36 scenarios were evaluated for each group of (Non‐)NWOW workers, and 72 for each case company. In total 216 scenarios were researched. For each scenario the participants were asked what channel they would choose. The (seven) channels defined for this research were: 1. Face‐to‐face 2. Video call 3. E‐mail 4. Phone call 5. Chat message 6. Document sharing system 7. Intranet message The channel list was not exclusive, but appeared to be sufficient for the research. Remark: when a video call was the preferred (and chosen) channel, a face‐to‐face meeting might in practice also be used when both parties happened to be nearby each other in the office. In each scenario the participants were asked whether they would use the channel in a formal or informal way. As discussed the formal use of a channel indicates a purposive knowledge exchange e.g. a planned meeting to share knowledge. The informal use of a channel indicates relational knowledge sharing e.g. an informal mail or unplanned meeting (Rulke & Zaheer, 2000). Finally, for each scenario the participant was asked why this channel was chosen. The interviews were recorded but not transcribed. From each interview the results were combined in a (216 row, multi column) table from which overviews were derived.
3. Research results 3.1 Context of case companies The context of how the three case companies approached the implementation of The New Way of working was researched as it may influence the extent to which NWOW affects knowledge sharing. Case 1: ProcessComp had a structured approach for the implementation of NWOW with open office design, supporting ICT tools and an organizational culture change program. After the initial successful pilot, ProcessComp plans to expand the NWOW concept to other departments. Case 2: At EnergyComp the Human Resource department started the NWOW project in an attempt to attract new talent, as 700 employees are to retire in the coming 5 years. Other goals were the increase of customer satisfaction and lowering of operational costs. The pilot groups received training on several aspects e.g. work‐life balance. Participants received a personal ICT kit. The new office locations and design and the clean desk policy also affected employees that still worked in the traditional way, to show them changes are on the way. Case 3: SoftwareComp chose to partially integrate NWOW and only use those elements that were seen as beneficial to them. The reason for implementing NWOW was the attraction of new talent, reduction of housing cost and improvement of competitive advantage. The NWOW implementation was to grow in an evolutionary way, but due to the economic crisis the project started to lack momentum. The result was that much depended on the individual manager whether flexible and result driven work was possible. The context analysis shows that the implementation in case 3 was less thorough than in the first two cases.
3.2 Effect of NWOW on channel choice Figure 2 shows the channel choice of non‐NWOW and NWOW workers for sharing knowledge on general information.
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Figure 2: Channel choice for sharing knowledge on general information It shows that in all three cases the NWOW workers use more channels than the Non‐NWOW workers. The NWOW workers clearly have less preference for face‐to‐face meetings. In case 1 video calls are even preferred over face‐to‐face meetings, in case 2 this is about equal. The Non‐NWOW workers can also have video calls, as the tool is at their disposal, but they are using it less (case 1) or not at all. The use of e‐mail is about equal in all cases for both groups. In case 3 the NWOW workers prefer phone calls over face‐to‐face meetings and do not make use of video calls. Figure 3 shows the channel choice for sharing knowledge on sensitive information.
Figure 3: Channel choice for sharing knowledge on sensitive information It shows that, similar to the previous figure, also for sharing of knowledge on sensitive information there is a clear difference in channel choice between Non‐NWOW and NWOW workers. In all cases the NWOW workers use a broader palette of channels than the Non‐NWOW workers. The Non‐NWOW workers have a strong preference to meet face‐to‐face when sharing knowledge on sensitive information. The NWOW workers however choose other channels e.g. a video call, e‐mail, chat or phone call just as well. In case 2 the NWOW workers use almost no face‐to‐face meetings when sharing knowledge on sensitive information. In all three cases, in contrast to the Non‐NWOW workers, the NWOW workers use video calls to share knowledge on sensitive information. Figure 4 shows the overall channel choice for sharing knowledge on general and sensitive information across all three cases. The figure shows traditional workers choose to meet more often face‐to‐face or call when sharing knowledge on sensitive information. The NWOW workers use a broad palette of channels in both situations, though e‐ mail is used less for sharing knowledge on sensitive information. This leads to the conclusion that NWOW workers attribute less value to the sharing of sensitive information; they seem to perceive information as something you can share in multiple ways, no matter what the sensitivity of that information is.
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Figure 4: Overall channel choice for sharing knowledge on general and sensitive information
3.3 Effect of NWOW on opportunity to share As discussed in paragraph 1.2, the Opportunity to Share can be formal or informal (Ipe, 2003). Figure 5 shows the preference for formal or informal communication when sharing knowledge on general information.
Figure 5: Formal / informal communication for sharing knowledge on general information In the first two cases the NWOW workers share knowledge on general information more often in an informal way, in case 3 this is the same. Remark: the down going trend over all cases is coincidental and caused by the participants in case 2 and 3 that use more informal communication than in case 1. The differences per case however do matter. Figure 6 shows the preference for formal or informal communication when sharing knowledge on sensitive information. Case 1 shows a clear difference between Non‐NWOW and NWOW workers when sharing knowledge on sensitive information. The Non‐NWOW workers choose to communicate almost twice as much in a formal way (compared to sharing general information, see figure 5), while the NWOW workers have almost no preference for sharing sensitive information in a more formal way than they would share knowledge on general information. Also in case 2 the NWOW workers choose less formal ways to share knowledge on sensitive information, though they are more formal in their behaviour than when sharing general information (figure 5).
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Figure 6: Formal / informal communication for sharing knowledge on sensitive information In case 3 the Non‐NWOW workers are an outlier: they hardly use more formal communication compared to figure 5. Compared to the other cases a larger increase of formal communication was to be expected. The NWOW workers are not an outlier; they use more formal communication compared to figure 5, which is in line with the other cases. The reason why the Non‐NWOW workers in case 3 use relatively less formal communication may be found in the setting of the participants. The non‐NWOW workers at SoftwareComp were located close to each other and their superior. The easiest way to share either general or sensitive information was to walk directly to each other or the superior. Figure 7 shows the formal and informal communication when sharing knowledge on general and sensitive information across all three cases.
Figure 7: Overall formal / informal communication for general and sensitive information For both knowledge sharing situations the NWOW workers use more informal communication than the traditional Non‐NWOW workers. The difference is larger when sharing knowledge on sensitive information, despite the discussed opposite effect of the Non‐NWOW workers in case 3.
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3.4 Reasons for channel choice As mentioned before, the reason for the channel choice is not easy to determine. In this research the question was raised in each scenario for each participant; that is 216 times. The reasons were not pre‐defined but evolved from the interviews and were later categorized to a list of twelve reasons. Figure 7 shows the reasons for channel choice, as mentioned by the Non‐NWOW and NWOW workers, in a ranked order.
Figure 8: Reasons for channel choice (overall) Overall no large differences were found between Non‐NWOW and NWOW workers in their reasons for channel choice. The Non‐NWOW workers have a slightly higher preference for the top three reasons: most personal, best way to forward task and possibility to react directly. The NWOW workers more often prefer not to disturb the receiver by using asynchronous communication. Most personal is the most often mentioned reason for channel choice. The Non‐NWOW workers mention this reason mostly for face‐to‐face meetings, while NWOW workers mention this reason for both face‐to‐face meetings and video calls. Apparently NWOW workers perceive video calls as just as personal as face‐to‐face meetings. Apart from this, patterns in the reasons for channel choice were hard to find.
4. Discussion, conclusions and future research 4.1 Discussion As mentioned in the introduction, research on the New Way of Working is scarce. Comparable literature on the effect of NWOW on knowledge sharing in organizations that are in the process of implementing NWOW cannot be found. The combination of knowledge sharing and the implementation of NWOW make this research a first step in this area. It sheds light on the direction knowledge sharing is heading for in the future, i.e. for those companies that implement NWOW. The results of the research were reasonably consistent; the only outlier was the informal behaviour of the Non‐NWOW workers in case 3. This reflected the informal setting of the participants in their office environment. It also showed that when there are only three participants in a Unit of Analysis, the results are sensitive for local variations. This effect could be diminished by creating larger groups of participants, which is always hard as companies voluntarily invest time in this sort of research. Finally, there is always more information to explore and describe e.g. ‘Do traditional workers share knowledge more formally with superiors than NWOW workers?’ The choice however was made to focus this paper on the channel choice, as this most impacts knowledge sharing in organizations.
4.2 Conclusions and future research The goal of this research was to investigate the effect of the New Way of Working on the sharing of knowledge and channel choice. To do so we developed a unified model on knowledge sharing that identifies different situations, i.e. scenarios, of knowledge sharing. For each of these knowledge sharing situations we identified if
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Arjan de Kok, Bart Bellefroid and Remko Helms the implementation of NWOW changed the knowledge sharing behaviour in terms of channel choice and opportunity to share (formal vs. informal communication). Our multi‐case research shows that:
NWOW workers use a broader palette of channels than Non‐NWOW workers for sharing knowledge on both general and sensitive information;
NWOW workers have less preference for the use of a specific channel in a given situation than Non‐ NWOW workers;
When sharing knowledge on sensitive information, Non‐NWOW workers prefer face‐to‐face meetings. The NWOW workers use less face‐to‐face meetings and more video calls. They note that video calls can also be used in a personal way;
NWOW workers more often share knowledge in an informal compared to Non‐NWOW workers, especially when sharing knowledge on sensitive information.
These findings may very well be related to the geographic separation of workers in a NWOW setting. One of the pillars of The New Way of Working is that employees no longer need to be at the office from 9 to 5 and have the freedom of choosing when to work and where to work. Hence formal meetings and those situations requiring physical presence are less likely to happen, causing alternative channels to be chosen. Research on the reasons for the channel choice did not lead to any clear patterns or findings: there are no dominant reasons by either of the groups to choose a certain channel in a certain setting. In other words, the preference for a certain channel does not change due to the situational circumstances (as described by Carlson & Zmund, 2009). This research shows that the traditional ways of knowledge sharing change by the implementation of NWOW. The practical implication for organizations is that they will need to realize that, because of the New Way of Working, knowledge is shared more often via multiple channels and in more informal ways. Organizations therefore need to invest in a proper technological infrastructure that supports the different preferences of knowledge sharing in a NWOW context. In making a selection for the infrastructure, organizations should realize that knowledge sharing needs a different approach for general and sensitive information and whether the knowledge is shared in a formal or informal way. The unified model and scenarios presented in this research can be used for assessing the needs in the current situation and help to think about future needs taking into account the particular choices that have been made concerning the implementation of NWOW (total implementation or only partial). This research is however only a first exploration: the results should be used with care and more research is needed in the future on the effects of NWOW on knowledge sharing and channel choice to support the findings of this research. More research cases will enlarge the insight in the effects of NWOW. Especially more future research is needed in the way in which knowledge is transferred in a NWOW setting: How are which channels used to share knowledge on which information and why are these channels chosen.
Appendix 1: Scenario questions For the multi‐case research the following scenario questions were defined (see also table 1): 1. Imagine you have discovered a cheaper way of traveling that is applicable to the entire organization. How would you share this knowledge with your organization? 2. Imagine that you have observed an emergency at a client, caused by (a product of) your organization, while you were there. You have an idea on how this could be prevented in the future by your organization. How do you share this sensitive knowledge with your organization? 3. Imagine you know an existing customer has great interest in the project you are currently working in. How will you share this knowledge with your project team? 4. Imagine you discover important project documents, you need as soon as possible for an advice to a client, have gone missing or may have never been produced. How do you share this knowledge with your project team? 5. Imagine you are the first to find out the main stakeholder of the project you are working on intents to withdraw. How do you share this knowledge with your project leader?
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Arjan de Kok, Bart Bellefroid and Remko Helms 6. 7. 8.
9. 10. 11.
12.
Imagine you find out that on a regular basis items have been stolen from the stockroom where only you and your project members have access to. How would you share this knowledge with your project leader? Imagine you see a certain development in your field of expertise which may be of importance to a board member of your organization you do not know personally. How do you share this knowledge with him? Imagine you find out that wrongly salary has been withheld from you and several employees of your organization. The HR employee directs you to his or her superior you do not know. How do you share this sensitive knowledge with someone you do not know personally? Imagine you have a hint for a colleague of your project team to carry out a certain task faster. How do you share this knowledge with your colleague? Imagine that an equivalent colleague in your project team does not take his responsibilities and you know what the consequences may be for him. How do you share this knowledge with him? Imagine that you hear about a new colleague you do not personally know, who is working in another project in a similar role to your project role, and you have several useful tips for him. How do you share this knowledge? Imagine that you just read about a colleague you do not know personally, who is working in a kind of equal role to you in another project, but who, according to you by human error, fails to comply with laws and regulations. How do you share this knowledge with him?
References Baane R., Houtkamp P.,& Knotter M. (2010). The new world of work unravelled – Het nieuwe werken ontrafeld – over Bricks, Bytes & Behavior. 1‐168. Koninklijke Van Gorcum BV. ISBN 9789023245858. Bijl, D.W. (2011). Journey towards the New Way of Working ‐ creating sustainable performance and joy at work. Par CC. ISBN: 978‐94‐90528‐00‐3 Carlson, J.R., & Zmud, R.W. (1999). Channel expansion theory and the experiential nature of media richness perceptions. Academy of Management, 42(2), 153–170. Daft, R.L., & Lengel, R.H. (1984). Information richness: A new approach to managerial behavior and organization design. In L.L. Cummings & B. Staw (Eds.), Research in organizational behavior (Vol. 6, pp. 191 – 233). Greenwich, CT: JAI Press. Davenport, T.H., & Prusak, L. (1998). Working knowledge: How organizations manage what they know. Boston: Harvard Business School Press. De Long, D.W., & Fahey, L. (2000). Diagnosing cultural barriers to knowledge management. The Academy of Management Executive, 14(4), 113‐127. Greene, C., & Myerson, J. (2011). Space for thought: designing for knowledge workers. Facilities, 29(1/2), 19‐30. Ipe, M. (2003). Knowledge Sharing in Organizations: A Conceptual Framework. Human Resource Development Review, 2(4), 337–359. Jones, P., & Jordan, J. (1998). Knowledge orientations and team effectiveness. International Journal of Technology Management, 16, 152‐161. Kluwer. (2011). National Investigation on the New Way of Working ‐ Nationaal Onderzoek Over Het Nieuwe Werken (pp. 1‐ 81). Kluwer Publishing. Retrieved from http://overhetnieuwewerken.nl/whitepapers/nationaal‐onderzoek‐over‐het‐ nieuwe‐werken‐2011 Nonaka, I., & Takeuchi, H. (1995). The knowledge creating company: How Japanese companies create the dynamics of innovation. New York: Oxford University Press. Okhuysen, G.A., & Eisenhardt, K. M. (2002). Integrating knowledge in groups: How formal interventions enable flexibility. Organization Science, 13(4), 370‐386. Pan, S.L., & Scarbrough, H. (1999). Knowledge management in practice: An exploratory case study. Technology Analysis and Strategic Management, 11(3), 359‐374. PWC. (2011). A macro‐economic survey for the effects of The New Way of Working ‐ Een verkennning van macro‐ economische effecten van Het Nieuwe Werken. PricewaterhouseCoopers Accountants N.V. Rulke, D.L., & Zaheer, S. (2000). Shared and unshared transactive knowledge in complex organizations: An exploratory study. In Z. Shapira & T. Lant (Eds.), Organizational cognition: Computation and interpretation. Mahwah, NJ: Lawrence Erlbaum. Schein, E.H. (1985). Organizational culture and leadership. San Francisco: Jossey‐Bass. Snyder, J.L., & Lee‐Partridge, J. (2009). Understanding choice of information and communication channels in knowledge sharing. Proceedings of the International Conference on Information Systems. Phoenix, Usa, 1‐9. Truran, W.R. (1998). Pathways for knowledge: How companies learn through people. Engineering Management Journal, 10(4), 15‐20. Yin, R.K. (2009). Case Study Research. Thousand Oaks , California, USA, Sage Publications.
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Job Evaluation for Knowledge‐Based Organizations Paweł Fiedor Cracow University of Economics, Kraków, Poland s801dok@wizard.uek.krakow.pl pawel@fiedor.eu Abstract: In this paper the author formulates new method for job evaluation and job remuneration for knowledge‐based organizations, which will be essential for competitive organizations in the future. The paper starts with a brief presentation of the standard methods of job evaluation as they have been formulated and used all throughout the twentieth century, contrasting their methodology and essence with the tenets of the knowledge‐based economy. Showing deep incompatibilities between the two allows the author to conclude that these methods cannot be sufficient in the new economy and therefore new approaches are needed if companies are to evaluate work in a satisfying manner. Author presents a few methods of knowledge work evaluation proposed in literature, concluding that they are not sufficient either, due to their insistence on elementary analysis stemming from Frederick Taylor’s work, which is not suitable for highly interdependent knowledge work tasks, and also from their lack of applicability to creating remuneration systems for knowledge workers. Therefore a model for knowledge work evaluation and fixed salaries derivation for knowledge workers is presented, using knowledge resources and knowledge tasks instead of dividing knowledge work into elementary tasks. It is argued that this model will more accurately estimate the impact of particular knowledge worker’s job on the company bottom line and therefore will be a better basis for creation of fixed salaries systems in knowledge‐based organizations. The salary in this model depends on the minimum and maximum salaries derived using market and economic constraints (Dobija 2011). A salary of a particular knowledge worker is located between these two and its exact value is dependent upon the importance of the knowledge tasks and resources he’s working on. A new method for bonus payment system creation is proposed, based on the above model and standard bonus pay system requirements (Ziębicki 2006). Bonus pay is determined by the real impact of a given employee (as opposed to a potential impact of the position measured for the fixed salaries) on the knowledge resources and knowledge tasks he undertakes and on the ROA of the company relative to the plan or another standard candle, such as the economic constant of potential growth (Kurek 2008). A methodology is presented to provide an empirical way of implementing these methods in knowledge‐based organizations. The author also discusses intermediary solutions, which can be useful for organizations which aren’t yet fully based on knowledge. Arguably as more organizations become increasingly dependent on knowledge in their operations systems allowing them to evaluate work on particular positions and devise fixed salaries and bonuses in a scientific manner will be essential to maintaining procedural excellence and competitive advantage. This paper presents one of the first ways to do it in a manner consistent with current state of knowledge in economics and management science. Keywords: job evaluation, knowledge workers, pay systems
1. Job evaluation methods Heneman (2003) presents a short literature review of job evaluation methods, most of which are merely useful for the vast number of very small organizations (Nicolae & Constantinescu 2011). However in knowledge‐ based economy with rapidly changing economic environment more sophisticated methods are necessary for the development of modern organizations. The simplest method of job evaluation consists in creating a ranking based on subjective assessment of the examiners as to the importance of a given job for the results of the organization (Knott 1983). In a similar approach the examiner finds the simplest job performed in an organization and compares it with all the other jobs, ranking them by their difficulty (Martyniak 1988). A more precise ranking can be obtained using pairwise comparisons. It is worth noting that each position should be assessed separately (Milkovich, Newman & Gerhart 2010). The above methods assess the difficulty of a job based on the knowledge, experience and intuition of the examiner. Analytical methods, on the other hand, use specific criteria, which makes them harder to employ, but also yield more accurate and objective results (Martyniak 1988). These are described below. Evaluation using market data may be an interesting approach, but the effectiveness of this method is heavily dependent on the obtained sample (Heneman & LeBlanc 2003). Another simple method consists in creating a classification based on various factors. Every level in such classification has a different pay grade, the number of levels is usually insufficient however (Jensen 1978).
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Paweł Fiedor There exist methods based on examining one or more factors, in which the scale is not as granular as in the above‐described classifications. Best results can be obtained by examining multiple factors (Dufetel 1991). These methods are most commonly used for job evaluation since the times of Adam Smith. A significant increase in the scale of economic activity after the industrial revolution, coupled with the division of labor initiated by the thoughts of Adam Smith (Smith 1776), and implemented by the effort of first management consultants led by F.W. Taylor (Martyniak 1996), and the consequent diversity in the value and difficulty of jobs performed within individual positions made it necessary for the value of these jobs to be determined. Such analyses not only allow for the variability in compensation, but also for the optimal distribution of work between positions within an organization. Traditionally job difficulty analysis is based on a few synthetic criteria (Mayre 1949). These criteria were first systematized for job evaluation by Charles Bedaux (Wibbe 1961). Criteria in later methods were similar, usually (Martyniak 1985):
Knowledge and work experience,
Physical and mental requirements,
Responsibility,
Environmental conditions.
Among analytical methods based on criteria similar to above one can enumerate the Bedaux system (Mayre 1949), the Hagner‐Weng method (Hagner & Weng 1951), the Sulzer method (Jasinski 1999), the Yugoslavian method (Martyniak 1998), the Kordaszewski method (Martyniak 1987), the Hay method (Czajka, Jacukowicz & Juchnowicz 1998), the AWP method (Oleksyn 1990) or the UMEWAP‐85 (Martyniak 1996, Martyniak 1997). A general methodology of analytical job evaluation was described by Martyniak (1988):
Preparation of the workplace,
Preliminary analysis of the work,
Choice of the analytical method,
Development of job descriptions for the examined positions,
Valuation of all examined positions,
Determination of wages through the valuations.
2. Knowledge‐based economy and organizations The above‐described methods have been developed in the first half of the 20th century. They are not ahistorical, so it seems that better methods should be developed for the new economy (Witzel 2012). The concept of knowledge‐based economy refers to an economy "in which knowledge as such becomes a more important factor determining the pace and level of economic development than the tangible inputs" (Zienkowski 2003). In such an economy knowledge allows for a faster economic development than was possible without using knowledge in a complex manner (Drucker 2000). Knowledge‐based economy consists of knowledge‐based organizations, which use knowledge or intellectual capital as their most important resource. As management processes concentrate on knowledge, knowledge strategies and knowledge management strategies have a leading role in knowledge‐based economy (Zack 1999). In such an economy one finds many learning organizations, but there also exist other concepts such as intelligent organizations. All those differ from classic organizations in many respects (Mikuła 2006). Learning organization as a concept has been first developed by P. Senge, who defined it as an organization which "constantly widens the possibilities of creating its own future" (Senge 1998, Argyris 1999, Schön & Argyris 1996). Learning organization effectively performs knowledge functions and processes, quickly responds to internal changes and changes in the environment by modifying its behavior (Garvin 1998). At present there are not many very well‐defined rules governing the practice of managing such organizations, therefore there are not many very specific methods and techniques. In particular, there isn't a well‐developed job evaluation methodology for knowledge‐based organizations. In knowledge‐based organizations an essential role is being
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Paweł Fiedor played by knowledge workers, a category created by T.H. Davenport describing workers with a high degree of knowledge, excellent education or experience, whose work is heavily based on knowledge (Davenport 2005). Such workers are significantly different from the classic labor force, and therefore the methods of assessing their work must be different.
3. Knowledge work evaluation methods Below the author has described two best‐known approaches to knowledge work evaluation, and provided a reference to a few more methods, as to present the state scientific knowledge on the topic. Yuri V. Ramirez and Harry J. Steudel have created knowledge work quantification framework (KWQF) (Ramirez & Steudel 2008), which in order to assess the level of knowledge work saturation (KWS) uses four principles and eight dimensions of knowledge work (KW). Model describes the KWS on a continuum, and eight dimensions may be used to distinguish between knowledge and manual work:
Autonomy.
Structure.
Tangibility.
Knowledge.
Creativity and innovation.
Complexity.
Routine and repetitiveness.
Physical effort.
This model defines knowledge‐based work in relation to the four main principles of such work. The first states that the knowledge‐based work can be described on a continuum that ranges from 0 to 100% of knowledge saturation. This model is therefore based on the fuzziness of KWS (Taylor 1998, Kelloway & Barling 2000, Ramirez & Steudel 2008). The second principle states that knowledge worker is not determined by what he is, but by what he's doing. The third principle states that a work is determined by the tasks that make up this work. Therefore the model is based on analytical approach. The fourth principle states that the dimensions of KW can be used to determine the saturation level of knowledge in the task. KWQF combines these principles and dimensions, so as to create an assessment of knowledge work saturation. This result is determined by examining the level of knowledge saturation of the work performed by the employee, through examining the individual tasks performed by that employee. Model therefore suggests that before identifying the KWS of the work the KWS of individual tasks within the job should be identified. Summation of these separate assessments weighted by the percentage of time each task takes provides a measure of KWS of the job. The methodology of this method is described below:
Determination of the type of tasks to be examined and employees performing them as objects of the study.
Determination of the sample size of workers performing given kinds of knowledge work chosen in step 1.
Indication of the period for which the work is analyzed (usually a year).
Selection of one of the employees among the sample and performing steps 5 and 6.
Filling the KW structure matrix for an employee by putting in average percentage of working time spent on each task and the evaluation of each of the eight dimensions for every task on a scale of 1 to 5.
Calculation of KWS using the matrix. KWS is calculated as the sum of products of the percentage of time a given task takes and partial KWS for the task. These are summed over all the tasks within the job.
Calculation of KWS for other employees by repeating the steps 5 and 6 for all the other employees.
Determination of KWS for other positions by repeating main steps (1 to 7) for all the different types of work analyzed.
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Paweł Fiedor This results in a number between 1 and 5, which may be represented on the knowledge work continuum by standardizing it to percentage points. This model is useful for determining the intensity of knowledge usage in jobs performed within a company, but is not very useful for determining the wages of knowledge workers, as for most organizations such an assessment will not be a good estimator for the effect a given job has on the bottom line of the organization. Heidary Dahooie Jalil, Abbas Afrazeh and Seyed Mohammad Hosseini Moathar created a model based on five principles (Dahooie, Afrazeh & Hosseini 2011):
All jobs can be defined as knowledge work. Each work can be presented on a continuum that ranges from 0 to 100 percent of KWS.
Knowledge‐based work is defined by the tasks that make up this work.
Every knowledge work includes three types of tasks (job‐specific tasks, building and maintenance of knowledge, work management tasks). Activities connected with these tasks can be divided into three classes (based on knowledge, based on communication and supplementary).
Knowledge intensive (KI) and communication intensive (CI) activities differ. In addition, each activity has different levels of knowledge and communication intensity.
On the basis of the proportion of the total time required and the importance of the tasks, two types of weights are assigned to each task when calculating JKIS (job knowledge intensity score) or JCIS (job communication intensity score).
In this model, two results are calculated to represent the KI and CI of each job. Based on the principles 2 and 3 knowledge work is assessed quantitatively in two dimensions. These dimensions are JKIS and JCIS. JKIS and JCIS are calculated based on the intensity of knowledge‐based activities (KBA) and communication‐based activities (CBA). The methodology of this method is presented below:
Use of appropriate methods of work analysis to extract specific tasks making up the job;
Determination of the proportion of time devoted to each task, its importance (as an integer between 1 and 7), and its type (knowledge or communication‐based task);
Analysis of the tasks extracted in the previous steps to determine their necessity based on O*NET database (Jeanneret and Strong 2003);
Determination of complexity weights for every task;
Determination of importance weights for every task (from 1 to 7, according to O*NET guidelines);
Assessment of knowledge and communication intensity of a given work as a supremum of the set of partial assessments for knowledge and communication‐based tasks.
The literature review would not be complete without mentioning models created by Shi‐you (2008), Wang (2008) as well as Lee, Lee & Kang (2005), but due to the fact that they do not contribute anything significantly different from the models described above when it comes to knowledge work evaluation these will not be presented in detail.
4. Resource and task‐based job evaluation model for KW In the words of Jay L. Brand: "problems arise because in contrast to the classic production process, work based on knowledge cannot be easily divided into behavioral components which can be scheduled to provide the best segmentation and integration of the component tasks" (Brand 2005). Indeed, the approaches described above are, to a large extent, based on elementary analysis of F. Taylor (Taylor 1911), while the work based on knowledge isn't easily definable as the sum of its components (even though Taylor’s thoughts are useful in knowledge management sensu largo (Fiedor 2012, Paton 2013)). In particular, the value of knowledge work cannot be estimated as the sum of the values of its component tasks. Therefore a job evaluation model for knowledge‐based organization, presented below takes into account the resources of knowledge used within the job and the strategic and operational functions of knowledge that are performed within the job, as static and dynamic sides of knowledge‐based work. Additionally the model described below, in contrast to the above, is used to directly evaluate knowledge‐based work in units of work, and thus determine the
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Paweł Fiedor appropriate size of payroll. In this model the author agrees with the classic assumption that wages depend on the impact of the job on the results of the company, and not personal characteristics of the worker (Martyniak 1988). In the proposed model salary for a given position depends on the minimum salary or the basic salary in the organization, defined as the salary for the simplest job, and on the knowledge the position uses as well as the knowledge‐related functions or tasks which are performed on the job. And so the basic salary for a given position can be described by the formula:
,where: – salary on position , – minimum salary, – maximum salary, ,
– binary variable, where 1 means that on position task on knowledge is performed, – a coefficient describing the importance of resource or task, where 5 denotes the most important ones, – is a set of positions being evaluated, – is a set of knowledge resources in the organization, – is a set of knowledge‐related tasks performed in the organization. Salaries in the above definition are measured in units of work. To measure salaries in currency instead (currency is a smaller unit of work (Dobija 2011)) the assumptions should be changed so that: The problem of the minimum and the maximum salaries can be solved in various manners. It is postulated, after M. Dobija, to set the minimum salary using the cumulative cost of human capital for a person with no experience, and for the qualifications and experience needed for the hardest work for the maximum salary (Dobija 2011). Minimum and maximum salaries can also be a result of market conditions (Martyniak 1999). In addition to indicating the minimum and maximum salaries it is necessary to assess both coefficients and the occurrence of tasks and functions for all tested positions, which is the main component of job evaluation in the presented model. Occurrence of functions and resources is modeled with the binary variable, and the value of the coefficients may be determined on any scale. The selected scale should be simple to use and easy to interpret (a scale between 1 and 5 is proposed). The first coefficient relates to knowledge resources in the organization. To calculate it for different positions one should first develop a map of knowledge in the company, including all available knowledge resources in the organization (Lee & Fink 2013). Knowledge maps construct a relatively well‐defined category, created by M. Eppler (Eppler 2006). Such map is never complete (Wexler 2011), but it needs the assignment of importance, and assignment of the positions in which it is utilized. The second coefficient describes the dynamic part of knowledge‐related functions or tasks performed within a position. Therefore one should make a list of the knowledge‐related tasks and processes. A sample list may be found in (Maier 2002). Examination of the two coefficients may be performed on the same matrix, it is merely separated here for clarity of description. These tasks have to have ranks assigned to them. These should be assigned based on the knowledge strategy of the organization and other necessary reasons. This will allow for the determination of wages for all examined positions in accordance with the formula set forth above.
5. The methodology for the model To sum up a methodology for job evaluation using the described model is presented below:
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Paweł Fiedor
Determination of the work to be studied. It should include all the positions in the examined organization;
Determination of the minimum and maximum salary for the organization in accordance with the external requirements and internal policies of the organization;
Creation of a knowledge map of the organization, and as a result also the list of knowledge resources in the organization as the rows of the assessment matrix;
Specification of all the knowledge‐related tasks in the organization, represented as the columns of the assessment matrix;
Assessment of the importance of each knowledge resource appearing on the knowledge map on a scale of 1 to 5;
Assessment of each knowledge‐related task present in the organization on a scale from 1 to 5;
Determination of which combinations of tasks and resources are performed at each position, therefore a determination of all
;
Presentation of the data in a matrix, and insertion of the importance of performing a given task on a given resource at the intersection of the resource and the task;
Derivation of salaries for all examined positions, according to the model presented above.
6. Intermediary solutions Not all companies are based on knowledge. Organizations use knowledge to a greater or lesser extent, but in practice no organization does so perfectly (Mikuła 2006). Thus, in most organizations, the above method may not be optimal. It is recommended that in any modern organization the use of knowledge is accounted for in the valuation of work however. In companies where knowledge is of minor importance, such assessment may be based on other methods combined with the above‐presented model. This may be done either by performing both methods, and using the average of the two assessments, or by using the presented model as an additional category added to the conventional methods.
7. Bonus pay for knowledge‐based organizations When examining salaries in knowledge‐based organizations one should also look at methods of determining bonus pay. Bernard Ziębicki (Ziębicki 2006) presented the basic principles of bonus pay system that are used as a guideline for the below bonus pay model for knowledge‐based organizations. Accordingly, for the determination of bonus pay one has to determine the bonus fund which will be partitioned among the employees. The following model assumes annual bonuses, but the general principle remains the same for a monthly bonus. The bonus fund for the year being examined (reporting year) is defined as:
where:
– bonus pay fund; – salary of employee x in the examined period; – return on assets; – average ROA in well‐managed firms under normal conditions, 8% according to research by B. Kurek (Kurek 2011). ROA can be exchanged for another measurement of the main objective of the organization. Replacing ROA with another measurement means that p will be replaced by a plan for that measurement. 40% in the formula above is following B. Ziębicki's advice that the bonus pay should not exceed this level. Thus it is evident that the bonus pay fund is ranging within 0 and 40% of the fund for basic salaries, and its value depends on the profitability of assets in relation to the return on assets in the average conditions for well‐
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Paweł Fiedor managed companies (or in relation to the plan). So the calculated bonus fund must then be divided, so as to obtain specific knowledge workers bonuses. Bonus pay of a given knowledge worker (χ) is calculated as follows:
where:
– weights assigned to the importance of the influence the worker has on the knowledge resources and tasks, and his basic salary. is following from B. Ziębicki’s rule saying that basic salaries should be included in the determination of bonus pay. It is postulated to use and These values are resulting from an assessment which should be performed every year to keep the salaries appropriate in the changing environment. The changing importance of knowledge‐related resources and tasks allows the organization to see the influence of particular employees. The coefficients used to determine bonus pay are relative, therefore creating even more competition within an organization. Additionally a level below which a bonus pay will not be paid can be set, so that bonus always has a meaningful value. Then: is a boundary below which a bonus is not paid. This condition can be specified as a percentage of Where basic salary, then: is a minimum bonus percentage in relation to basic salary, which qualifies the bonus to be Where paid. It can be noted that bonus is bounded by 0, so that the workers cannot be punished for results below expectations.
8. Summary and further research Studying classic methods of job evaluation gives one an opportunity to observe the lack of knowledge in the modern sense in these. Thus there is a need to find new solutions for the valuation of work in organization in the 21st century (Davenport, Thomas & Cantrell 2002). Several methods of knowledge‐based work evaluation are largely based on elementary analysis of Taylor (Taylor 1911), which is incompatible with knowledge‐based economy. Therefore a model for the job evaluation in knowledge‐based organization has been presented, equipped with different characteristics. Further research should focus on the practical problems in using this method and more accurate mapping methodology for describing knowledge and defining the tasks relating to knowledge, as well as determining their importance for the company for the purposes of this method.
References Argyris, C. (1999), On Organizational Learning, Blackwell Business, Oxford. Brand, J. L. (2005), Rewarding knowledge worker productivity beyond the corner office. Haworth employee engagement white paper. Czajka, Z., Jacukowicz, Z. & Juchnowicz, M. (1998), Wartościowanie pracy a zarządzanie płacami, Difin, Warszawa. Dahooie, J. H., Afrazeh, A. & Hosseini, S. M. M. (2011), ‘An activity‐based framework for quantification of knowledge work’, Journal of Knowledge Management 15(3), 422–444. Davenport, T. (2005), Thinking for a living, Harvard Business Press, Boston. Davenport, T., Thomas, R. & Cantrell, S. (2002), ‘The mysterious art and science of knowledge‐worker performance’, MIT Sloan Management Review 44, 23–29. Dobija, M. (2011), Kapitał ludzki w perspektywie ekonomicznej, Wydawnictwo Uniwersytetu Ekonomicznego w Krakowie, Kraków. Drucker, P. (2000), Zarządzanie w XXI wieku, Muza SA, Warszawa. Dufetel, L. (1991), ‘Job evaluation: Still at the frontier’, Compensation & Benefits Review 23(4), 53–67.
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Towards a Decision Approach for the Characterization of Potential Sahar Ghrab1, 2, Ines Saad2, 3, Faiez Gargouri1, and Gilles Kassel2 1 MIRACL Laboratory, High Institute of Computer Science and Multimedia, Sfax, Tunisia 2 MIS Laboratory, University Of Picardie Jules Verne, Amiens, France 3 France Business School, Amiens, France ghrab.sahar@gmail.com ines.SAAD@france‐bs.fr faiez.gargouri@gmail.com gilles.kassel@u‐picardie.fr Abstract: This paper discusses the issue of characterization and evaluation of potential crucial knowledge in medical field. We are interested almost in medical know‐how. The aim of the paper is to propose a preference model based on DRSA approach in order to characterize this kind of knowledge known as “potential crucial knowledge” which can be validated in the short or medium term and is considered provisionally realistic by an actor at least or presumed by the study men for the decision support. We reuse the method for identifying crucial knowledge already tested in the automobile field and we adopt it in the medical field. We propose to build a new decision class for potential crucial knowledge which is located between two classes: Cl1 for non crucial knowledge and Cl2 for crucial knowledge. The decision classes are ordered by preference. The characterization of the new class is established using the criteria previously used to characterize the two other classes. These criteria are measured in terms of vulnerability, role of knowledge and duration of use of the knowledge. This class has been tested in the organization of protection of motor‐disabled of Sfax‐Tunisia (ASHMS). The aim of this society is to take early care of a handicap affected by a cerebral palsy (IMC). This global process contains many medical examinations. In this paper, we are interested in physiotherapy examination. Keywords: knowledge capitalization, medical knowledge, decision class, potential crucial knowledge, decision rules, tacit knowledge
1. Introduction The knowledge‐based View exploits a new orientation for organizations which is not to have better resources but to know more accurately the relative productive performances of those resources (Grant, 2009). This type of knowledge is known by intellectual capital. It is considered as a source of innovation and novelty used to create competitive advantages for organizations in the era of knowledge where human beings must rely on intellectuality, intuition and creativity (Parpandel, 2013). In fact, the intent is to know how to coordinate, share, generate and integrate the suitable knowledge in the suitable time and in the suitable context (Tow and al., 2012). Different types of knowledge are present in the organization: explicit knowledge which can be articulated and easily communicated between individuals and organizations and tacit knowledge (skills, know‐how, and contextual knowledge) which is manifested only in its application, transferring it from one individual to another (Nonaka and Krogh, 2009; Kogut and Zander 1992). From the epistemological point of view, philosophers distinguish between three types of knowledge: (i) knowledge‐that which is expressed in declarative sentences or indicative propositions, (ii) knowledge by acquaintance which involves a relation between a subject and some entity or feature of the world that is either a truth maker, or a constituent of a truth maker and (iii) knowledge How which determines how to accomplish or to do something (Fantl, 2012). Considering the amount of knowledge to be preserved in different fields, the organizations are brought to engage a reflection in order to locate the knowledge which should be the object of capitalization. In fact, capitalization is the formalization of experience gained in a specific field. Its principal purpose is to locate and make visible the enterprise knowledge, be able to keep, access, actualize, know how to diffuse and better use, put in synergy and valorize it (Viale, 2010). Many works are proposed in the literature dealing with knowledge capitalization. We present (Berkani and Cheikh, 2012) for knowledge capitalization within Communities of Practice of E‐learning (CoPEs), (Le Bellu, 2012) for capitalization and transfer of professional explicit and tacit know‐how embodied in professional gesture, (Okunoyet and al. , 2010) for annotation model to knowledge capitalization and (Marcandella and al., 2009) for past projects memory for knowledge capitalization. This is particularly true in the medical field. In fact, knowledge management in general and knowledge capitalization in particular have a great importance in knowledge which is a vital strategic resource and a key factor for
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Sahar Ghrab et al. modern healthcare. Healthcare knowledge management (HKM) can be characterized as the systematic creation, modeling, sharing, operationalization and translation of healthcare knowledge to improve the quality of patient care. «The goal of HKM is to promote and provide optimal, timely, effective and pragmatic healthcare knowledge to healthcare professionals (and even to patients and individuals) where and when they need it to help them make high quality, well‐informed and cost‐effective patient care decisions» (Abidi, 2008). In fact, medicine is in a pressing need to organize, choose and identify the most crucial, important and interesting knowledge. In account for the complexity of medical knowledge, its rapid increasing and its enormous tacit and explicit knowledge, the capitalization in this field is important. Nowadays, the capitalization emerges to take into account tacit knowledge which is difficult to encode, identify and transfer (Pivar and al., 2012). This knowledge includes know‐how, skill, competence and even knowledge which can be hardly explicit. The awareness of organizations about tacit knowledge and especially about know‐how is rapidly increasing. This is justified by looking for keeping traces of experience, expertise, know‐how and collective know‐how in order to reuse, to renew and not to lose this intellectual capital embodied in human gesture and process learning which is considered a wealth for organizations. This awareness about the importance of tacit knowledge in general and know‐how in particular, affects the medical field (Kinchin and al., 2008; Nelson and al., 2011; Panahi and al., 2012). In fact, the identification of crucial know‐that and know‐how is being more and more overwhelming. Consequently, this improves the care quality and helps healthcare professionals to make the right clinical decisions in complex circumstances. Indeed, several methods have been proposed by healthcare knowledge management. These methods are classified through subjects treated and users’ and physicians’ needs. We distinguish mainly works dedicated to medical ontology (Dhombres and al., 2011), medical application (Chniti and al., 2012; Minutolo and al., 2012), integrating user preference (Flores and al., 2011), healthcare approach and framework (Pérez‐Gallardo and al., 2013; Bordoloi and Islam, 2011) and representation of medical knowledge (Kamsu‐Foguem and al., 2013). (Dhombres and al., 2011) propose an ontology OntoOrpha to support the editing and audit of knowledge of rare disease in Orphanet. This ontology supports efficiently the edition, update and data sharing process demanded by a constantly growing rare diseases knowledge. It facilitates a better visualization of the knowledge base and improves classification and annotating editing procedures. (Chniti and al., 2012) present an application of pharmaceutical validation of medication order based on an OWL ontology and clinical decision rules. For (Minutolo and al., 2012), they propose a pattern‐based knowledge editing system to guide and assist the creation and formalization of condition‐action clinical recommendations to be used in knowledge‐based Decision Support. (Pérez‐Gallardo and al., 2013) propose an iPixel Recommender Engine for recommending mammographic evaluations. For (Bordoloi and Islam, 2011), they develop a framework tounderstand the impact of Knowledge Management practices in healthcare delivery. Finally, (Kamsu‐Foguem and al., 2013) propose a conceptual graph‐based knowledge representation for supporting reasoning in African traditional medicine. Most of types of knowledge cited in the previous works are explicit through medical records, Knowledge Management practices in healthcare delivery, business rules, medical diagnosis, practice medicine, routine medical decision and formal requirements (Bordoloi and Islam, 2011; Minutolo and al., 2012; Pérez‐Gallardo and al., 2013; Kamsu‐Foguem and al., 2013). The medical documents and guides are, with doubt, important in this field whereas tacit knowledge is more important and more insightful (Le Bellu, 2012; Panahi and al., 2012; Henry, 2011). Because of the tacit dimension of some medical knowledge embedded in experience, physician skills, competences and know‐how are threatened to disappear and to be lost. « Michael Polanyi’s theory of tacit knowing is advanced as the basis for developing a more accurate understanding of medical knowledge. (…) The implications of recognizing tacit knowing in medicine and medical decisions (…) include the ability to explain the importance of the clinical encounter in medical practice, mechanisms for analyzing patient and doctor as persons, and the need for humility given the uncertainty that the tacit dimension injects into all medical decisions» (Henry, 2010). Taking into account the progress of medicine, looking for a pathway for the nature of disease pathologies especially scarce diseases, and the medicine professionals’ and users’ needs, healthcare organizations and associations are becoming aware of tacit knowledge. «Healthcare professionals’ tacit knowledge is the most
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Sahar Ghrab et al. valuable source of their experiential know‐how acquired in critical situations of patient management. Indeed, it is about what really works and how to make it work rather than explicit knowledge of how things should work» (Abidi and al. 2005). Although, most works led to facilitate the use of medical information, to access to electronic medical records, to build medical database and application, few recent works are developed aiming to highlight the tacit dimension of medical knowledge (Panahi and al., 2012; Saxell, 2013; Henry, 2012). The work of (Panahi and al., 2012) examines the contributions of social media to facilitate tacit knowledge sharing among physicians. For (Saxell, 2013), he highlights the value of the physician’s tacit knowledge and social learning in medical treatment. In addition to the tacit dimension of knowledge which is our first worry in this article, the study of knowledge under validation and development is our second worry. This type of knowledge can be validated in the short or medium term. Due to the new knowledge created, it is momentarily difficult to study its validation which is embodied mainly in innovative projects. In fact, it is interesting to propose a method or an approach to study the knowledge which can be validated in the short or medium term. This method characterizes potential crucial knowledge. As for us, we propose a preference model of decision makers in order to characterize a new decision class called “potential crucial knowledge” which is based on the work of (Saad, 2005). We rely on the DRSA approach (Dominance‐based Rough Set Approach) (Greco and al., 2002) for defining preference models of decision makers which characterize the decision class Cl2. The new build class aims to highlight the importance of know‐how in medical field and to endeavor the necessity of capitalization of knowledge which is under validation and experimentation. This class was tested in the organization of the Protection of motors‐disabled of Sfax‐Tunisia (ASHMS). Its aim is to facilitate the early treatment of children affected by a Cerebral Palsy which is a scarce disease. We are mainly interested with the analysis of the physiotherapy process related to the global process of early treatment of children having cerebral palsy. This process is already mentioned by Turki and al (2012a) as a sensitive process. The plan of the paper is proposed as follows: In Section 2, we present, on the one hand, the background of our method and the specificity of our method on the other hand. This method adds a new decision class related to potential crucial knowledge. In Section 4, we introduce the application context (ASHMS) of our research, where we validate our method and we present some evaluation results of the application of our preference model. To conclude, we provide a general overview of the contributions proposed in this paper and we present the research perspectives hopefully to be achieved in the future.
2. Background In this section, we present the methodology for identifying crucial knowledge (Saad, 2005). This method is adopted to build the preference model of decision makers for identifying potential crucial knowledge. The method for identifying crucial knowledge classifies knowledge into two decision class (Cl1: non crucial knowledge and Cl2: crucial knowledge) whereas our proposed method classifies knowledge into three decision class (Cl1: non crucial knowledge, Cl2: potential crucial knowledge and Cl3: crucial knowledge. We define potential crucial knowledge as knowledge which can be crucial in the short or medium term and can be identified from organization processes. This type of knowledge is related to new projects or processes and is not still mature. In this paper, our research is limited to expose the preference model of decision makers related to new decision class added. The method that we present to identify potential crucial knowledge, is composed of two phases (see Figure 1) like the method of (Saad, 2005): (i) construction of the preference of decision makers and (ii) classification of potential crucial knowledge. The term “potential crucial knowledge “refers to “potential action” as defined by (Roy, 1985) in the multi‐criteria decision making. It represents a real or virtual action considered by at least one decision maker as a realistic one. Thus, a “potential crucial knowledge” is a knowledge that has been identified as a potential crucial knowledge by at least one decision maker. The first phase determines the decision rules based on the preference model of decision makers. It establishes a set of «reference knowledge» that are examples of learning. The reference knowledge is related to the decision to assign the organization’s knowledge to one decision classes. We distinguish three decision classes
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Sahar Ghrab et al. as follows: (i) Cl1 the decision class for "non crucial knowledge", (ii) Cl2 the decision class for "potential crucial knowledge" and (iii) Cl3 the decision class for "crucial knowledge ". Cl1 refers to knowledge which is not necessary to capitalize. Cl2 refers to knowledge which can be beneficial to capitalize in the short or medium term and Cl3 refers to knowledge which is necessary to capitalize.
Figure 1: Method of characterizing potential crucial knowledge
2.1 Phase 1: Construction of the preference model This phase is composed of four steps: (i) identification of a set of “reference organization’s knowledge”, (ii) depth analysis of reference knowledge, (iii) building a criteria coherent family and evaluation the knowledge among this criteria (iv) inference of decision rules. The second step is to identify in‐depth knowledge by studying the cost, the complexity and the duration of use. The third step builds a coherent set of criteria to evaluate knowledge. The fourth step is to infer the decision rules collectively accepted by decision makers. It classifies knowledge into three decision classes already mentioned at the beginning of this section: Cl1, Cl2 and Cl3. 2.1.1 Identification of a set of reference knowledge Considering the large amount of knowledge used in a project or organization’s process, it is too difficult and expensive to analyze and evaluate the whole amount. That‘s why, the decision maker chooses a learning sample representing the set of “reference knowledge”. This sample includes sufficient representative examples for each decision class. The identification of this set is based on the GAMETH framework which is composed of four steps: (i) a description of the organizational model, (ii) definition of sensitive processes, (iii) modeling and analysis of sensitive process and critical activities associated with each process and (iv) identification of sources of knowledge and its location. 2.1.2 Depth analysis of reference knowledge We characterize knowledge according to a set of criteria (depth, scarcity, explicit/tacit, validation level, quality, effectiveness, transferability and etc). For each criterion, the analysis must be in‐depth in order to choose the suitable criteria. Consequently, it is preferable to ask questions to actors to verify if knowledge is specific to the organization. 2.1.3 Building a criteria coherent family and evaluation of reference knowledge Building a criteria coherent family is done according to three points of view: (i) ontological point of view which determines criteria related to knowledge characterization to study its vulnerability, (ii) generic point of view which predicts use duration of organization knowledge according to the goals in the medium or long term and (iii) functional point of view which determines the knowledge contribution degree to the organization’s goals. The model for measuring the contribution degrees of the organization’s knowledge has been detailed in (Saad and al., 2009). This degree is calculated through three steps. First, we calculate the degree of contribution of knowledge to the process. Then, we calculate the degree of contribution of each process to each object.
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Sahar Ghrab et al. Finally, we calculate the degree of contribution of each project to each organization’s goal. 2.1.4 Inference of decision rules In this step, we propose an iterative procedure allowing the inference of decision rules collectively accepted by decision makers. The different steps of this procedure are represented in Figure 2.
Figure 2: Procedure of decision rules inference The first step defines a set of "reference knowledge" and three decision classes (Cl1, Cl2, and Cl3). The second step affects the reference knowledge to the appropriate decision class and builds the decision table in collaboration with decision makers. Using the DRSA method, we infer decision rules and we verify the inconsistency of each rule. The decision table contains all reference knowledge, all criteria and decision taken. This decision represents the affectation of each knowledge to the suitable decision class according to decision makers (Cl1, Cl2 or Cl3). An example of decision table is represented in Table 1. Table 1: Schematic representation of the decision table Connaissances de référence K1 … Kn
Critères g1 ……………………….…gm
Décision d1………………………….….dj
f(K1, g1)…………...f(K1,gm) Cl1/Cl2/Cl3…….....Cl1/Cl2/Cl3 … … f(Kn,g1)…………….f(Kn,gm) Cl1/Cl2/Cl3……….Cl1/Cl2/Cl3
2.2 Phase 2: Classification of potential crucial knowledge In this phase, we use the preference model of decision makers already built in the first phase. In fact, we classify new knowledge into three decision classes: Cl1 "non crucial knowledge", Cl2 "potential crucial knowledge" and Cl3 "crucial knowledge". The new knowledge is produced by study project, development project, organization process or innovative process. Like the first phase, this second phase is composed of four steps. The first identifies knowledge which can be the object of capitalization operation known as "potential crucial knowledge". The second analyzes in‐depth the new knowledge. The third evaluates the knowledge among the criteria already mentioned in the first phase. Finally, in the fourth step, we affect each knowledge to its appropriate decision class (Cl1, Cl2 or Cl3).
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3. Experimentation The context of our research is the project EGIDE/CMCU. The scientific partners of this project are ISIM (Higher Institute of Computer Science, University of Sfax) and MIS (Modeling, Information and System Laboratory, University of Picardie Jules Verne). The social partner is the association of protection of motor‐disabled of Sfax –Tunisia (ASHMS). The project title is “Development of a crucial knowledge support system for improving the medical and social care of motors‐disabled. The work of (Turki and al., 2012a) is integrated in this project. It proposes a multi‐criteria method and a core ontology for organization process. It aims to support and models the identification of sensitive process. In fact, our proposed method represents a continuation of the method of (Turki and al., 2012a) and a supplement to the identification of crucial knowledge and the medical support system of motors‐disabled.
3.1 Case study The preference model already defined in the previous section has been tested in ASHMS. The association has different levels of process. We are particularly interested in the early care of the disabled children affected by a cerebral palsy. In this paper, our purpose is to complete a research project already elaborated by (Turki and al., 2012a). Their work consists in proposing a method for identifying sensitive process. Thus, a “sensitive process” is a process that «mobilizes crucial knowledge, which is considered as immaterial resource. This process must contain at least one activity which mobilizes some tacit knowledge held by a very small number of experts or is poorly mastered to solve critical problems» (Turki and al., 2012b). Turki and al. (2012a; 2012b) identify only sensitive processes of the organization because it is too difficult to analyze whole processes. It represents the most important processes in the organization. The cerebral palsy is a scarce disease. Several researches have been triggered off to study this disease, its causes, its consequences and the maturity level of knowledge used. In addition to that, our research is led to study its specificity, its knowledge, its process, its innovative research and its innovative products related to cerebral palsy. This can facilitate the understanding of this disease. The care process is composed of several actions in terms of medical and paramedical consultations in different specialties. Among these specialties, we quote: neonatology, neuro‐pediatrics, physical medicine, orthopedics, psychiatry, physiotherapy and occupational therapy. Due to accessibility, we are interested in the analysis of the physiotherapy process. A deep analysis of the physiotherapy process has been made and has led to identify its strategic goals and the used, produced and created knowledge. This knowledge is validated through the physiotherapy group. Initially, a scoping meeting has been conducted with the steering committee physiotherapy paramedical in order to collect, identify and determine knowledge, its stakeholder and its source. Subsequently, we conducted two interviews with each stakeholder. During the first interview, an initial list of knowledge has been proposed by each actor. After that, we have made a synthesis of the identified knowledge in order to validate them with the stockholders during the second interview. In addition to that, each knowledge has been evaluated by each stakeholder through the set of criteria already defined and has been classified into the appropriate decision class. The decision makers are represented by the physiotherapy paramedical.
3.2 Method application In this section, we present the application of the method already detailed in the previous section in order to classify the knowledge to its appropriate decision class (Cl1, Cl2 and Cl3). The method is tested on 37 knowledges mobilized through the physiotherapy examination. We led 3 individual interviews and another interview for the group with physiotherapy professionals (Table2). Consequently, these interviews aim to analyze, classify and evaluate knowledge. Table 2: Role of actor which are implicated to the physiotherapy interview examination Decision maker Role Decision maker 1 Manager of the physiotherapy examination Decision maker 2 Physiotherapy health professional Decision maker 3 Physiotherapy health professional
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Sahar Ghrab et al. 3.2.1 Phase1: Construction of the preference model First of all, we define the set of reference knowledge. We consult mainly the medical records and the physiotherapy evaluation form which represent knowledge source. Then, we build the set of criteria and evaluate reference knowledge among these criteria (Table3). In the table 3, we present a part of the decision table of the first decision maker who affects knowledge K25, K26 and K27 to the appropriate decision class according to him.
K25: Knowledge related to “serment” gesture
K26: Knowledge related to the possibility of walking
K27: knowledge related to the hip articulation
Table 3: An extract of the decision table of decision maker1 Ki K25
G1 1
G2 1
G3 3
G4 2
G5 1
G 6 2
G7 1
G8 2
G9 1
Decision Cl1
K26 K27
1 2
1 2
3 3
2 2
1 1
2 2
1 1
2 2
2 2
Cl2 Cl3
A classification of reference knowledge is shown in Figure3. We use 4eMk2 tool which is a rule system for multicriteria decision support integrating dominance relation with rough approximation. It is build in the Laboratory of Intelligent Decision Support Systems. It supports the DRSA method.
Figure 3: Application of the DRSA method for a decision maker After having the decision tables related to each decision maker and the first classification of the reference knowledge, we should extract the decision rules related to the decision class Cl2. These decision rules should be studied later with the decision makers for validation. Figure4 demonstrates a decision rule extracted from the preference model of one decision maker. This decision rule characterizes the decision class CL2. 3.2.2 Phase2: Classification of potential crucial knowledge In this phase, the analyst uses the preference model constructed in the first phase to assign the new potential crucial knowledge to three decision classes: Cl1 “non crucial knowledge”, Cl2 “potential crucial knowledge” and Cl3 “crucial knowledge”.
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4. Conclusion In this paper, we propose a preference model of decision makers to characterize the decision class CL2 related to potential crucial knowledge. This type of knowledge is related to knowledge under validation and experimentation. The preference model leads to a decision table from which we extract the decision rules characterizing the new decision class CL2. Our proposal method goes by the method of Saad (2005) for elaborated in the same project EGIDE.
Figure 4: Example of decision rules extracted from the decision table Our contribution is to add a new decision class referring to potential crucial knowledge contrary to the method of Saad (2005) which classify knowledge into two decision rules (the first for non crucial knowledge and the second for crucial knowledge). This class was tested preliminarily on the ASHMS association and was applied particularly to the physiotherapy process. After evaluation, the experimentation results are mediocre. In fact, the physiotherapy process isn’t the suitable one for evaluating the new decision class and its rules. This is justified by the lack of a process of creating new knowledge and so there is no knowledge under validation. As future work, we wish to evaluate our model for others processes of the ASHMS association in order to know which suitable process can let us have the most pertinent results evaluations. The process chosen must identify more tacit knowledge and medical professional know‐how in order to well characterize CL2. In fact, we will propose an ontology of know‐how and we will hope to evaluate our model on other projects like innovative ones.
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Knowledge Management Influence on Innovation: Theoretical Analysis of Organizational Factors Ingrida Girniene Vilnius University, Vilnius, Lithuania ingrida.girniene@kf.vu.lt Abstract: Nowadays economic, political and social development is influenced by the process of globalization. In the knowledge‐oriented society the most important factor of competitive advantage is not the newness of the applied technologies, the uniqueness of the product/service, the disposed tangible assets, but the gained knowledge and the ability to manage it, since this resource is hard to repeat or copy. Business survival depends on the organization’s ability to gain a competitive advantage. Organizations can no longer expect that earlier created products/services or formerly for a long time conducted business will be still successful tomorrow and keep ensuring great organizational performance. Today, knowledge is the main source of innovation in a knowledge‐based economy. Creation of innovations depends on the gained knowledge and its commercialization, transformation into the productive knowledge. The article limits itself to the approach that innovations are the result of the use of the productive knowledge. Efficient management of productive knowledge results in innovation development in organizations. In order to analyze the interaction of knowledge management and innovation, a systematic approach is required. There is a lack of researches dealing with interrelation of knowledge management and innovation in organizations. There has been made a preliminary overview of the aspects, which could be related to knowledge management activities and the process of innovation creation. The most important aspects of knowledge management and innovation interrelation mentioned in the scientific researches are: the process of creating ideas, fundamental methods of learning, knowledge and knowledge management processes in creation of innovations. Knowledge management and innovations are influences by organizational factors. There has been made a preliminary overview of the main internal factors that have the greatest influence on knowledge management and innovation in the organization: organizational culture; learning; leadership; human resource management; information technologies and other factors (such as organizational structure, size, processes and business field). Besides, some knowledge management factors, influencing innovation are: knowledge management strategy; knowledge management processes (creation, acquisition, identification, use and dissemination). The internal factors, influencing innovations are devided into five main groups: strategic, management, resource, cultural and structural. The main purpose of the article is to identify interrelation of knowledge management and innovation and to create an integral knowledge management model that could ensure continuous innovation creation and development in the organization. The created model includes three main areas: strategic, culture and knowledge management processes, innovation. Keywords: knowledge, knowledge management, innovation, organizational factors
1. Introduction Knowledge and social capital turn into a specific environment for society development, creation of innovations related to knowledge and become an especially important tool of the economic growth. The knowledge management discipline is a reaction to the phenomenon of the knowledge economy (Cantner et al, 2009, p. 190). Knowledge management (KM) in the context of an organization is an ability to collect and use what the employees know, in order to create innovative products and services, implement efficient and socially responsible activity methods. Interrelation of knowledge management and innovations is very often researched with employing several organizational factors, which may influence innovations; however such researches are lacking a systematic approach. Most often accentuated factors of internal organizational environment, making influence on knowledge management and innovations, are: organizational culture; organizational learning; leader/manager; strategy and human resource management. When analyzing the factors of knowledge management most frequently there are identified knowledge management processes and knowledge management strategy, which influence the creation of innovations in the organization. The article provides a brief theoretical overview of knowledge management and innovations; the most significant organizational factors, influencing innovation, are identified; and an integral theoretical knowledge management model, influencing creation of innovations in the organization, is build.
2. Interrelation of knowledge management and innovations: theoretical approach Today, the competitiveness and effectiveness of the organization is determined by the ability to use the most important resource – knowledge. In the business, which is open to knowledge, the knowledge management is
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Ingrida Girniene a creation of the environment favourable for progress of knowledge processes, and purposeful, continuing, systematic management and development of these processes (Atkociuniene, 2010). Knowledge management affects making optimal decisions and ensures that the value of the intellectual capital would be perceived and properly used. Darroch and McNaughton (2002) state that innovations are the basic factor for survival of the organization, whereas the researches performed and the knowledge as well as new ideas gained from them make the source of innovations and competiveness advantage. Innovations are perceived as transformation of productive knowledge into new products, services, processes, in order to get profit (Figure 1). The majority of processes, related to innovations, are accompanied by the main resource – knowledge. Organizations, having more knowledge, may manage and use their obtained sources and abilities anew, thus, creating more added value. Creation of innovations depends on the gained knowledge and its commercializing, transformation into the productive knowledge.
Source: Adapted from Jakubavičius, A., Jucevičius, R., Jucevičius, G., Kriaučionienė, M., Krešys, M. (2008), Inovacijos versle: procesai, parama, tinklaveika. Vilnius: Lietuvos inovacijų centras, pp 7. Figure 1: Interaction of knowledge and innovations In the carried out scientific researches (Cardinal et al, 2001; Pyka, 2002; Cavusgil et al, 2003; Jensen et al, 2007; du Plessis, 2007; Jashapara, 2011) which reveal a concept of interrelation of knowledge management and innovations, the following is most frequently accentuated: creation of ideas; fundamental methods of organizations’ learning; knowledge and knowledge management processes in creation of innovations. In order to create innovations in the organization it is required to efficiently use knowledge. According to Jashapara (2011), new ideas are not enough for creation of innovations. Generated ideas must be developed and integrated into the context of the processes, activities and policy of the organization. When expanding these insights, it is worthwhile mentioning that the culture of the organization is one of the critical factors, determining analysis, development or rejection of new ideas. The stronger the relations between the employees in the organization are, the greater the probability is that the new ideas, being intercommunicated and discussed among the employees, will be implemented and become innovations in the real life. A successful innovation starts with an irrational idea, but turns into a codified, fixed form of knowledge (Jashapara, 2011). The literature suggests several underlying methods (models) of organizational learning: “Science, Technologies, Innovations” (the centres, generating knowledge, the scientific researches designed for codified and explicit knowledge used for innovations); “Do, Use and Interact” (knowledge and experience are identified via actions; there dominates “learning by doing”, communicating; continuous support of relations with suppliers, clients and market; interaction between tacit internal and external knowledge) (Jensen et al, 2007). Knowledge and knowledge management processes in creation of innovations are expressed via: enabling of sharing and codifying of the tacit knowledge in the organization; influence of the explicit knowledge on innovations; collaboration encouragement; running of knowledge management processes. Tacit knowledge sharing is a mandatory process when striving for innovations. Innovative organizations apply the tactics of learning via actions, which prevents the competitors to divine and copy the formulated strategy and plans (Cavusgil et al, 2003). According to Cardinal et al (2001), when creating innovations, the organization requires tacit knowledge, which is transferred at collaboration of different work groups and teams of different divisions. Despite the fact that the explicit knowledge is less important in the process of innovation than the
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Ingrida Girniene tacit knowledge, since the former may be easier copied by the competitors, nevertheless it is also used while creating innovations (Cardinal et al, 2001). Innovation is a process, where the knowledge, circulating inside the organization, is applied with the help of new methods, and knowledge management plays a significant role, when transforming the explicit knowledge into new innovative ideas. Collaboration may be characterized as an ability of clients, suppliers, partners and employees to form knowledge sharing communities, networks both in and beyond the organization, which would work together striving for the same common business goals (du Plessis, 2007). The stronger the relations between the partners are the more intensive knowledge sharing and transfer is. When striving for collaboration, the most important networks are the informal ones (Pyka, 2002). Informal relations replace formal ones, the ambience of mutual trust is created and efficient channels are formed for dissemination of the knowledge. Knowledge management processes embrace knowledge use, identify, creation, acquisition, sharing/transfer, storage and value (Heisig, 2009). Thus, knowledge management ensures that knowledge needed for innovations would be easily accessible. After having performed the theoretical analysis of knowledge management and innovations, it is possible to state that there have been performed many researches on the topic of knowledge management, as well as innovations and their interrelation, however it has not been purified what organizational factors make the greatest influence on creation of innovations in the organization.
3. Organizational factors, influencing innovation In the scientific literature, there is analyzed an influence of organizational factors on the knowledge activities and innovations. It is analyzed how the knowledge capital, which is being transformed by learning, collaboration, knowledge characteristics, culture, management, open and flexible structure, communication, collaboration, may determine creation of innovations (du Plessis, 2007; Donate, Guadamillas, 2011; Akram et al, 2011; Koch, 2011). Various organizational factors influence the activity of an organization. They determine its effectiveness and competitiveness. The organizational factors may be defined as a certain element, acting inside or beyond the organization and influencing the achievement of the stipulated strategic goals and its efficient activity. The scientists (Holsapplea, Joshib, 2000; Chourides et al, 2003; Wong, 2005) tend to distinguish different organizational factors, which influence efficient application of knowledge management and implementation of its means, and correspondingly, innovations. These factors may be classified in several ways (Table 1). Table 1: Classification of organizational factors, influencing knowledge management and innovations Authors of theoretical and empiric researches Skyrme, Amidon, 1997 Ozsomer, Calantone, Di Bonetto, 1997 Liebowitz, 1999 Sivadas, Dwyer, 2000 Holsapplea, Joshib, 2000 Alazmi, Zairi, 2003 Chourides et al, 2003 Diakoulakis et al, 2004 Wong, 2005 Maqsood, Walker, Finegan, 2007 Balezentis, 2007 Heisig, 2009
Organizational factors Compelling vision and architecture, knowledge leadership, knowledge creating and sharing culture, continuous learning, technology infrastructure and systematic organisational knowledge processes Environmental, strategic and organizational structure factors KM strategy, chief knowledge officer, KM infrastructure, knowledge ontologies and repositories, KM systems, supportive culture Internal and interorganizational factors Environmental, managerial and resource influences Top management commitment, KM strategy, KM processes, KM infrastructure, and culture Strategy, human resource management, information technology, quality and marketing (connected with organisational functional areas) Environmental factors, market activities, product operations Internal and external factors Information and communication technology infrastructure, leadership, human infrastructure Internal and external forces Human‐oriented factors, organization, technology, management‐ process
At summarizing the classification of organizational factors, influencing knowledge management and innovations, it could be concluded that they influence innovations more. They may be divided into two wide groups: external and internal factors (Figure 2). The article shall deal a lot with the analysis and research of
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Ingrida Girniene internal organizational factors, since they are closely related to knowledge management and innovations, and what is more, they may be controlled by the organization itself, whereas the external factors may not be managed. External factors such as environmental influences are not taken into account since organisations have little control over them when implementing knowledge management (Wong, 2005). Among the enumerated classified organizational factors, influencing knowledge management and innovations, the internal organizational factors are identified and discussed to a greater extent. The external factors shall be briefly overviewed, however they are not the main subject of the article, since an organization may have less control over them.
Figure 2: Organizational factors, influencing innovations Considering the overview of the mentioned organizational factors, there are formulated blocks of external and internal organizational factors, influencing innovations. They embrace the factors of direct and indirect effect. Competitors, clients, suppliers and partners as well as their provided knowledge may be assigned to the group of external factors of direct effect. Political, economic, social and technological factors may be assigned to the group of external factors of indirect effect. The block of internal factors embraces five groups of factors of direct effect: strategic, management, resources, cultural and structural factors. The mentioned blocks of factors shall be further discussed in the article.
3.1 External factors A modern organization, developing its activity in the knowledge‐oriented society, must continuously observe and evaluate the external environment, in order to successfully expand its activity in the knowledge‐based economy. Very often the following external environment’s factors of indirect effect to the organization’s activity (as well as to innovations) are being distinguished: economic, technological, social and political (PEST analysis), as well as the following factors of direct effect: competitors, clients, suppliers and partners (Figure 2). Economic factors define the country’s economic situation; reveal its economic indicators, such as gross domestic product (GDP), level of unemployment, inflation, fluctuations of currency rates and the like. These factors are closely related to the country’s economic activity and the direction of its development. Technological factors embrace the state’s policy of the technological sphere as well as technological novelties. In the knowledge‐oriented society the changes in technologies continuously influence the development of organizations’ activities. It is required to observe what kind of new technologies are applicable to the organization’s activity, which of them are applied by the competitors and offered by partners. Social, social‐cultural, demographic factors embrace the country’s culture, consuming of the products/services, birth‐rate etc.
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Ingrida Girniene Political, legal factors embrace the international and local government’s policy as well as its priorities. The external knowledge gained from the clients (support of continuous feed‐back), suppliers (offers, overviews of novelties) and partners (establishing and keeping of new relations) must be especially important for each organization. It is required to continuously observe the competitors’ activity, to analyze their products/services, new projects and possibilities of collaboration. As it has been already mentioned, despite that the external organizational factors influence innovations, however the organization itself may not control them and they are not a subject of the present article. Further, there shall be analyzed essential internal factors, influencing innovations.
3.2 Internal factors After the performed analysis of the internal organizational factors (Table 2), there have been identified groups of critical internal organisational success factors, including knowledge management factors, influencing innovations. Groups of critical success factors are areas in which results, if they are satisfactory, will ensure successful competitive performance for the organisation (Wong, 2005 cit. by Rockart, 1979). These factors are grouped into five main groups (Figure 2): strategic, management, resources, cultural and structural factors. In the context of the mentioned factors, knowledge management is perceived as an integral part of the strategic management, oriented to development of innovative activity. Innovations are perceived as a reaction to changes, creation and purposeful use of efficient knowledge determined by the organizational culture, learning, knowledge management processes, and risk. Creation of innovations is related to strategic goals, organization activity’s effectiveness and competitiveness. Table 2: Internal organizational factors, influencing innovations Internal factors, influencing innovations Organizational (knowledge) culture
Organizational learning
Strategy
Manager/leader Knowledge management Knowledge management strategy Knowledge management processes
Human resource management Information technology infrastructure Creativity Organizational structure, atvitivies, size Motivation
Scientific researches (authors) Ahmed, 1998; Cumming, 1998; Alazmi, Zairi, 2003; du Plessis, 2007; Ortt, van der Duin, 2008; Heisig, 2009; Akram et al, 2011; Donate, Guadamillas, 2011 Ahmed, 1998; 2004; Chanal, 2004; Merx‐Chermin, Nijhof, 2005; Ju et al, 2006; Jensen et al, 2007; Chen, Huang, 2009; Cantner et al, 2009; Amalia, Nugroho, 2011; Koch, 2011; Akram et al, 2011; Donate, Guadamillas, 2011 Skyrme, Amidon, 1997; Ozsomer et al, 1997; Liebowitz, 1999; Alazmi, Zairi, 2003; Chourides et al, 2003; Wong, 2005; Heisig, 2009 Skyrme, Amidon, 1997; Liebowitz, 1999; Cumming, 1998; Ahmed, 1998; Holsapplea, Joshib, 2000; Anderson et al, 2004; Wong, 2005; Maqsood et al, 2007; Amalia, Nugroho, 2011; Ortt, van der Duin, 2008; Heisig, 2009; Donate, Guadamillas, 2011 Darroch, McNaughton, 2002; Darroch, 2005; Ju et al, 2006; Popadiuk, Choo, 2006; Maqsood et al, 2007 ; du Plessis, 2007; Chen, Huang, 2009; Xu et al, 2010; Akram et al, 2011; Donate, Guadamillas, 2011; Andreeva, Kianto, 2011; Koch, 2011; Quintane et al, 2011; Delgado‐Verde et al, 2011; Jensen et al, 2007 Swan et al, 1999; Carneiro, 2000; Cantner et al, 2009; Chen, Huang, 2009; Donate, Guadamillas, 2011 Swan et al, 1999; Carneiro, 2000; Darroch, McNaughton, 2002; Maqsood et al, 2007; Cantner et al, 2009; Xu et al, 2010; Amalia, Nugroho, 2011 Ahmed, 1998; Lubart, 1999; Cumming, 1998; Stenberg, 2006; Zhou, Shalley, 2008 Ahmed, 1998; Anderson et al, 2004; Chanal, 2004; Ortt, van der Duin, 2008; Akram et al, 2011; Darroch, 2005; Cantner et al, 2009 Ahmed, 1998; Anderson et al, 2004;; Stenberg, 2006; Maqsood et al, 2007; Zhou, Shalley, 2008
Strategic factors stipulate directions and goals of acting of other groups of factors. They embrace: strategy, policy, vision, mission, goals, plans, and manager/leader. Strategic factors depend on the strategic team, managers’/leaders’ activity and strivings. It is the main group of factors and it directly influences other groups, especially management factors.
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Ingrida Girniene Management factors are ones of the most important. They embrace: knowledge management, human resource management, communication management, information technology management, business processe management, sale and marketing management, change and risk management. The purposeful management of all the mentioned spheres ensures creation of innovations in the organization. Resource factors embrace: human resources, financial resources, knowledge resources; besides this group includes natural resources; however their influence on innovations may be indirect. Cultural factors embrace: organizational culture, organizational learning, manager’s/leader’s support, creativity, collaboration, open communication, motivation, and networks/communities. These factors are directly related to management factors and greatly influence them. Structural factors embrace: organization’s structure, activity area, size. They are also very important; however there is a lack of researches, confirming their direct effect to creation of innovations in the organization. At summarizing, there may be drawn a conclusion that all the identified groups of internal factors interact and depend on each other. In the article, there is underlined an interrelation and influence made on innovations by knowledge management, embraced by the group of management factors, thus further, considering the identified critical internal organizational success factors, there shall be built an integral knowledge management model, influencing innovations.
4. Integral knowledge management model, influencing innovations In the scientific area, the effect of knowledge management to innovations in the organization has not been fully analysed. Thus, after identification of groups of critical internal organizational success factors, including knowledge management, there may be built an integral knowledge management model, embracing three main areas (Figure 3): strategic area, area of culture and knowledge management processes, innovation area. Strategic area embraces: manager/leader, organization strategy and knowledge management strategy, as an auxiliary to the main one. Leader (manager) should support creation and cherishing of the culture in the knowledge‐oriented organization; motivate employees to continuously learn, collaborate and integrate the gained knowledge into the processes, taking place in the organization, striving to improve them. The knowledge management strategy should encourage knowledge management processes in the organization. This area directly influences other mentioned areas. Area of culture and knowledge management processes embraces: organizational culture, creativity, organizational learning and knowledge management processes. Organizational culture, favourable to knowledge management and innovations, should embrace such values as tolerance, trust, continuous knowledge sharing; encourage creativity and innovativeness and be one of the motivating factors in creation of novelties and improvement of the existing products/services. Organizational learning should embrace continuous learning (either individual or in groups), collaboration, creation of networks and practical communities, researches and development, improvement of competences and rising of qualification level. Knowledge management processes are ones of the most important factors, influencing innovations; thus, after having analyzed and systemized the knowledge management processes mentioned in the empiric scientific researches, there may be drawn a conclusion that the most often analyzed ones are: knowledge use, identify, creation, acquisition, sharing/transfer, storage and value (Heisig, 2009). All the processes of knowledge management influence innovations. The process of knowledge creation could be distinguished as a mediator in the context of other knowledge management processes. Knowledge creation is therefore perceived as one of the major assets of innovative organisations, and the innovative organisations are defined by knowledge creation (Merx‐Chermin, Nijhof, 2005). The organizations, which are intensively creating knowledge, increase the number of innovations even more rapidly than the organizations, which are slowly absorbing the knowledge (Andreeva, Kianto, 2011). Innovation area embraces: an innovative process (starting with generating ideas and finishing with their successful implementation) and innovations as a result (a new or improved product/service).
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Figure 3: Integral knowledge management model, influencing innovations All the distinguished areas of integral knowledge management model are interrelated. If innovations are not created in the organization, it is required to return to the strategy and analyze whether it is oriented to development of the innovative activity. It is also required to analyze the following: whether organizational learning takes place, whether the organizational culture is favourable to it, to what extent the creativity and divergent thinking are encouraged, whether a continuous process of idea creating and sharing takes place. In such a case, knowledge management processes, which should be supported by the knowledge management strategy and encouraged by the favourable organizational culture, could serve as a critical success factor. If all the areas specified in the model support each other, it may be assumed that the innovative activity is being intensively developed in the organization and innovations are being created. It should be marked out that practical application of the built up theoretical model should be checked after carrying out of an empiric research, which is planned to be executed in Lithuanian organizations.
5. Conclusions The knowledge is inexhaustible. The more skills or competencies are used, the more they expand. Creation of knowledge is a joint process of production, where innovations make one of the outputs; and the other output is learning and reinforcement of skills. Continuous creation of knowledge and purposeful management are especially important for modern organizations, which require innovations in order to gain profit and survive in the hyper‐dynamic market. After having analyzed the performed scientific researches, which revel interrelation of knowledge management and innovations, it may be stated that the following concepts are accentuated most often: creation of ideas; fundamental methods of organizations’ learning; knowledge and knowledge management processes in the creation of innovations. Despite that the interrelation between the knowledge, knowledge management and innovations has been researched in the works of scientists, however one may lack a complex analysis, during which knowledge management effect to innovations would be set and evaluated; influence of direct and indirect organizational (including knowledge management) factors on innovations would be systemized and exhaustively revealed. With the help of the systemic and critical analysis, there have been determined external and internal organizational (including knowledge management) factors, which may influence innovations. The external factors embrace: economic, social, political and technological as well as those related to competitors, clients, partners and suppliers. Critical internal organizational success factors are divided into five groups: strategic, management, resources, cultural and structural factors. The groups of management factors embrace knowledge management and its factors. After having analyzed the mentioned groups of internal organizational factors, there has been built up an integral theoretical model of knowledge management, influencing innovations. The present model embraces three areas: strategic, culture and knowledge management processes, innovation. While striving to determine
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Ingrida Girniene the effect of knowledge management to innovations in the organization, it is required to apply the built up model at the empiric level.
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Developing Knowledge Management Capabilities in Social Enterprises: UK Experience Maria Granados, Vlatka Hlupic, Elayne Coakes and Souad Mohamed University of Westminster, London, UK m.granados@westminster.ac.uk V.Hlupic@westminster.ac.uk coakese@westminster.ac.uk S.Mohamed@westminster.ac.uk Abstract: Knowledge Management (KM) practitioners and academics have demonstrated how an organisation can improve its performance and develop competitive advantages, by managing effectively the knowledge they have. However, there is still a lack of empirical evidence especially in small business and social economy organisations. Among the last type of organisations, Social Enterprises (SEs) are a distinct type of enterprise that operate as a business but are driven by social and environmental objectives. These organisations have received significant attention in the recent years by academics and politicians as a solution to alleviate current social and environmental problems. However, there is a lack of empirical evidence of how these organisations operate and perform. Therefore, in order to help to fill these gaps, this paper proposes and empirically accesses a theoretical model named Knowledge Management in Social Enterprises (KMSE), supported by the knowledge‐based theory of the firm and organisational capabilities theories. The model explores organisational characteristics and knowledge activities within SEs that can develop Knowledge Management Capabilities (KMCs) and improve organisational performance. The empirical evidence has been collected through a quantitative study that had 432 responses from senior members of SEs in UK to a survey about their current activities of KM and their organisational characteristics. The empirical study confirms that SEs are developing KMCs differently from their counterparts in the private, public and third sector, and these capabilities account for 20% of the improvement in a SE’s performance in the last 12 months. The main differences are associated with efforts from SEs to built and maintain a culture that promoted trust, knowledge sharing and learning under decentralised and flexible structures, more than information systems or extrinsic motivations. These findings present significant contributions for practitioners, consultants, politicians and academics who are interested in (a) demonstrating the tangible outcomes of developing KMCs, (b) understanding how to develop these capabilities under different organisational settings, and (c) understanding the organisational characteristics of SEs that would allow the provision of more accurate strategies to enhance the SE sector and maximise its impact and coverage. Keywords: knowledge management capabilities, social enterprises, organisational performance
1. Introduction Under the growing pressures of complexity and globalisation, enterprises that effectively capture the knowledge in their organisations and distribute it to their operations, productions and services, have an strategic advantage over their competitors (Drucker 1991, Kogut and Zander 1992, Quinn 1992). Developing adequate capabilities to manage knowledge is therefore important for organisations. This has resulted in considerable research, both empirical and theoretical, studying how organisations can develop Knowledge Management Capabilities (KMCs) and obtain positive outcomes (Leonard‐Barton 1995, Gold et al. 2001, Lee and Choi 2003, Mills and Smith 2011). This research has been mainly completed in larger private organisations, where resources and competitive conditions can trigger the use of Knowledge Management (KM). However, there are other sectors and other organisation types and sizes that can develop these capabilities and improve their organisational outcomes, as is the case of small businesses and social economy organisations that have organic structures and cultures fostering knowledge capabilities and innovation (Ruiz‐Mercader et al. 2006, Hume and Hume 2008). Therefore, there is a growing need for more empirical research that can explain how these KMCs can be developed by organisations of different sizes, sectors, structures or strategic orientations, and demonstrate what are the tangible outcomes of this development. In that sense, the objective of this paper is to bridge the different theoretical and empirical approaches on KMCs with the under‐researched, distinct characteristics of SEs, which are hybrid organisations, usually with a multi‐bottom line, related to social, environmental and economic goals, a multi‐stakeholder dimension, and a broader financial perspective to focus on sustainability. This is supported with empirical data collected from 432 senior managers of SEs in UK. Commencing with a brief theoretical background, the paper continues by presenting the theoretical model developed, which informed the hypothesis to be evaluated with the empirical element of this research.
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Maria Granados et al. Furthermore, a general explanation of methodological consideration is specified, with a presentation of findings and further discussion.
2. Theoretical background 2.1 Knowledge management capabilities (KMCs) This research is based on KMC literature and empirical studies conducted in the private and public sectors, determining the theoretical grounding to study knowledge from the perspective of a Knowledge‐based view theory of the firm and Organisational Capabilities theories (Kogut and Zander 1992, Leonard‐Barton 1995, Spender 1996, Grant 1997, Sveiby 2001). These theories involve the development of organisational capabilities that enhance the chances for growth and survival (Kogut and Zander 1992) and establish their long‐term strategies. Therefore, knowledge would become the primary source of competitive advantage for a company, and knowledge management would support the aggregation of resources into capabilities. These capabilities should be controlled by the organisation in order to improve efficiency and effectiveness (Barney 1991). Based on this review, the following definition of KM capabilities for Social Enterprises is developed for this research: ‘The ability to mobilise and deploy knowledge resources in combination with other capabilities for enabling KM activities, and thus distinguishing and providing a sustainable advantage, and enhancing organisational performance of Social Enterprises.’ However, there is still a lack of empirical evidence of how the development of these capabilities results in strategic and operational outcomes for organisations (Gharakhani and Mousakhani 2012). Moreover, the reduced number of empirical studies that have been undertaken are focused only on large private and public organisations (Gold et al. 2001, Lee and Choi 2003, Liang et al. 2007, Nguyen et al. 2009, Zheng et al. 2010, Mills and Smith 2011). Therefore, more research needs to be undertaken to study and assess the development of these capabilities under different organisational scales and structures, and how this development results in known and, potentially, some unknown outcomes for these organisations.
2.2 Social enterprises (SEs) The focus of this paper is on Social Enterprises (SEs), which, although there are still important discussions about their exact definition, are considered organisations that represent another step in the continuing re‐ invention of the ‘third sector’ or Social Economy (Dees 2007), and therefore are crucial for its expansion. Because this research is undertaken mainly in the UK, and, as Mair (2011) suggested, the socio‐economic context defines the origins and motivations of SEs, this paper follows the UK perception of SEs as businesses that trade to tackle social problems, improve communities, people’s life chances, or the environment (Social Enterprise UK, www.socialenterprise.org.uk). The impact of the sector has significantly increased in the recent years, with 68,000 SEs in the UK contributing at least £24bn to the economy and estimated to employ 800,000 people, with 39% of SEs concentrated in the most deprived communities (Villeneuve‐Smith 2011). Although these organisations are attracting the attention of governments and private organisations alike, as a response to mitigate current failures in the public, private and not‐for‐profit sectors, there is still a lack of empirical knowledge about how these organisations operate, perform and consequently scale up. Therefore, there is an increasing need for more research and empirical data that describes and explains the intrinsic characteristics of SEs. This knowledge will be crucial for internal and external supporters to design and provide accurate strategies to enhance the sector and maximise its impact and coverage.
3. Knowledge management in social enterprises model (KMSE) and hypotheses Drawing upon the previous discussion it can be deduced that knowledge is a key source of competitive and sustainable advantage and, by developing KMCs, a company can enhance organisational performance and effectiveness. Therefore, these capabilities can create value to organisations of different sizes, from varying sectors with contrasting strategic drivers. As a starting point for managing knowledge in an organisation, companies need to know which are the activities that create knowledge ‐ the process capabilities, understanding exactly what organisationally constitutes a KMC ‐ organisational capabilities, and understanding what value these can add – organisational performance (Leonard‐Barton 1995, Gold et al. 2001, Ndlela and du Toit 2001). For this reason, the focus of
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Maria Granados et al. this research is on knowledge organisational and process capabilities and Perceived Organisational Performance (POP). A model is developed based on previous KMC models, such as Leonard‐Barton (1995), Gold et al. (2001) and Lee and Choi (2003), that integrated infrastructural and process capabilities with KMCs and explores its influence on various organisational outcomes. The proposed model builds on this and explores KMC influences on organisational performance of SEs. Because SEs are a relatively new academic field, with more theoretical than empirical research developed (Granados et al. 2011), few explicit references to KM in SEs were found. Therefore, the hypothesised model is developed largely from theoretical and empirical research of KMCs in other sectors and the limited empirical and descriptive research available on SEs’ organisational behaviour. Thus, it is through the empirical exercise of this research that the KMSE model is tested and validated. The KMSE model is presented in Figure 1 and illustrates the three main components of the model, KOC, KPC and POP, their hypothesised relationships and their reflective indicators. The theoretical grounding, justification and explanations of each component and its hypothesis associated is presented in Table 1.
Figure 1: Knowledge management in social enterprises (KMSE) model Table 1: Variables description and hypotheses Variable
KOC
Technology
Structure
Explanation and relationship with KMCs Dimension of KMCs that represent the mechanisms for fostering knowledge consistently and increasing the efficiency of knowledge processes. They are the reservoir of knowledge embedded in people and technology systems, followed by the management structures and the culture that support the growth of knowledge. Degree of IT support for collaborative work, for searching and accessing, for communication, and for information storing. SEs use technology in a general way to manage their information, but these systems are not integrated or sufficiently developed to support decision‐making, and operation and production management. Level at which most decision‐making occurs, as well as the amount of formal rules, policies and procedures within the SE.
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Hypothesis
H1: KOC has a positive effect on POP of SEs
H3: Technology has a positive effect on the KOC of SEs
H4: Structure (centralisation and formalisation) has a
Literature support (Leonard‐Barton 1995, Osterloh and Frey 2000, Gold et al. 2001, Hansen and von Oetinger 2001, Lee and Choi 2003, Paton 2003, Chuang 2004, Alavi et al. 2005, Bull and Crompton 2006, Bull 2007, Chen and Huang 2007, Chin‐Loy and Mujtaba 2007, Mason et al. 2007, Shaw and
Maria Granados et al. Variable
People
Culture
KPC
Acquisition
Conversion
Application
Protection
Explanation and relationship with KMCs
Hypothesis
KM literature suggested that less centralised, less formalised and more integrated structure would lead to favourable levels of social interaction and thus effective knowledge management. Structure characteristics among SEs are diverse. However, patterns of participatory, flexible, adaptable, transparent and multi‐stakeholder models were recognised as core elements in SE organisational style. Degree to which employees believe that they can have extrinsic or intrinsic incentives due to their knowledge sharing. Entrepreneurial skills and intrinsic motivations can be considered as unique factors for successful SEs. Degree to which employees actively help one another in their work, have reciprocal faith in others’ intentions, behaviours, and skills toward organisational goals, and share the definition of the organisation’s purpose. Additionally, the degree of opportunity, variety, satisfaction, and encouragement for learning and development. These elements guide the behaviour of the enterprise’s employees and are crucial drivers of the successful implementation and adaptation of KMCs. SEs culture is open to new ideas, with people socially and ethically motivated by the SE’s mission but with limited recourses to invest in organisational learning and employees’ development. Represent the knowledge activities within the organisation that leverage organisational capabilities. This research follows the activities proposed by Gold et al. (2001) that create, control and integrate the knowledge necessary for a company’s’ current and future operations. These are acquisition, conversion, application and protection. An organisation requires to acquire knowledge internally and externally, by sharing, disseminating, innovating, improving use of existing knowledge and creating new knowledge. This process opens new productive opportunities and enhances the firm’s ability to exploit these opportunities. An organisation needs to develop the ability to organise, integrate, combine, structure, coordinate, replace or distribute their knowledge. This results in consistency or common dialog for knowledge, reduces redundancy, improves efficiency by eliminating excesses and duplications, allows the organisation to integrate specialised knowledge An organisation needs to use, store, retrieve and apply knowledge. This might result in new products, higher productivity or more innovative activities. An organisation needs to protect their knowledge, so it will not lose its qualities of rare and inimitable that are main sources of competitive advantage.
negative effect on the KOC of SEs
Literature support Carter 2007, Yang and Chen 2007, Nguyen et al. 2009, Mohan and Potnis 2010, Ohana and Meyer 2010, Zheng et al. 2010, Allameh et al. 2011, Mills and Smith 2011)
H5: People (T‐shaped, extrinsic and intrinsic motivation) have a positive effect on the KOC of SEs
H6: Culture (Collaboration, trust, learning and mission) has a positive effect on the KOC of SEs
H2: KPC have a positive effect on POP of SEs
H7: Acquisition has a positive effect on the KPC of SEs
H8: Conversion has a positive effect on the KPC of SEs
H9: Application has a positive effect on the KPC of SEs H10: Protection has a positive effect on the KPC of SEs
(Barney 1991, Pentland 1995, Zander and Kogut 1995, Grant 1996, Davenport et al. 1998, Davenport and Klahr 1998, O'Dell and Grayson 1998, Gold et al. 2001, Yli‐Renko et al. 2001, Edvardsson 2009, Durst and Edvardsson 2012)
To measure POP, four systems that measure Social Enterprises’ performance were analysed (Paton 2003, Somers 2005, Bull and Crompton 2006, Meadows and Pike 2010). These systems recognised certain difficulties and differences when measuring performance in SEs. Such differences are associated with their multi‐bottom
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Maria Granados et al. line, their multi‐stakeholder dimension, and a broader financial perspective to focus on sustainability. These customised systems for measuring performance in Social Enterprises permit the KMSE model to assess more accurately the impact of KMCs on SEs’ organisational performance. Therefore, integrating measures used in empirical studies discussing KMCs, and in the aforementioned performance systems for SEs, the elements that comprise POP are: ‘Return’, ‘Workforce and Innovation’, ‘Stakeholder environment’ and ‘Internal activities’.
4. Method The population for this research is Social Enterprises in UK. Due to the difficulty in deciding which enterprises are really a Social Enterprise, and in order to access the Social Enterprises that follow the UK definition, the sample frame for this research considered only the Social Enterprises that are self‐defined and are members of at least one of the listed UK Social Enterprise networks. An online questionnaire was designed including some questions used by other researchers of KM capabilities (Denison and Mishra 1995, Gold et al. 2001, Bock and Kim 2002, Lee and Choi 2003, Burgess 2005, Chen and Huang 2007, Chin‐Loy and Mujtaba 2007, Lin 2007), and also other questions that permit the assessment of the theoretical model developed in this research. These were then evaluated from a respondent’s perspective. The initial version of the questionnaire was entered on Survey Monkey, which is a web site that offers online survey services. After piloting the survey questionnaire with SEs researchers and practitioners, a survey invitation email was sent to 2,210 senior members of SEs in UK in January 2012. A total of 432 responses were collected, resulting in a 19.5% response rate. The responses were downloaded from Survey Monkey and prepared for their analysis with SPSS software and consequently AMOS software. A demographic analysis of all respondents was obtained, followed by an initial analysis of missing data and outliers. Continually, confirmatory and exploratory factor analyses were executed in order to operationalise the theoretical model before its test with Structural Equation Modelling (SEM) in AMOS software. SEM is a modelling framework that permits the estimation of multiple and interrelated dependence relationships, the representation of unobserved concepts in these relationships, such as latent constructs, and the definition of a model to explain the entire set of relationships (Hair et al. 2010).
5. Results and analysis 5.1 Sample description The demographic description of the sample is presented in Table 2. From the 432 responses collected, 39 respondents did not work for SEs, thus these entries were deleted from the final dataset, resulting in a total of 393 responses. Table 2: Sample demographic description Organisational information Number Region where operating England 308 Wales 80 Scotland 61 Northern Ireland 32 International 44 Age of SE Less than one year 36 1 ‐ 2 years 72 3 ‐ 4 years 62 5 ‐ 9 years 85 10 or more years 121 Number of employees (paid staff) 0 47 1 ‐ 9 201 10 ‐ 49 82 50 ‐ 249 33 250 ‐ 999 11 1,000 and over 3 Participant’s information
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Frequency 59% 15% 12% 6% 8% 10% 19% 16% 23% 32% 12% 53% 22% 9% 3% 1%
Maria Granados et al. Organisational information Role in SE Owner/Managing Director/CEO Senior Management Junior Management Gender Male Female
Number
Frequency
183 74 18
67% 27% 7%
124 135
48% 52%
In terms of demographic findings, Social Enterprises presented characteristics already identified by other researchers, such as small and medium sized organisations (87%), with main social objectives (63%), but also with double and triple bottom lines, such as profit and environmental (39%). Only 7% of respondents affirmed that their Social Enterprises have a KM programme in place, mainly related to acquisition of data management software and implementation of knowledge sharing practices. However, there are still 24% of respondents that were not sure if their Social Enterprise has a KM programme in place, confirming that KM is still an unfamiliar business practice for most practitioners.
5.2 Exploratory factor analysis (EFA), confirmatory factor analysis (CFA) and structural equation modelling (SEM) Before starting to analyse the data with CFA and SEM, the data were checked for missing values and outliers. This resulted in a final valid sample of 306 with 0% of missing data. The KMSE model was tested using this sample. Initially, the EFA resulted in a different dimension of some constructs and items that represent the elements of the model. The new measurement model was then redefined and validated with the CFA resulting in three indicators of KOC excluded from the model: ‘technology’, ‘T‐shaped skills’ and ‘extrinsic motivation’, and one indicator of KPC ‘Protection’. The final CFA measurement model, with eleven constructs, fit the data very well as evidenced by the CFI of 0.904 and RMSEA of 0.055. The CFI and RMSEA are indices to assess goodness‐of‐fit of the measurement model, which is how the empirical data differs from the theoretical KMSE model. For a sample greater than 250 and a number of observed variables greater than 30, Hair et al. (2010) recommended CFI above 0.90 and a RMSEA below 0.08 to be accepted. The last step was undertaking the SEM, which assess the hypothesised relationship between the main variables. The SEM Final Model is presented in Figure 2. The values associated with each path are standardised regression coefficients. These values represent the amount of change in Y given a standard deviation unit 2 change in X. The values above each dependent variable are the R values. Consequently, it can be determined that 54 % of the variance associated with KPC is accounted for by its predictor KOC. Likewise, it can be determined that the indirect effect of KOC and the direct effect of KPC explain 20 % of the variance associated with POP. The determined KMSE model accepted Hypotheses 2,4,6,7,8 and 9, partly accepted 5, rejected 3 and 10, and resulted in an alternative hypothesis to 1. This alternative hypothesis suggested an indirect effect of KOC on POP through its effect on KPC.
6. Discussion The statistical process to reach the final SEM model presented in Figure 2 demonstrated that the initial hypothesised model established in Figure 1 was not explaining the real experiences and practices undertaken by Social Enterprises in UK. However, this difference was expected. This is because the model was developed under theoretical assumptions drawn from previous KM research in other sectors and types of organisations. Moreover, no previous empirical research was undertaken about current KM practices in SEs, and there was a paucity of research on organisational behaviour of SEs. Therefore, each of the elements that were either confirmed, or rejected, by the empirical data, presented a contribution to current KM and SE literature and practitioners by themselves. These differences are presented as follows:
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0.72
CMIN/DF: 1.915 CFI: 0.904 RMSEA: 0.055
0.88
Not supported rela onship
0.81
T‐shaped skills Extrinsic Mo va on Intrinsic Mo va on
New rela onship
Knowledge Organisa onal Capability (KOC)
Indirect effect
0.79
0.20
0.75
Structure
Perceived Organisa onal Performance (POP)
0.74
Technology
0.61
Innova on 0.85 0.78
Acquisi on Conversion
0.87 0.82 0.83
Applica on
0.48 0.54
Return and Workforce
Stakeholder sa sfac on Internal ac vi es
Knowledge Process Capability (KPC)
Protec on
Figure 2: Determined SEM model for the KMSE model
No influence of ‘T‐shaped skills’, ‘Extrinsic Motivation’ and ‘Technology’ in KOC: ‘T‐shaped skills’: Even though literature has suggested that employees and managers that posses ‘T‐shaped skills’ would influence positively the development of KM capabilities (Madhavan and Grover 1998, Hansen and von Oetinger 2001, Lee and Choi 2003, Soon and Zainol 2011), this relationship was not found in SEs. The responses obtained to each indicator of the variable ‘T‐shaped skills’ demonstrated that members of SEs are specialised in their own area and, at the same time, communicate well with other members. Although this can be described as ‘T‐shaped skills’, this did not have any influence on developing KMCs in SEs. A possible reason is that communicating with others is the way SEs work, due to their small size and flat structures, which has already been assessed in the variables ‘Collaboration’ and ‘Structure’. Thus, even if members of SEs have ‘T‐shaped skills’, it is actually their flat structures and culture of knowledge sharing that develop KOC.
‘Extrinsic Motivation’: Contrasting with ‘T‐shaped skills’, the responses obtained for each indicator of the variable ‘Extrinsic Motivation’ demonstrated that members of SEs do not receive any extrinsic motivation, such as bonuses, promotional opportunities or job security for sharing their knowledge. Thus, this element did not have any influence, either positive or negative, in developing KOC. This concurs with previous studies of SEs (Shaw and Carter 2007, Ohana and Meyer 2010), which found that the motivation of SE members was less money‐related and more associated with benefits obtained by the results of collective rather than individual actions. This might result in an environment of less competition and more knowledge sharing.
‘Technology – IT support’: When asked about the IT support received in their SEs, participants indicated that this support was more for storing and retrieving information, than for communication and collaboration among members and, even in the first two cases, the support was not significant. Thus, technology support did not have any influence on developing KOC, concurring with previous studies in both larger enterprises (Lee and Choi 2003, Chuang 2004, Mills and Smith 2011) and SMEs (Desouza and Awazu 2006). A possible reason for this is the scarcity of economic resources in SEs, resulting in Social Entrepreneurs not considering technology part of their priorities to develop (Paton 2003). Other reasons may be time constraints of busy managers to input data into IT systems (Bull 2007), inexperienced field staff, and the lack of training required (Mohan and Potnis 2010).
No influence of ‘Protection’ in KPC: Responses given to the survey about knowledge protection activities, indicated SEs are not effectively protecting their knowledge from inappropriate or illegal use, or restricting access to information. Although protecting knowledge has been considered an important component of KPC (Gold et al. 2001), SEs are only acquiring, applying and converting knowledge but not protecting it. This confirms that SEs are only informally managing the knowledge they have, without following formal
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Maria Granados et al. KM procedures, and without recognising the value of their knowledge. Consequently, it is not surprising that SEs are not interested in protecting what they do not know they know.
Measures of POP of Social Enterprises: The hypothesised variables of organisational performance of SEs were determined using previous performance measures developed for the sector. However, when assessing the outcomes of developing KMCs in SEs, not all the hypothesised elements were included in the model. This is the case of ‘Introduction of new products’, which relates to innovation. This can indicate that SEs are not necessarily managing their knowledge in order to develop new products or services.
Mediated or indirect effect of KOC in POP though its effect on KPC: The hypothesised KMSE model indicated that both organisational and process capabilities, together creating a KM capability, have an influence on POP of SEs. The initial findings from the quantitative study suggested a different scenario, as illustrated in Figure 2. It is determined that KOC have a significant influence on the effectiveness and development of KPC, but not a direct effect on POP. This denotes that, only by developing and implementing knowledge activities and procedures can the knowledge organisational capability, which is more inherent to the unique characteristics of SEs, improve performance of SEs.
In general, the empirical evidence from 432 senior managers of SEs in UK validates that SEs are currently developing KMCs, perhaps more informally rather than following formal structures of KM. This development results in a perceived increase in organisational performance, as assessed by the creation of social/environmental value, income, expenditure, workforce, teamwork, ability to deal with change, and customers’ and stakeholders’ satisfaction. In terms of KOCs, SEs are developing these by encouraging an environment of collaboration and trust, offering various opportunities for learning and staff development, and having members with a clear understanding of the purpose of the SE who are willing to improve the organisation’s performance through knowledge sharing. Moreover, the majority of SEs have organisational structures where members participate actively in decision‐ making and are encouraged to make their own decisions. In terms of KPCs, SEs have processes and mechanisms in place to acquire, convert and apply knowledge, but not yet to protect it. However, as was demonstrated with the SEM, organisational elements are not enough for creating a KMCs that improves the SE’s performance. SEs, after assuring that they have the organisational elements required, need to develop processes, activities and/or mechanisms to acquire, capture and apply the knowledge existent within the SE. It is through this sequence of progresses that KMCs can be developed in the SEs context to enhance their performance.
7. Conclusions and implications As has been demonstrated in previous studies, managing knowledge within an organisation can became a core capability, which results in competitive advantage and improvement of organisational performance. This paper has demonstrated that SEs can also develop this core capabilities and obtained similar outcomes that its counterparts in the private, public and non‐for‐profit enterprises. However, due to their idiosyncratic characteristics of multi‐stakeholder organisations, with multiple bottom lines and flat and democratic structures, SEs would need to devote more attention, time and effort in order to exploit the values of knowledge effectively. It is considered that effective management of knowledge requires coherent and well‐established KM pre‐ conditions in order to improve performance. Contradicting the findings of (Gold et al. 2001) and (Lee and Choi 2003), this paper demonstrates and proposes that Social Enterprises, or any similar organisation with multi‐ bottom lines, need to assess and focus on these organisational pre‐conditions, assuring a knowledge‐friendly enterprise, before devoting efforts and resources to develop KM activities and systems. Contributing to KM literature in general, this study presents a first attempt to understand how KM can be developed in different organisational contexts, perhaps more informally, and still improve performance of organisations. Moreover, it can help enterprises to achieve their objectives, whether they are social, environmental or economic, or any combination of the three. Despite these observations, it is clear that more research is needed on organisational behaviour of SEs and on knowledge management in organisations of different sizes, sectors and strategic orientations. While it appears
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Maria Granados et al. that the primary concepts of KMC can be transferred from large to small, multi‐strategy organisations, the empirical data presented in this paper demonstrate that the development of KMCs is likely to differ substantially among different types of organisation. The understanding of these differences would enable academics and practitioners to design, implement, and manage effective strategies with less risk of disruption to the organisations themselves.
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Research Regarding the Informational System (Information and Knowledge) Required for an Environmental Manager Ionut Viorel Herghiligiu1, Luminita Mihaela Lupu and Bogdan Budeanu Department of Engineering and Management, Gheorghe Asachi Technical University of Iasi, Romania herghiligiuionut@gmail.com Abstract: Environmental manager is the organizational agent (a) invested in environmental decision‐making process with power at organizational level (manager/s of Environmental Management Systems (EMS) if the organization has implemented and integrated a management system of this type) and (b) must possess all necessary environmental information and knowledge to effectively exercise the hierarchical position that he holds. Taking into account that the necessary of environmental information and knowledge (possess / received) is set arbitrarily by the environmental manager (considered to be the environmental knowledge manager), as is presented and resulted from: (1) analysis of environmental documentation of the of 15 large companies with industrial profile from Moldova Province, NE Region, Romania, (2) scientific specialized literature consulted in the doctoral research (e.g., Lupu L. in 1998), (3) discussions with different experts in environmental management, different deficiencies may occur (errors in environmental decisional‐ making process / disorganization of information activity / ineffectiveness / deficiencies in work environment e.g.) Therefore the main objective of this paper is to develop a conceptual framework for environmental manager for his own information system based on his own personal reasons which (a) satisfies the environmental information and knowledge necessary to make the best environmental decisions for organization, decisions based the judgment and understanding (as it shows by the authors: Ulman D.G. in 2003, Vandergriff L. in 2006 / 2008, Nonaka, I. si Takeuchi, H. in 1995), and (b) to clarify and systematize her own environmental knowledge. Keywords: environmental knowledge, environmental information system, environmental manager
1. Introduction Organizations tends towards flexibility with the main objective to meet promptly the consumer demand, but the dynamics of demand, correlated with uncertain and unstable characteristics of organization environment, fundamentally can disturb all levels of an organization (Deming, 1994; Herghiligiu et al., 2012.a; Gavronski I. et al., 2013). This rush to meet consumer needs by organizations has unfortunately sometimes negative environmental effects, materialized through delayed responses to various environmental problems and undoubtedly with a negative effect on the environment. However, organizations realize that regardless of their environmental impact, without taking sustainable environmental actions, cannot efficient interact with each other and with the market and cannot sustainably contribute to economics welfare (Herghiligiu and Lupu, 2012; Herghiligiu et al., 2012.b.). Therefore, it was and it is necessary that an organization to effectively manage its relationship with the environment, and this can be realized in terms of environmental performance only through implementing an Environmental Management System (EMS) (Herghiligiu et al., 2012.a.). In another way of thots, the substantial development of information technologies used at the organizational level, that identifies, stores and uses data and information, and today even knowledge, allow to these organizations to achieve a new level of performance in information management (Richards and Kabjian, 2001). This creates the basis for substantial improvement of management decision‐making process that is the essence of an organization management. Implementations of new techniques and information technologies have positive affect also on environmental management level of organizations by improving the efficiency of environmental activities undertaken by managers and employees; in this way is respected also the principle of continuous improvement of implemented environmental practices. Besides those mentioned above it is necessary to emphasize the need of an environmental manager/ manager that carries out various environmental activities at the organization level, to have developed an personal
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Ionut Viorel Herghiligiu, Luminita Mihaela Lupu and Bogdan Budeanu environmental informational system, that allows him to make good environmental decisions (decisions based on the best available environmental information and environmental knowledge).
2. Environment information system (information and knowledge) and environmental manager First, it is necessary to try to define the environmental information system, and thus it is considered to be: all the environmental data, environmental information and environmental knowledge, environmental information flows and circuits, the techniques and procedures for assessment and analysis of environmental information and environmental knowledge used to produce efficient environmental decisions and thus contributing to accomplish the environmental objectives and targets of the organization (adapted after Lupu, 2008). And secondly, the environmental manager is considered to be the person (a) empowered to make environmental decision at the level of environmental management of an organization (from the EMS level ‐ if the organization has implement and integrated such a management system/ if not, is considered as the person responsible for the environment), (b) who must have all necessary environmental information and knowledge to effectively exercise his hierarchical position he holds, and that (c) through the environmental activities undertaken or delegated, contribute to the achievement of the environmental objectives and targets set out at the organization level.
2.1 Researched methodology used Main goal: Increase the efficiency of the activity carried out by the Environmental Manager
Collecting data/ information: Analysis of environmental documentation of 15 companies in the Province of Moldova, NE Region, Romania (1) The specialized literature reviewed (2)
O.Main objective: Developing a conceptual theoretical framework (as a system) for the environmental informational necessity of environmental manager
Discussions with different specialists in environmental management (3) Other previous investigations carried out (4)
O.1. Designing a methodology for determining the necessary of environmental information and knowledge for environmental manager
Evaluation and analysis of collected data / information
O.2.Develop a matrix model that shows the correlation:
Design the methodology - O.1 and the matrix model - O.2.
SPECIFIC ACTIVITIES OF ENVIRONMENTAL MANAGER
Environmental Information
Environmental knowledge
FINAL CONCLUSIONS
Figure 1: Researched methodology
2.2 Methodology for determining the environmental informational necessary for environmental manager Determining the environmental informational necessary for an environmental manager (and not just of environmental manager/ can be extended to all the managers that conduct environmental activities at the organization level), can be achieved by developing of a personal system that could be characterized as that system that has all the environmental information and knowledge, ranked by criteria established by the environmental manager, and that actually contribute to the fulfillment of established environmental objectives and targets (adaptat dupa Lupu, 1998).
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Ionut Viorel Herghiligiu, Luminita Mihaela Lupu and Bogdan Budeanu So to prevent: the redundancy, parallelisms, overloading the communication channel, the inefficiency use of time and resources, orientation to the quantity and not to the quality of information, and to give a real foundation to the environmental informational necessary (environmental information and knowledge used by an environmental manager), it is proposed the following analysis methodology presented in the following (Figure 2): Identification and evaluation of the actuality of environmental objectives and targets established at level organization
NO
Identifying specific environmental objectives and targets for each department at the organization level
Assess the correlation between of environmental responsibility and of environmental activities
Evaluation/ collection of information and knowledge regarding the informational necessary
Determining the environmental activities of the manager to achieve of environmental objectives and targets
NO
Correlation activities - objectives
YES
Self-assessment of their of environmental knowledge and information needed
Focus group / survey to determine the environmental informational necessary
YES
Analysis of information / knowledge collected
Removing redundancy of collected information/ knowledge
NO
Evaluation the parallelism regarding the environmental activities between departments
Necessary of environmental information and knowledge (for managers)
YES
Figure 2: Proposed methodology for determination of environmental information and knowledge for a manager Regarding on the two methods of collecting environmental information/ knowledge (informational necessary), it should be mention that both, focus group and the survey must address the following issues: the relationship between manager needs and (a) grouping, (b) synthesis, (c) type, (d) quantity, (e) fairness, (f) error level, of the environmental information/ or knowledge; the environmental information and knowledge that could be absent from the informational necessary; the existence of situations in which decisions were made and various environmental activities were conducted without holding sufficient environmental information and knowledge; solutions used in cases where different environmental information and knowledge were missing; methods and techniques for verification of environmental information and knowledge; methods of informational error correction; use of informal sources; the environmental information and knowledge that are used across multiple environmental activities; and so on (adapted from Lupu, 1998). In the following (in the two tables that are designed as matrix model ‐ Table 1 and 2), taking into consideration pct. 1‐4 from the stage of collecting data/ information ‐ Figure 1, will be presented detailed and synthetic, the qualitative results obtained.
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Ionut Viorel Herghiligiu, Luminita Mihaela Lupu and Bogdan Budeanu Table 1: Matrix model I which shows the correlation between environmental activities and environmental informational necessity of managers (internal level)
ENVIRONMENTAL INFORMATION ENVIRONMENTAL KNOWLEDGE
PERFORMED ACTIVITIES ‐ INTERNALLY
ENVIRONMENTAL INFORMATION
ACTIVITY LEVEL
ADMINISTRATIVE
899
1. ongoing analysis of environmental legal compliance of the organization; 2. design and implementation (if doesn’t exists, and if the organization wants to undertake such a measure) an environmental management system (EMS) / develop the existing EMS; 3. analysis and processing of environmental data and information gathered from internal organizational (mathematical and statistical standpoint to obtain synthetic reports); 4. financial analysis carried out in the
TECHNICAL
1.informations regarding the levels and dynamics evolution of the pollutant emissions of the organization; 2.1. informations about the number and characteristics of the developed products that have minimal environmental impact; 2.2. informations on the functioning level regarding the internal processes of the organization and the environmental efficiency level of this process; 3. informations about the number/ frequency/ content/ results obtained after performing environmental audits; 4. informations about the number and results of field inspections; 5. informations on environmental programs developed in the past, about the content and timeliness of currently environmental programs used at the level organization. 1.knowledge about (a) the environmental legislative framework (maximum levels allowed) and (b) about the best methods and techniques for assessing and controlling the emission levels of pollutants; 2.1. theoretical knowledge regarding products and their impact on the environment/ new clean technologies for product with minimal impact on the environment; different ideas to improve/ change products; 2.2.practical and theoretical knowledge about processes and methods to improve the functioning of different process; ideas and strategies on improving processes; 3.knowledge about the internal audit mission (methodology); 4.knowledge about the use of various technical equipment used in the field inspection/ analysis and quantified the results obtained during the inspections / theoretical and practical knowledge about components of technological process; 5. knowledge on the best techniques for developing and updating the environmental management programs. 1.informations about the level regarding the legal compliance of the organization/ the level regarding the fines received for failure to comply with environmental regulations; 2. informations about the existence/ non‐existence of an EMS; 3. informations regarding the number and content of the various assessments made at the organization level; 4. informations about periodicity and different aspects tracked in various financial analyzes performed at of the Environmental Department level; 5. informations about the existence and content of the different measures developed for the emergency situations.
ENVIRONMENTAL ACTIVITIES (starting from environmental responsibilities of an environmental manager) Source: Herghiligiu et al., 2012.c. 1. evaluation and/ or control of different pollutant emissions levels regarding the activities of organization (negative environmental impact); 2. research on (a) obtaining products / (b) operation of the organization's internal processes, with minimal negative impact on the environment; 3. periodic audits and preparation of documents that contain the results; 4. periodic inspections and / or in field evaluations (at sections ‐ inspection of various parts of the technological process); 5. design / development of various environmental programs;
THE NATURE OF ACTIVITY
ENVIRONMENTAL INFORMATIONAL NECESSARY OF MANAGERS
Ionut Viorel Herghiligiu, Luminita Mihaela Lupu and Bogdan Budeanu
900
ENVIRONMENTAL KNOWLEDGE ENVIRONMENTAL INFORMATION ENVIRONMENTAL KNOWLEDGE
ACTIVITY LEVEL
1. planning / development / and implement trainings that aim to inform and increase the employee awareness on environmental issues; 2. design / development / and implement various environmental initiatives that have as their primary objective the internal relations between the environmental department and other internal departments; 3. design and / or development of environment organizational culture; 4. implementing and maintaining at all levels of the organization's the environmental policy;
SOCIAL
1. theoretical knowledge about environmental legislation / best methods used to observe the legislative changes / the analysis of impact induced on medium activities of the organization; 2. theoretical and practical knowledge about the design / implementation / integration and function of the EMS; 3. theoretical knowledge concerning the design and application of different research methodologies (tools of investigation / sampling / technical analysis / interpretation); 4. knowledge about content and stages of financial analysis; 5. theoretical knowledge about the complexity of emergency situations and about the best techniques used in their planning / development / and implementation. 1. information about the frequency / number / content and results obtained after environmental trainings for organization's staff (quantified through the evaluation and analysis of increased efficiency and effectiveness of environmental activities performed by employees / to achieve the environmental objectives and targets of the organization); 2. information about the type and frequency of interdepartmental collaborations that have as main objective the different environmental activities; 3.information about the level of internal development of environmental organisational culture; 4. information about the level of distribution and understanding by the organization's staff of environmental policy. 1. knowledge regarding: (a) the best techniques and methods used in environmental trainings, (b) the real need of environmental trainings; 2. knowledge about environmental information management system, ideas and strategies about the best ways to increase the efficiency and effectiveness of inter‐ departmental relationships; 3. knowledge about the best techniques and modern methods used in the development of environmental organizational culture; 4. knowledge about the best methods of dissemination of environmental policy / about the way the environmental policy should be implemented and maintained at the level of an organization.
ENVIRONMENTAL ACTIVITIES (starting from environmental responsibilities of an environmental manager) Source: Herghiligiu et al., 2012.c.
THE NATURE OF ACTIVITY
ENVIRONMENTAL INFORMATIONAL NECESSARY OF MANAGERS
Ionut Viorel Herghiligiu, Luminita Mihaela Lupu and Bogdan Budeanu Table 2: Matrix model II which shows the correlation between environmental activities and environmental informational necessity of managers (external level)
ENVIRONMENTAL INFORMATION
PERFORMED ACTIVITIES – EXTERNAL PURPOSE
ENVIRONMENTAL KNOWLEDGE
ACTIVITY LEVEL
901
TECHNICAL
1. information about: (a) previous simulations of potential environmental impacts performed, (b) the criteria used in these simulations of environmental impact, (c) the level of use of simulations results; 2.1. information about the technological and moral wear of equipment used at organizational level; 2.2. information about the financial resources that could be used by the organization to purchase clean technologies; 3. Information about: the number / frequency / previous organization's participation to different scientific events; 4. information about (a) the level of expertise of potential external partners of the organizations, (b) previous inspections performed at the level of different components of the technological process; 5.idem point 4. 1.1. theoretical knowledge about software programs that can be used in making simulations of the induced impact on the environment organization’s activities; 1.2. knowledge of techniques used to evaluate the relationship between pollutant emissions and the best methods / simulation programs of the induced impact on the environment; 2.1. technical knowledge about the machinery and equipment used by the organization; 2.2. knowledge about environmental operational performance evaluation of the equipment used in the activities of the organization; 2.3. theoretical knowledge about clean technologies; 3.knowledge about the best communication techniques at scientific events; clear knowledge about environmental objectives and targets of the organization; 4. knowledge about the best ways to evaluate the technological process; 5. knowledge about design / development / implementation / functioning of environment programs;
ENVIRONMENTAL ACTIVITIES (starting from environmental responsibilities of an environmental manager) Source: Herghiligiu et al., 2012.c. 1. potential environmental impacts simulation based on assessments and / or control current levels of emissions regarding the pollutants from the organization; 2. research on the possibility of implementing at the organization level of new technologies with minimum impact on the environment; 3. representing the organization on different environmental scientific manifestations (congresses / conferences ‐ discussions on various clean technologies and so on); 4. partnerships and collaborations with various external partners (external experts) on various measurements / inspections regarding the different parts of the technological process; 5. partnerships and collaborations with different external partners (external experts) on various environmental programs ‐ design / development at the organization level;
THE NATURE OF ACTIVITY
ENVIRONMENTAL INFORMATIONAL NECESSARY OF MANAGERS
Ionut Viorel Herghiligiu, Luminita Mihaela Lupu and Bogdan Budeanu
ENVIRONMENTAL INFORMATION ENVIRONMENTAL KNOWLEDGE ENVIRONMENTAL INFORMATION ENVIRONMENTAL KNOWLEDGE
SOCIAL
1. partnerships and collaborations with various external partners on various environmental initiatives; 2. management of the relationship between organization and environmental NGOs. 3. management of the relationship between the organization and the public;
ADMINISTRATIVE
1. information about any fines received by the organization from different qualified agencies in environmental issues; 2. information about (a) the content and form of environmental reporting regarding environmental issues managed, (b) previous reports made regarding various environmental problems occur; 3. information about the existing environmental authorizations for various activities of the organization; 4. information about the content and results of previous and in progress environmental projects at organizational level; 5. information about current environmental regulations required and about the type of the link between the organization and external stakeholders; 1.ideas (or even medium and long term strategies) to improve the relationship between the organization and institutions environmental issues; 2.knowledge about the form / content / complexity of environmental reporting; 3. knowledge of the procedure to obtain various environmental authorizations from the qualified agencies; 4. theoretical and practical knowledge of environmental project management and cost benefit analysis; 5. knowledge of the regulations established by the state and about stakeholder theory; 1. information about the number of partnerships and previous / current collaborations with various external partners concerning various environmental initiatives; 2. and 3. information about community notification actions / ONG at local level about activities carried out by the organization, information about informational satisfaction level of the community regarding the activities carried out by organization (activities that have an impact on the environment); 1.communication and negotiation knowledge that have as main goal partnerships completion and collaborations with various external partners concerning various environmental initiatives; 2. and 3. knowledge of the best techniques of communication and negotiation / regarding to evaluation methods of individuals perception;
ACTIVITY LEVEL
ENVIRONMENTAL ACTIVITIES (starting from environmental responsibilities of an environmental manager) Source: Herghiligiu et al., 2012.c. 1. managing the relationship between the organization and various public institutions ‐ ability in environmental issues (environmental agencies, environment ministry, and so on); 2. performing of different environmental reports (on various current environmental actions or extraordinary) regarding the management of environmental issues; 3.o obtaining of different environmental permits from the institutions that have the ability to provide it for the organization; 4. managing environmental projects undertaken by the organization (funded national or international); 5. management of the relationship between organization and environmental guidelines set by state and the management of the relationship between the organization and external stakeholders (customers / suppliers / community and so on);
THE NATURE OF ACTIVITY
ENVIRONMENTAL INFORMATIONAL NECESSARY OF MANAGERS
3. Discussion Taking into consideration the informational character of the activities developed by an environmental manager (and not only) it is necessary to be given a special attention to the environmental information’s and knowledge; as a consequence the environmental manager must build his own system to managed this type of information’s and knowledge.
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Ionut Viorel Herghiligiu, Luminita Mihaela Lupu and Bogdan Budeanu This paper bring a practical contribution concretized by presenting the most important environmental information and knowledge (necessary to make the best environmental decisions), and thus to improve the environmental activities performed in an organization. It is necessary also to emphasize that this work, besides classifying and systematizing the environmental information and knowledge, and besides proposing original methodologies and matrices, bring a contribution at the processing level (completion/ selection/ evaluation/ verification/ synthesis ‐ environmental information and knowledge) of environmental information and knowledge. Therefore it is proposed the following:
at the level of the completion stage regarding the environmental information and knowledge, environmental manager should evaluate what environmental information and knowledge hold and supplement them, if is necessary, through different requests (either from the same source/ or from another source);
at the level of the selection stage regarding the environmental information and knowledge, environmental manager should manage the amount of environmental information and knowledge in a optimum way with the purpose to select the essential one (personal criteria; starting from existing environmental principles at the level of environmental policy of the organization’s);
at the level of the evaluation stage regarding the environmental information and knowledge, environmental manager should analyze the value and the reliability of environmental information and knowledge that he hold;
at the level of the verification stage regarding the environmental information and knowledge, environmental manager should analyze the sources of environmental information and knowledge and also to assume the responsibility for their quality and truthfulness;
at the level of the synthesis stage regarding the environmental information and knowledge, environmental manager should develop a hierarchy of environmental principles and personal considerations that could contribute to the quality of this step.
4. Conclusions Taking into account the complexities and particularities of their environmental activities, managers involved need methodologies, models, matrices, and so on, that satisfy their needs for environmental information and knowledge to take the best environmental decisions based on judgment and understanding. The proposed methodology for determination of environmental information and knowledge for a manager together with model matrices (shows the environmental information requirements) bring a positive contribution because (a) allow the organization of environmental information activities within each department / division that belongs to an organization (for managers involved in various environmental activities), (b) allow a selection of environmental information on scientific basis (but not refereed and without foundation), (c) removes redundant information and knowledge environment used in the environmental activities, (d) remove overloading communication channels with useless information, (e) contribute significantly to the implementation / integration and management system environment. With all this, there is need to be said that for the proposed methodology for determination of environmental information and knowledge for a manager to be effective: (a) the organizational structure needs to be flexible, (b) environmental liabilities to be determined clear and consistent with environmental activities undertaken, (c) each manager (environmental manager or manager that perform activities environmental) to have the capability to define the necessary information that is needed either for the developing of environmental activities, either for environmental decision making, and (d) each manager must periodically introspect their necessary environmental information and knowledge in order to improve / update / optimize the performed activity.
Acknowledgements This paper was realised with the support of POSDRU CUANTUMDOC “DOCTORAL STUDIES FOR EUROPEAN PERFORMANCES IN RESEARCH AND INOVATION” ID79407 project funded by the European Social Found and Romanian Government.
References Deming, W.E. (1994) The New Economics for Industry, Government and Education, Massachusetts Institute of Technology, Cambridge, MA.
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Ionut Viorel Herghiligiu, Luminita Mihaela Lupu and Bogdan Budeanu Herghiligiu I.V., Lupu M.L., Epure S.P., (2012.b.), „Contributions to environmental decision making by developing environmental adaptive decision models”, Proceedings of Modern Technologies, Quality and Innovation ‐ New face of TMCR, Sinaia, Romania, Vol. I, pp. 437‐440. Herghiligiu I.V., Lupu M.L., Robledo C., (2012.a.), „Necessity of change environmental management system architecture – introduction”, Quality – Access to Success, Vol. 13, Issue Suppl. No. 5, pp. 175‐178. Herghiligiu, I.V., Lupu, M.L. (2012), „Performance analysis methodology of environmental knowledge at organizations th level”, Proceedings of 13 Europ. Conf. on Knowledge Management ‐ ECKM 2012, Cartagena, Spain, Vol. II, pp. 1402‐ 1410. Herghiligiu, I.V., Lupu, M.L., Robledo, C., Kobi, A. (2012.c.) „A new conceptual framework for environmental decision at the organizational level based on fractal philosophy”, Environmental Engineering and Management Journal (http://omicron.ch.tuiasi.ro/EEMJ/index.htm), (in press). Lupu, L.M. (1998) “Research on improving the decisional informational system of a company with chemical profile”, PhD dissertation, “Gheorghe Asachi” Technical University of Iasi, Romania. Nonaka, I. and Takeuchi, H. (1995), The Knowledge Creating Company, Oxford University Press, Oxford. Richards, D.J. and Kabjian, M.R. (2001) „Improving environmental knowledge sharing”, Information System and the Environment, pp. 59‐79, [online], National Academy of Engineering, http://www.nap.edu/catalog/6322.html. Ullman, D.G. (2004) “Decision management: punctuating the process”, Incose Insight, 6 ( 2), January. Vandergriff, L.J. (2006) “Unified approach to decision support for agile knowledge‐based enterprises”, DSc. dissertation, George Washington University, Washington, DC. Vandergriff, L.J. (2008) “Welcome to the Intelligence Age: an examination of intelligence as a complex venture emergent behavior”, Journal of information and knowledge management systems, Vol. 38, No. 4, pp. 432‐444.
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The Impact of Emotional Knowledge on key Aspects of the Economy Andrei‐Alexandru Morosan, Gabriela Arionesei, Paul‐Panfil Ivan and Cristian‐Valentin Hapenciuc Stefan cel Mare University of Suceava, Romania alexandrumorosan@gmail.com gabriela.arionesei@gmail.com ivanpaul.ro@gmail.com valentinh@seap.usv.ro Abstract: Emotional knowledge consists of elements that are more and more related the decision processes and individual performance. In literature and in practice, this concept has been mostly used in studies and research related to decision‐ making people, people with high hierarchical positions in various organizations or in theory of education and behavior. The question that this paper wants to address is whether the term “emotional knowledge” should be limited to those individuals with high hierarchical positions or it is a concept that can be studied at the level of a mass of people (population), since emotional intelligence affects the overall daily decision‐making processes and all those minor decisions give rise to major phenomena. In order to determine the influence of emotional knowledge and their need to be studied at the level of mass phenomenon, this article proposes an analysis of the impact emotional knowledge has on certain key elements of the economic system, such as human resources (both in terms of quantity, and quality) and its specific problems (focusing on labor migration, which is one of the issues that require attention in Romania), strategic orientation of economic activities (policies of sustainable development, orientation towards activities with minimum consumption of resources and minimal environmental impact, and investment policy). Moreover, this paper will also take into account how emotional management might affect orientation of investments towards sustainable development and towards the valorification of touristic potential of each country. Analysis is limited to countries in Central and Eastern Europe that joined the EU during the period 2004 ‐ 2007, as they take large efforts to accelerate economic growth and their national economies have undergone extensive changes and reforms, emotional skills being a key factor that could explain the structural resulted differences. Keywords: emotional intelligence, decision making process, emotional factors
1. Introduction The term “emotional intelligence” was firstly used in an article in 1990, by psychologists Peter Salovey and John Mayer and since then has become enormously popular. They described this notion as “a type of social intelligence that involves the ability to monitor emotions, to discriminate among them, and to use the information to guide thoughts and actions” (Salovey and Mayer, 1993). When we talk about emotional knowledge, we think about capabilities, competencies, and skills that influence someone's ability to succeed in coping with environmental demands and pressures that directly affect a person's overall psychological well‐being. Still, there are some barriers to effective control of emotions. The goal is to understand emotions and emotional intelligence in order to learn how they can be managed. Emotional intelligence has been purported to be distinct from traditional IQ and crucial in predicting many real‐life outcomes. The aim of this paper is given by the need to see the latest changes in the society we live in, from the economic and human behaviour point of view. It is believed that the future society will be stratified by differences in intelligence and, thus, social mobility will be influenced by these differences. Hence, the key factor in this type of social division is intelligence and not social status of a person or his origin. The famous types of intelligence proposed by Gardner (2008) have been translated by Albrecht into a model, considered practical and useful for psychologists. Thus, ASPEAK model assumes that people have six main dimensions of intelligence:
Abstract intelligence (A) which involves rational concepts, verbal skills, mathematical and symbolic information.
Social intelligence (S) refers to the efficient and successful interaction with other different social contexts.
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Practical intelligence (P) includes the ability to solve current problems and be persuasive in carrying out activities.
Emotional intelligence (E) leads to insights and to the ability to control a positive reaction.
Appreciation (aesthetic) intelligence (A) refers to the accurate form of appreciation, design and relationship.
Kinesthetic intelligence (K) involving capabilities of the entire body and developing specific skills.
In order to see whether emotional intelligence is limited to people with positions in top management or it can be met at the level of a population, we want to see if there is a significant correlation between emotional intelligence and various economic and social achievements. In this regard, we will take into consideration three areas of the socio‐economic field: demography, absorption of structural funds and tourism. Our aim is to find the role of emotional intelligence factors and their effect on certain indicators of the mentioned areas, such as emigration and immigration rate, rate of contracted funds, number of tourists’ arrivals and overnight stays in hotels. If academic intelligence gives us information about our performance in terms of thinking and its operations, emotional intelligence gives us information about our performance through emotions and feelings. Moreover, it is believed that social intelligence is different from academic intelligence and the difference can easily be seen in our daily lives. Some people who are successful in solving academic problems may have difficulties in coping with socio‐economic situations. They also may face problems in understanding the others, while others, who do not excel in terms of academic intelligence, can effectively relate and appropriately respond in social contexts. Before going further with emotional intelligence and its importance in the economic environment, we want to point out the difference between these two types of intelligence: academic intelligence (IQ‐ intelligence quotient) which is the kind of intelligence that expresses our capacity for learning and mental development, the ability to theorize and to gain knowledge and information in various fields and emotional intelligence (EI) which is our ability to learn, express and manage feelings, helping us to know and understand others' feelings (to be empathetic). Recently, people discuss about more than a new management, that of emotional intelligence; it is about the situation when an organization pays attention to emotional abilities of its members, ensuring their compatibility in terms of affective emotional relationships. Research in this area has shown that managers and employees with a high emotional intelligence are more successful professionally, are intrinsically motivated, more positive, cooperative and able to establish positive relationship with others. One important element of emotional intelligence is the empathy and the ability to create effective relationships with others. A key to improve emotional intelligence is understanding behavioral preferences, not just of one's self but, more importantly, of others’, and understanding how to adapt behaviors to achieve the desired outcome. Empathy, as an element of emotional and social intelligence, cannot be performed without the identification with partners’ feelings. Even if people have a native empathic potential, empathic behavior is learned primarily through social and later in the survival tendency of group solidarity. Emotional knowledge impacts a variety of business areas, including recruiting and job selection, sales results, leadership performance, investments, tourism development, migration and other economic aspects. Our research indicates a certain connection between economic performance and emotional intelligence, referring to optimism and pessimism. Optimists and pessimists differ in ways of approaching problems and challenges, affecting how they cope with situations in life. Carver and Scheier (2003) believe pessimists fear that additional failures will come from current struggles, therefore optimists are more prone to believe that good outcomes will happen and see a future of opportunities, so they tend to approach adversity with confidence and persistence, while they are more doubtful and hesitant.
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Andrei‐Alexandru Morosan et al. How does emotional intelligence, namely optimism and pessimism, affect strategic orientation of economic activities such as: sustainable development, investment policies, labor migration, and tourism development? This is the question we will further try to answer in this paper.
2. The impact of emotional factors Emotional factors are key elements in any decisional process. The question that this article wants to address is whether the concept of emotional intelligence should be limited to the individuals with high hierarchical positions, or it is a concept that can be studied at a mass of people, since most individuals take decisions in everyday life, and all these minor decisions give rise to major events. Considering the components of the concept of emotional intelligence and the fact that it involves: perceiving emotion (ex. ability to identify emotions in faces, pictures), facilitating thought with emotion (ex. ability to harness emotional information and directionality to enhance thinking), understanding emotion (ex. ability to comprehend emotional information about relationships, transitions from one emotion to another, linguistic information about emotions) and managing emotion (ex. ability to manage emotions and emotional relationships for personal and interpersonal growth) (Mayer, Salovey, Caruso and Sitarenios, 2001); and the fact that emotional factors like optimism and pessimism tend to affect decision makers we based the empirical analysis on the following assumption: if the impact of emotional intelligence is negligible, then the impact of emotional factors (such as optimism and pessimism) would be great. If, on the other hand, the impact of emotional intelligence on the analyzed phenomena is great, we would expect that emotional factors have a small impact on the decision maker, because he is: perceiving emotion, facilitating thought with emotion, understanding emotion and managing emotion, thus he is not influenced by emotions, states of mind or other basic instincts. Based on this assumption we devised an empirical test through which we will try to correlate decision outcomes with emotional factor such as optimism and pessimism. If then we identify significant correlations, then we can conclude that the impact of emotional intelligence is negligible, if, on the other hand, we do not find significant correlations than we can assume that emotional intelligence is a factor to be analyzed when dealing with mass phenomena. Emotional factors such as optimism and pessimism were selected for analysis because they have the biggest potential to affect the decision making process and because statistical data can be obtained from reliable sources. In order to answer this question, we will analyze in the case study the impact of these factors in three areas:
Demographic indicators;
Indicators of absorption of structural funds (funds meant to stimulate economic growth);
Indicators in tourism.
2.1 Methodological part The data used are secondary data obtained from various sources:
Demographic indicators ‐ Eurostat;
Indicators of absorption – Report of the International Audit Firm KPMG ‐ EU Funds in Central and Eastern Europe end of 2010;
Indicators of tourism ‐ Eurostat;
Indicators of emotional factors ‐ The global reports on emotions published by Gallup Worldview database.
Gallup measured positive and negative emotions in 2011 using five questions. These questions asked people whether they experienced a lot of enjoyment the day before the survey and whether they felt respected, well‐ rested, laughed and smiled a lot, and did or learned something interesting. The average percentage of respondents worldwide who said "yes" to these five questions reflects a relatively optimistic world. The opposite reflects a relatively pessimistic world.
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Andrei‐Alexandru Morosan et al. Results are based on telephone and face‐to‐face interviews with 1,000 adults in each country, aged 15 and older. For results based on the total sample of national adults, one can say with 95% confidence that the maximum margin of sampling error ranged from ±3.4 percentage points to ±3.9 percentage points. We chose these indicators because the Gallup study is a reliable one, with a representative sample and goes from positive and negative emotions to the optimistic and pessimistic state of mind. At the same time, awareness of positive or negative emotions is one of the key aspects of emotional intelligence. Possible correlations will be analyzed by graphical representation, with trend lines, and also using Pearson index that takes values in the interval [‐1,1]. A value which tends to ‐1 indicates a strong negative correlation, a value which tends to 0 indicates a very weak correlation or absence of this correlation, and a value of the coefficient which tends to 1 indicates a strong direct correlation.
2.2 The impact of emotional factors on demographic indicators The state of mind of a decision maker (optimistic or pessimistic) is a factor that should have an impact when it comes to his decision to emigrate. The countries from Central and Eastern Europe that recently joined the European Union face considerable problems in terms of labor migration. Given the scope of this process, we consider as being appropriate the analysis of this area. We expect that a country whose population is relatively pessimistic to present a relatively low tendency to migrate.
Figure 1: Correlation between pessimism and emigration rate in 2011 Table 1: Pearson correlation coefficient Pessimism
Emigration Rate Pearson Correlation Sig. (2‐tailed) N
‐0,472 0,168 10
Source: Authors’ calculations using SPSS
Analyzing data obtained from the graphic method and from the Pearson coefficient, we find that there is a reverse connection (estimated trend, descending from left to right and the coefficient is negative), but its intensity is very weak link, pessimism showing a minimal influence on emigration (the value obtained for Significance is greater than 0,05 which would be an minimal acceptable level for the significance of a correlation, any value greater than this dismisses the correlation). General optimism of a population inevitably leads to the design of a positive image for people from outside and this is the reason why between optimism and immigration should be a direct relationship. When analyzing both Figure 2 and Table 2, we observe a direct link (the coefficient has a positive value and the trend is upward from left to right) between the two variables, this time the intensity being a lower one (the coefficient has the value 0.1 very close to the lowest level 0), the significance is still over the minimal level of 0,05. We can deduce that emotional factors have no decisive influence for migration because it is based on a series of other different factors with major influences. This can be also deduced from the fact that, at the global
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Andrei‐Alexandru Morosan et al. level, the most optimistic countries are not necessarily the highest situated from the economic and standard of living point of view.
Figure 2: Correlation between optimism and immigration rate in 2011 Table 2: Pearson correlation coefficient Optimism
Immigration Rate
Pearson Correlation
0,101
Sig. (2‐tailed) N
0,782 10
Source: Authors’ calculations using SPSS
2.3 The impact of emotional factors on the use of structural funds Traditionally, EU funds absorption is influenced by three categories of factors (Nei 2002, Horvat 2004, Oprescu 2006):
Macro‐economic factors;
Administrative absorption capacity;
Financial absorption capacity.
However, these three categories of factors cannot fully explain the evolution of structural funds absorption, lower impact factors not being taken into consideration. One of them could be optimism‐pessimism report, at least for projects that have private beneficiaries (programs presented in table no. 3). In a country characterized by optimism, the opening towards entrepreneurship, investment decisions, and, thus, the use of EU funds should be more frequent, and, in addition, the projects should be higher, all these things concluding to higher rate of contracting funds. Table 3: Programs addressed for business environment No. 1. 2. 3. 4. 5. 6. 7. 8. 9. 10.
Country Bulgaria
Analyzed programs Development of the Competitiveness of the Bulgarian Economy OP Education for Competitiveness OP Czech Republic Enterprise and Innovation OP Research and Development for Innovations OP Estonia Development of Economic Environment OP Hungary Economic Development OP Latvia Entrepreneurship and Innovations OP Lithuania Economical Growth OP Poland Innovative Economy OP Romania Increase of Economic Competitiveness OP Competitiveness and Economic Growth OP Slovakia Research and Development OP Slovenia Strengthening Regional Development Potentials OP Source: Data taken from KPMG – EU Funds in Central and Eastern Europe 2010
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Figure 3: Correlation between optimism and rate of contracted funds in 2011 Table 4: Pearson correlation coefficient Optimism Pearson Correlation 0,470 Contracting Rate Sig. (2‐tailed) 0,171 N 10 Source: Authors’ calculations using SPSS
If we analyze from the perspective of optimism, we find that it is correlated with the rate of contracted funds within the meaning described above; yet, the intensity is relatively weak, with a correlation coefficient reaching the 0.470 value, being closer to the middle of the interval (0, 1); the significance is still over the minimal level of 0,05.
Figure 4: Correlation between pessimism and rate of contracted funds in 2011 Table 5: Pearson correlation coefficient Pessimism Pearson Correlation ‐0,618 Contracting Rate Sig. (2‐tailed) 0,057 N 10 Source: Authors’ calculations using SPSS
In the case of pessimism, there can be observed the correlation (the connection is reverse in both Figure 4 and Table 5), but, in this case, the intensity of the relationship is greater (coefficient having the value ‐0.618, tending to 1).
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Andrei‐Alexandru Morosan et al. This correlation cannot be treated as negligible the significance levels is slightly over the 0,05 limit). This is the strongest correlation we identified so far.
2.4 The impact of emotional factors on tourism indicators Tourism activity is measured through different indicators and national economies are classified according to their rankings within international classifications. However, the travel industry is characterized by a number of specific issues, out of which we selected those considered relevant for our case study. Similar to the analysis of demographic indicators, for the tourism domain optimism should be a stimulating factor for selected indicators, therefore it should show a direct connection.
Figure 5: Correlation between optimism and arrivals in 2011 Table 6 ‐ Pearson Correlation Coefficient Optimism Pearson Correlation 0,496 Sig. (2‐tailed) 0,145 Arrival Rate N 10 Source: Authors’ calculations using SPSS
We find that emotional state has a medium impact on tourism indicators. The link between total arrivals, expressed as a percentage by dividing the population, and optimism is of a medium direct intensity. The significance level is over the 0,05 limit, thus this correlation is negligible. Optimists assume that the challenges can be handled successfully, while disaster is always anticipated by pessimists. Therefore, optimists are likely to engage in focused and active coping, instead of showing signs of disengagement or giving up, when compare to pessimists (Carver and Scheier, 2003).
Figure 6: Correlation between optimism and overnight stays in hotels and similar facilities in 2011
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Andrei‐Alexandru Morosan et al. Table 7: Pearson correlation coefficient Optimism Pearson Correlation .408 Overnight Stays Sig. (2‐tailed) .242 Rate N 10 Source: Authors’ calculations using SPSS
Similarly with arrivals, number of overnight stays in hotels and similar establishments is moderately influenced by optimism, this correlation being a result of the strong relationship between arrivals and optimism, since the number of nights is influenced, in its turn, by the number of arrivals.
3. Conclusions and discussions The results of the empirical analysis shows that the impact of emotional factors on the decision making process is relatively low. Neither one of our test showed a significant link (the significance value under 0,05). This being the case, we conclude that emotional intelligence appears as a catalyst, empowering the decision maker, not letting him appeal to emotional factors, mental states or other impulses of such. The fact that decision outcomes do not correlate with emotional factors, proves the importance of emotional intelligence in every phenomenon. Results of the present study might have both theoretical and practical implications. From a theoretical perspective, the results may be added to the basic understanding of the interrelationships among optimism, pessimism and emotional intelligence (viewed as a catalyst) in different economic environments or situations. The present study is not without its limitations. Firstly, it uses secondary data therefore any undetected errors from the first studies are automatically transposed to the present results. Secondly, we cannot assume whether the present results could be generalized or they are specific for populations from Central and Eastern Europe. Thirdly we must take into account the limitations of research instruments used for measuring emotional factors and states of mind, since human emotions are very complex and hard to observe using conventional methods. The results of the present study can constitute the argument for future research regarding the empirical relation between emotional intelligence and the process of decision making, on a wider base and on a larger scale. Therefore the research should be extended to other geographical and cultural areas, for example: other European countries or other continents.
References Carver, C.S. and Scheier, M. (2003) Optimism. In S.J. Lopez & C.R. Snyder (Eds.) Positive psychological assessment: a handbook of models and measures. Washington, DC: American Psychological Association Gardner, H. (2008), Inteligențe multiple. Noi orizonturi pentru teorie și practica, Ed. Sigma, Bucharest Horvat A. and Gunther M. (2004) Regional development, Absorption problems and the EU Structural Funds; Some aspects regarding administrative absorption capacity in the Czech Republic, Estonia, Hungary, Slovakia and Slovenia, ERSA Conference, Austria, Vienna Mayer J., Salovey P., Caruso D., Sitarenios G. (2001), Emotional Intelligence as a Standard Inteligence, American Psychological Association Inc. Emotion Vol. 1 No. 2 p. 232‐242; Mayer J., Perkins D., Caruso, D., Salovey P., (2001), Emotional inteligence and giftedness, Academic Research Library, Roeper Review, Vol 23, nr. 3, article available at www.sagepublication.com NEI Regional and Urban Development (2002) Key Indicators for Candidate Countries to Effectively Manage the Structural Funds, Rotterdam Oprescu G., Constantin D.L., Ilie F. and Pislaru D. (2006) Analysis of Absorption Capacity of The EU Funds in Romania, European Institute of Romania – Pre‐accession impact studies III, Bucharest Roco, M., (2001), Creativitate si inteligența emoționala, Ed. Polirom, Iasi Salovey, P., Mayer, J.D. (1990) Emotional intelligence. Imagination, Cognition and Personality, no. 9, Baywood Publishing Co., Inc, New York KPMG (2012) EU Funds in Central and Eastern Europe 2011 KPMG (2011) EU Funds in Central and Eastern Europe Progress Report 2007‐2010 KPMG (2010) EU Funds in Central and Eastern Europe Progress Report 2007‐2009 KPMG (2009) EU Funds in Central and Eastern Europe Progress Report 2007‐2008 Eurostat ‐ http://epp.eurostat.ec.europa.euhttp://epp.eurostat.ec.europa.eu/ Gallup ‐ https://worldview.gallup.com/https://worldview.gallup.com/
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Factors for Knowledge Sharing Behaviour to Develop Trust in Professional Organisations Environment Salah Rana, Malcolm Crowe and Abel Usoro University of the West of Scotland, UK Salah.rana@uws.ac.uk, Malsolm.crowe@uws.ac.uk, Abel.usoro@uws.ac.uk Abstract: This paper develops a new theoretical model to represent the important knowledge sharing factors and their role in development of trust in professional learning organisations. These factors are built upon the assumptions of organisations knowledge sharing behaviour and the role of technological advancement/awareness based on literature. The factors we have introduced are hard and soft ties, habits and their relationships to tie’s (soft/hard), development of personal mastery, development of system thinking, internal dialogue with the use of technology, and resource exchange (physical and explicit). We have also argued that some factors such as age, gender, citizenship, and marital status are not the significant factors in building trust for knowledge sharing behaviour in professional real organisations environments. Keywords: knowledge, learning, knowledge sharing, trust, organisations
1. Introduction We have created a generic model for knowledge sharing to develop trust based on available empirical literature and on our own critical analysis. In the start we have developed the general understanding about knowledge sharing in different organisations and their consideration of trust in professional environment. After that we have mentioned few available trust models in the literature to abstract the general perception of researchers about the development of trust in knowledge sharing behaviour in organisations. Based on a critique of existing work we have developed our model for building trust for knowledge sharing.
1.1 Knowledge and learning Learning organisation buzzwords originated in 1990 (Villardi and Leitao, 1999) and refers as an on‐going process due to the ‘continuous evolutionary determinants’ of organisations such as generation of new knowledge (Kennedy, 2007). According to Chang, et.al (2005), organisation theory is based on development of understanding, determining and identifying the organisational performance factors. The link between learning and knowledge had been identified few years back in learning organisations in literature (Kennedy, 2007). Stephens (2008, p 172) explained the two forms of knowledge namely tacit knowledge (Tsoukas, 2005) and explicit knowledge. Tacit knowledge cannot be openly expressed and taught. However, it can be transformed by using different techniques like combination of demonstration, illustrations, experiences and stories. Venkitachalam and Busch (1999, p 357) refer to tacit knowledge as a procedural knowledge for the organisation also known as practical intelligence. Nissen and Levitt (Nissen and Levitt, 2002) expressed tacit knowledge as internalisation or learning by doing. Explicit knowledge is more formatted and presentable form of information which can be codified, articulate, and could be stored in various forms of available formats ((Stephens, 2008, pg‐172).
1.2 Knowledge flow or sharing Due to the strong logical link between learning and knowledge it is very important for companies to improve knowledge sharing behaviour to achieve the organisations shared vision. Knowledge sharing is among the important processes of knowledge management. Knowledge sharing enhances innovative performance and reduces redundant learning efforts (Scarbrough, 2001; Cavusgil et al., 2002), and provides a source of competitive advantage. Knowledge flows/sharing is divided into two main categories namely knowledge inflows also known as recipients of knowledge and knowledge outflows also called source of knowledge (Michailova and Mustaffa, 2012, p 385). Michailova and Mustaffa discussed different variables involved in knowledge sharing/transfer/flow in multinational companies (MNC) at recipient end such as speed of transfer, perceived benefits and effectiveness of transfer, and cost of transfer (Michailova and Mustaffa, 2012, p 386).
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Salah Rana, Malcolm Crowe and Abel Usoro There are various mechanisms to facilitate knowledge sharing/flow such as coordination, control, socialisation, HRM training programs, and knowledge management infrastructure. Further, Michailova and Mustaffa (2012, p 388) elaborate the knowledge mechanism into hard (use of technology and hard knowledge management infrastructure) and soft (social networks, and socialisations) mechanisms; and they have also noticed the shift from hard to soft mechanism. Further, Michailova and Mustaffa (2012, p 385‐388) discussed four main clusters involved in knowledge flows which are outcomes of knowledge flow, characteristics of knowledge, characteristics of actors and characteristics of relationships between actors. After detailed literature review of all four clusters, Michailova and Mustaffa (2012, p 389) found that knowledge inflows received more research attention as opposed to knowledge outflows, which is difficult to manage due lack of trust. It is also found that knowledge sharing/flow research is more biased on quantitative approach according to the literature. Thus, authors emphasised under‐representation of conceptual, qualitative and mixed methods studies (Michailova and Mustaffa, 2012, p 387). Michailova and Mustaffa (2012, p 387) further elaborate relational elements of knowledge sharing such as trust, norms, expectations, commitment, identifications with other actors, and cognitive elements which involves practices, mindset, goals, paradigms, and shared discussion. In another place Michailova and Mustaffa (2012, p 388) also mentioned the importance of other aspects such as institutional, organizational, and cultural distance. According to the ODell and Hubert (2011) many organisations face looming knowledge retention and transfer issues, regardless of industry, annual revenue, or their number of employees. Durst and Advardsson (2012, p 881) have also mentioned that knowledge sharing is time consuming and comparatively need a firm level of trust. Therefore, in organisations trust has been identified as an extremely important issue in knowledge sharing behaviour as identified by several authors as well (Harden, 2012; Evan, 2012; Michailova and Mustaffa, 2012).
1.3 Traditional knowledge sharing model for trust There are different knowledge sharing models had been developed to build trust for knowledge sharing within the organisations (Venkitachalam and P. Busch, 2012). APQC (American Productivity and Quality Centre) performed on a study ‘The Role of Evolving Technologies’, where they have highlighted how leading organisations like IBM and HP have adapted social tools to serve for KM strategies (Dell and Hubert, 2011). The APQC study was contextual and applies to large organisations. He (Evan, 2012) investigated trust factor in detail in the context of knowledge sharing behaviour. Evans (Evan, 2012) discussed different knowledge sharing models by Davis et al., 1995, p.715; Yang and Farn, 2009) and their factors were based on ability, benevolence and integrity. Evan (2012) identified 14 different factors of trust in his literature map for knowledge sharing which he condensed into five main cognitive factors such as sharing vision, tie strength, shared language, homophile, and relationship duration. Evans (2012) investigated and developed his new theoretical framework in two main interrelated categories which are a) trust which involves age, sex, race, ethnicity, citizenship and immigration and b) knowledge sharing behaviour includes education, experience, and marital status. In his model he has mostly found positive relationship and less significance between most of identified factors with trust and knowledge sharing behaviour; which shows the clear behaviours of professionals towards practical approach of developing trust based on realistic attributes in the professional organisations which are discussed in our proposed trust framework for knowledge sharing behaviour.
2. Critical analysis Several models have been developed and proposed in the literature; however, in real world organisations it is near to impossible to find that most of the proposed models in literature have been fully adopted. Technology has a very huge impact on the current era by reducing distance and face to face communication gaps for the remote locations of the same organisation. Wikipedia discussed about Millennial generation and their mental compatibility with the technological advancement; as it is stated that by Microsoft Chairman and CEO “Bill Gates encouraged America’s teachers to use technology to serve the needs of the first generation of kids to grow up with the Internet” (Generation Y, 2013). According to the Moor’s law of economical factor for technological advancement will continue over the current decade as‐well (Scott, 2006). We can also see the technological advancement and adoption in most of the modern world businesses even in the context of reducing trust risk from the technical aspect, also discussed by Westergren and Holmstrom (Ulrika, 2012). Technology have huge impact on different companies from different dimensions such as in our today’s life we have no more distance with our business partners (Muller, 2012), families, friends and specially in professional business organisations (Hertwig, 2012) irrespective of cultural barriers, age, gender, language, citizenship and
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Salah Rana, Malcolm Crowe and Abel Usoro immigration status. However, lack of expert decisions and under utilisations of advanced technology in some organisations still exist in knowledge sharing activities (Muller, 2012), one of the main cause Vuori and Okkonen (2012) have mentioned is motivation. Thus, we believe companies are more interested in human capital or ability without consideration of location (Evan, 2012), culture, age (Thong et al. 2012), sex (Thong et al. 2012) and other related assumption used to made in past. Nowadays people and companies are making huge revenues as compared to past few decades due to the advancement of technology i.e. off‐shoring of big financial banks IT infrastructure and development of huge products all over the world without having any cultural (Michailova and Mustaffa, 2012, p 388), gender, and citizenship issues mostly. However, another most common factor namely common grounds between individuals do improve better knowledge sharing behaviour as discussed by Hwang et.al (2012). In our opinion age, sex, culture and other related barriers are drastically reduced in practical environment of professional organisations such as Banking sectors call centres, IT infrastructure, and collaborative software and strategic developments. Therefore, after analysing the current available literature and the professional organisational attitude towards modern connected world, we have proposed the conceptual representation/framework for the professional organisation to demonstrate the knowledge sharing factors and their relationship between each other to develop trust. We believe there may be a few more entities which are also important in real professional environment in the organisations to enrich the knowledge sharing behaviour and trust. We have also discussed with few professionals in university of the west of Scotland that in professional organisations for knowledge sharing or any other related activities at work age, gender, ethnicity, immigrations status, different geographical locations of the same company, and marital status do not really matter due to the modern connected world and exchange of rich technological infrastructure. Thus, we have considered human capital as a unique ability irrespective of aforementioned factors discussed by some researchers (Michailova and Mustaffa, 2012; Evan, 2012).
3. Proposed framework for knowledge sharing behaviour in professional organisations to improve trust In Evans (2012) thesis we have also found two different thoughts based on his model for trust to develop better knowledge sharing between individuals in the organisations such as strong tie (Levin and Cross, 2004; Krackhardt, 1992) and week tie (Nahapiet and Ghoshal, 1998; Nonaka and Takeuchi, 1995; Brown, 2000; Makela and Brewster, 2009). We also believe ties do matter from human psychology purpose. However, we have divided employee tie’s in two main categories first hard and soft as shown in the Figure‐1. Soft tie is related to employee company relation, emotional link to the company and employee’s own links/network in the company. Soft ties can be improved based on more interaction. In our case we have refer it rate of change for knowledge sharing to develop trust. Hard tie is more related to technology and its own job compulsory responsibilities in the company. In hard tie it is more towards how employee feels/react/accept like using available company products and their responsibilities for their job or for knowledge sharing. We also noticed in different literature that country culture has been mostly mentioned as another critical factor in companies overall progress or for knowledge sharing behaviour in term of trust (as discussed above in literature section) (Evan, 2012). However, we believe mainly company own developed environment or culture based on their shared vision have stronger influence on its different locations in the world irrespective of country culture it’s resides. Another important factor we have identified during our research is habit (Thong et al., 2012). We believe that employee’s habit is also one of the factor, which always have influence on both soft and hard tie’s (as shown in the Figure‐1) in the organisations. Habits can be judge by employee hobbies, skills and area of interest. We believe that hobbies and area of interest are interrelated and also come under soft tie category. For example, sometimes human hobbies become a cause of their area of interest, which sometime help them to get a relevant job in their area of interest; and also give them a better opportunity to progress faster in the company such as better/quicker grip to their job responsibilities and building trust. We also consider that skill come under the hard tie, where employees developed their own skills during the course of day to day responsibilities in their workplace. Individual’s skills gradually (with the passage of time) develop their new habits of work which is different from soft tie. In other words habits (mainly hard tie) developments are based on experience (time). Better development of habit in workplace based on experience
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Salah Rana, Malcolm Crowe and Abel Usoro is dependent variable of time; which sometime gives a priority to that employer to gain more trust than other less experience employees in companies. Therefore, we believe tie is a more random variable which is harder to impose because it is a dependent variable of individual learning abilities, time and habit (hard/soft) development process. Tie (soft/hard) variables are harder to control and predict in the professional organisation also because of its random nature of development which could be negative or positive for the company outcomes. Development of personal mastery (Senge, 2006), development of system thinking (Senge, 2006), and internal dialogue (Senge, 2006) in the professional organisation are very important factors to build better trust between individuals and organisations (as shown in Figure‐1). We believe it can help company to develop gradual and better trust level to improve knowledge sharing process in the workplaces. We have considered internal dialogue process in a bit different way irrespective of the different locations of the same company due to the technological advancement such as video conferencing where there is a requirement of observing and judging individuals body language and expressions; however resource exchange (discussed in next paragraph) plays very important role in normal dialogue process in professional organisations to build better trust level in term of reducing communication and organisational cultural gap. Another important factor/activity we have introduced in our model (Figure‐1), which can bring dramatic results in development of trust and for effective knowledge sharing behaviour is resource exchange (man power (management team/individuals), exchange of good policy execution strategies/processes). We believe that explicit knowledge is also the part of resource exchange (i.e. exchange of better policy execution strategies, portals, announcements, company news, success stories and development issues)elaborate resource exchange ingrained by personal mystery and system thinking development process. We believe that exchanging man power is very slow process or more time consuming, however, it is very effective in building strong trust level. This is a unique technique to accepting and sharing individual implicit knowledge with individual physical existence. Man power exchange is also very important to create mutual social and professional organisational culture in remote locations of the same company. We also believe that it will also help to build/promote the mutually and naturally acceptable organisational mental model between individuals of the same company in the different locations. On the other hand, exchanging good policies execution strategies are quicker process depending upon the use of technology and the advancement of technology (i.e. better security). Exchanging good policies through technological media may cause misunderstanding to the receiving end. Here, we are not conflicting our argument that technological advancement has dramatically reduced the physical face‐to‐face requirement; however, it is because misunderstanding may occur even with existence of physical face‐to‐face communication irrespective of technology or we can say that it is more dependant entity on receiving end (i.e. observations). However, the risk of misunderstanding is identifiable and can be rectified by reviewing the updated results based on followed strategy and re‐ communicate/discussed it with the same individual/location. The least we believe feedback procedure is also very important to evaluate the overall performance of the company and their individual’s (i.e. progress reports, personal consultancy, and other related activities). However, we believe apart from specific work responsibilities company should provide other related curriculum activities to be a part of progress report (i.e. maybe even 5‐10 per‐cent of rating) such as considering his deliverables (soft/hard tie’s) in‐case of resource exchange (i.e. man power), what change individual employee is making (positive/negative, i.e. may be any new habit influence by others) after resource exchange activity. In this paper we have established a point of view of professional organisations for those attributes which are more critical in term of knowledge sharing behaviour and development of trust. Current literature review has been conducted to establish the background of traditional knowledge sharing behaviour in organisations and the consideration of trust in their normal routine related work. Critical review has also been conducted to represent our own opinion about current literature. We have introduced different factors, which we believe are more realistic and have ability to absorb itself into the today’s professional working environment. We have also argued that professional organisation more interested in human capital/abilities instead of gender, marital status, citizenship, immigration due to technological advancement.
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Figure‐1: Flow diagram for developing trust in professional organisations Conclusion and area of further investigation The next effort will be to empirically test the model in an organisation.
References Brown, J. S. (2000) Balancing act: Capturing knowledge without killing it. Harvard Business Review. Cavusgil S. T. Calantone, R. J. and Y. Zhao. (2002) Learning orientation, firm innovation capability, and firm performance. Industrial Marketing Management. Chang et al., Kenneth l. J. and Tesfation, L. (2005) Agent‐Based Models of Organizations Handbook of Computational Economics II: Agent‐Based Computational Economics. Durst, S. and Edvardsson, R. I. (2012) Knowledge management in SME’s: A literature review. Journal of Knowledge Management. Stephens et.al. (2008) Knowledge management strategy for web 2.0 integration.” knowledge management strategies”. Evans, M.M. (2012) Knowledge Sharing: An empirical study of the role of trust and other social‐cognitive factors in an organizational setting. PhD thesis, Faculty of Information. Muller J., Hutter K., Matzler K., Fuller, J. and Hautz, J. (2012) Virtual worlds as collaborative innovation and knowledge platform. In System Science (HICSS), 2012 45th Hawaii International Conference on, pages 1003–1012. Harden, G. (2012) Knowledge sharing in the workplace: A social networking site assessment. 45th Hawaii International Conference on System Sciences. Hertwig, M. (2012) Institutional effects in the adoption of e‐business‐technology: Evidence from the German automotive supplier industry. Information and Organization, 22(4):252 – 272. Param, V. Hwang, Elina H. S. and L. Argote. (2012) Learning to cross boundaries in online knowledge communities: Fading of surface‐level and rise of deep‐level similarity with experience. Tepper School of Business. Kennedy, M.T. (2007) Organisational learning, knowledge management and complexity fusion ‐ exploring the “flavour of the month”. In Proceedings of OLKC ‐ "Learning Fusion". Krackhardt, D. (1992) The strength of strong ties: The importance of philos in organizations. Networks and organizations: Structure, form and action, 1992. Levin, D. Z. and Cross, R. (2004) The strength of weak ties you can trust: The mediating role of trust in effective knowledge transfer. Management Science.
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Salah Rana, Malcolm Crowe and Abel Usoro Makela, K. and Brewster, C. (2009) Interunit interaction contexts, interpersonal social capital and the differing levels of knowledge sharing. Human Resource Management. Davis J. H. Mayer, R. C. and F. D. (1995) Schoorman. An integrative model of organizational trust. The Academy of Management Review. Michailova, S. and Mustaffa, Z. (2012) Subsidiary knowledge flows in multinational corporations: Research accomplishments, gaps, and opportunities. Journal of World Business. Nahapiet, J. and Ghoshal, S. (1998) Social capital, intellectual capital, and the organizational advantage. Academy of Management Review. Scarbrough, H. Newell, S. and Swan, J. (2001) Global knowledge management to internal electronic fences: contradictory outcomes of intranet development. British Journal of Management. Nissen, M. and Levitt, R. (2002) Dynamic models of knowledge flows dynamics. CIFE working paper. Nonaka, I. and Takeuchi, H. (1995) The knowledge‐creating company: How Japanese companies create the dynamics of innovation. Odell, C. and Hubert, C. (2011) The new edge in knowledge: What is organizational knowledge. Senge, M. P. (2006) The Fifth Discipline. "The Art Practice of the Learning Organization". Bantam Doubleday Dell Publishing Group Inc. Scott, E. Thompson and Parthasarathy, S. (2006) Moore’s law: the future of Si microelectronics. Materials Today, 9(6):20 – 25. Tsoukas, H. (2005) What is organizational knowledge. Complex knowledge: Studies in organizational epistemology. Thong, J. Y. L. Venkatesh, V. and Xu, X. (2012) Consumer acceptance and use of information technology: Extending the unified theory of acceptance and use of technology. MIS Quarterly. Venkitachalam, K. and Busch, P. (2012) Tacit knowledge: review and possible re‐search directions. JOURNAL OF KNOWLEDGE MANAGEMENT. Villardi, B. and Leitao, S. (1999) The learning organisation concept to develop organisational learning and change. Vuori, V. and Okkonen, J. (2012) Knowledge sharing motivational factors of using an intraorganizational social media platform. Journal of knowledge management. Ulrika, H. W. and Holmstrm, J. (2012) Exploring preconditions for open innovation: Value networks in industrial firms. Information and Organization, 22(4):209 – 226. Wikipedia. Generation, y. (2012) [Online] Available from: http://en.wikipedia.org/wiki/Generation_Y. [Accessed: 1st May 2013] Yang, S. and Farn, C. (2009) Social capital, behavioural control, and tacit knowledge sharing: A multi‐informant design. International Journal of Information Management.
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Knowledge Sharing as a Problem of the Individual Nature of Knowledge Vaclav Reznicek, Zdenek Smutny, Jaroslav Kalina and Alexander Galba Department of Systems Analysis, Faculty of Informatics and Statistics, University of Economics, Prague, Czech Republic vaclav.reznicek@vse.cz zdenek.smutny@vse.cz jaroslav.kalina@vse.cz alexander.galba@vse.cz Abstract: Informatization of society allows (technically) new possibilities for knowledge sharing between individuals. With the support of new information and communication technology, it leads to "objectification" of knowledge; i.e. its objectification or separation from the individual, which allows to share and to manage the "knowledge". The paper combines narrative with arguments and analysis. The aim of the paper is to critically discuss and emphasise the individual nature of human knowledge and to highlight its role (meaning) in the process of interpretation of information or the real phenomena. The explanation is based on the knowledge of information science, system science and modern approaches in information and knowledge management. It presents the system view with an accent on the mutual understanding of the relations between the studied phenomena and processes. The fact that the individual nature of knowledge is not appreciated brings negative consequences. The current trend towards the instrumentalization of education and knowledge doesn't contribute to it, on the contrary, informatization often positively supports it. Unprecedented data availability allowed through the internet enhances a false idea of the information literacy and the inutility of using the critical thinking. Many individuals can take up with the passive acceptance of mass media interpretation. This is related to the resignation on the development of knowledge, because everyone can find everything on the internet. People forget that a certain quota of knowledge is necessary to create a frame of contemplation and thinking. Instrumentalization of education leads to a quasi‐utilitarian "practical orientation", where the "unnecessary" humanities and general education are relegated to the background. In a society that calls itself a "knowledge society", the knowledge itself disappears because it is degraded to fragments/pieces of knowledge that are usable in practice – and the knowledge as a whole loses its value. This paper analyses the negative implications of the information age and discusses it. Thanks to the system view on the area, the paper presents critical reflections of the current situation. The authors of this paper consider a very actual and crucial question about problems associated with the sharing of knowledge. They critically present the phenomenon of knowledge sharing from an untraditional point of view. Keywords: knowledge, knowledge sharing, information, informatization
1. Introduction In the context of dynamic development of the information technologies based on ICT and in relation to the application of the knowledge management in the business practice, the question of the sharing of knowledge comes to the fore. To use knowledge of workers/professionals within a company or any other formed and purpose‐driven organization effectively, it is necessary to consider not only development and potential of technologies and their application, but also the nature of human knowledge that should go first of all. The aim of this paper is to critically discuss the problems arising from disregard of the individual nature of human knowledge (as a dynamic structure), its role in the process of information (information is the importance and meaning of data) or real phenomena interpretation, which is in literature discussed especially in terms of philosophical approach (Fumerton, 2002), (Williams, 1996) and the unappreciated importance of its (real) development (learning process). The interpretation is based on knowledge of information science, system science, computer science, modern approaches in information and knowledge management (Nonaka, Nishiguchi, 2001), (Rosicky, 2010) and related transdisciplinary fundamentals, while it tries to present the system view with an emphasis on the understanding the mutual relation between the studied phenomena. After defining the theoretical framework in the first part (i.e. definition of thinking framework in which the problems are observed by the authors), the concept of knowledge sharing is presented in the second part. Practically, there is used an example that shows the problems of modelling and model as a purpose‐built abstraction of reality that allow working with explicit knowledge (part three). Eventually, the problems that go beyond the framework of the knowledge management, but directly or indirectly arise from its (inappropriate)
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Vaclav Reznicek et al. application are critically discussed. It is shown that the fundamental issues are the education and the changes that informatization (ICT applications) brings to the educational process.
2. Knowledge and its role in the process of interpretation the meaning We understand data as the representation of reality which is devoid of any meaning. Based on personal pieces of knowledge, a person is able to interpret the meaning of data and he is able to actually (somehow) understand the reality. If he shares his own understanding with other subjects, he has to externalize it back into data (symbols, etc.) that are available for the subsequent (next) interpretation. The importance of establishing role of the human knowledge is crucial in the question of understanding the reality (or assessment of the phenomena in general). The extent of similarity of individual interpretation does not depend on objectivity or accuracy of knowledge of the individuals. It rather depends on the similarity or difference of their individual knowledge, which can be regarded as a knowledge framework of each individual and its application (simply knowledge). Based on human interaction with the outside world and, therefore, the continuing process of information interpretation, knowledge is also, as it is sometimes not fully understood and emphasized, constantly (re)formed (Nonaka, Nishiguchi, 2001). The suggested concept is illustrated in the figure below.
Figure 1: Interpretation of meaning the information, circular process (relationship) – data, information, knowledge (Source: Authors)
3. Knowledge sharing and „objectification“of knowledge If knowledge is shared, it must be externalised from its originally implicit (tacit) form, which leads to its "objectification" into the explicit form of data, as mentioned above. This process of crossing from implicit knowledge into an explicit form and conversely is illustrated by the knowledge spiral (SECI model, see Figure 2).
Figure 2: The knowledge spiral, SECI model. Source: Authors according to (Nonaka, Nishiguchi, 2001)
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Vaclav Reznicek et al. A model is the tool for allowing the reduction of complexity which enables understanding of the system. The relation between real system and (system as) model is illustrated in figure 3. The model is a purpose‐built abstraction of reality (real system) arising from the modelling process. The model (here an explicit model of the system) is an explication of understanding the system (the key elements and linkages between them) by the individual. The system is approached as a model (not as a system per se), we see it as a selective (purposefully) simplification based on individual skills (knowledge) and preferences. Especially in the case of complex systems, this approach gives us a possibility to abstract from elusive detail (from the principle and definition of system as a complex system) and conversely, to grasp it thanks to understanding general principles of their functions.
Figure 3: The relation between the system and the model ‐ the model as a tool for understanding the system. Source: Authors. Next image (see figure 5) emphasizes importance of individual knowledge of the individual in the process of creating models (modelling), or understanding the system (Rosicky, 2010), and their applications (in the real‐ world). Model, as it was mentioned, has been traditionally explained as a presented conception of reality (real object); it is explicit. Importance of individual knowledge should be stressed, because it forms mental model into the individual conception, or more precisely, the internal representation of reality (system). The mental model should not be confused with this knowledge. The mental model is always related to the circumstances – the individual chooses the essential elements and put them into relation in which individual knowledge (and the individual contemplation) can be applied. There are two fundamental conclusions. We work with the system in the same way as with the model ‐ in case of complex systems we apply the important abstraction ability. Secondly, this model (i.e. understanding respectively knowledge about system) is strongly dependent on individual knowledge of a particular individual. Knowledge sharing is a process which starts with externalization of knowledge into an explicit form and ends with interpretation of data obtained by externalization of originally implicit human knowledge; individual knowledge of all stakeholders is essential. In order to be able of abstraction thinking (and thus capable of making adequate models), the individual must have some background knowledge that enable to consider the phenomena (things) in the context and to assess what is important and what is not significant.
Figure 4: Modelling and role of individual knowledge. Source: Authors (inspired by Rosicky, 2010).
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Vaclav Reznicek et al. This will be discussed in the next part, dealing with the problems of modelling and a model as an abstraction of reality which allows us to work with explicit knowledge; practical examples will be shown here.
4. Model as a knowledge sharing instrument Our notion of the knowledge sharing process is affected by the three dimensional point of view on the main factors that do play critical role in establishing viable knowledge sharing behaviour across organizations:
Knowledge Basis ‐ One needs to have a fitting level of knowledge to interpret additional information as a new knowledge (Huber, 1991).
Barriers ‐ consisting of time, cost, distance, social factors (Ruggles, 1997) and capabilities of available knowledge sharing means (Bertschi, 2009).
Motivation ‐ factors based on a mix of explicitly encouraged or enforced behaviour by the management and inner personal level factors of motivation based grounded in the concept of hierarchical personal needs of a Maslow's Pyramid type alongside with the mutual recognition of the value of knowledge to be shared by providing and receiving party.(Hendriks, 1999).
Figure 5: Factors of effective knowledge sharing in organizations. Source: Authors. Contemporary information and communication technology allows for lowered barriers of knowledge sharing in terms of distribution channels. Models from the constructivist point of view do provide a useful mean of knowledge representation, or more precisely support for knowledge reconstruction by the receiving party (Mendling, 2008). These notions are compatible with the concepts of infrastructure (means facilitating the contact of knowledge network members) and infostructure (formal rules, cognitive resources aka common language) as described by (Pan, Scarbrough, 1999). For example, in the area of ICT applications, the problems of infrastructure and infostructure in software development across the different departments involved in the development become most important. “Thousands of models are daily transformed to computers programs and tested. The transformation must overcome the gap between abstract human formal notation and low level semantics of contemporary programming languages.” (Fiser, 2012) Or, to bridge the gap between manager ideas and the possibilities that programming languages offer to the programmer in the implementation. The difference is shown here between the knowledge frameworks of both actors with the same goal. The problem is partly solved by using the standard languages for software systems designing. New way to bridge this gap is a unification of knowledge frameworks through the domain‐specific languages. The layers of infrastructure and infostructure both fall under the Barriers factor of effective knowledge sharing. The third layer identified by (Pan, Scarbrough, 1999) as infoculture relates to motivation factor (see figure 5), encompassing both the formal and informal incentives for knowledge sharing behaviour. Achieving lowered barriers for knowledge sharing two parallel approaches are viable: Forming cost and time efficient channels and organizational routines through which the knowledge sharing would occur. Using shared communication platform in a way of a common language for transmitting knowledge across participants of the knowledge sharing process.
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Vaclav Reznicek et al. Dyer and Nobeoka (2002) describe the way a formalized set of knowledge sharing routines were shaped into a network‐based system in the Toyota company, establishing defined channels for knowledge transfer to happen, e.g. the infrastructure. This relates to the first approach of lowering the transaction related aspects of the set of existing barriers for knowledge sharing. The second approach relates to the utilization of common communication instrument that would act well as a carrier of knowledge. In our paper we focus on the formal ontology models as a specific sub category of conceptual models. By its nature the predominant type of a knowledge sharing model is the conceptual one, constructed for the purpose depicting an abstract phenomenon (as opposed to the physical model). Guizzardi (2005) elaborates on the notion that in principle all kinds of conceptual models are either ontologies on its own or at least defined by a ontological model (formally specified or not). This is related to earlier notions which equate the term ontology as a specification of a conceptualization (Gruber, 1993). IDEF 5 (Benjamin et al., 1994) species following knowledge sharing related goals for ontology model creators: creation of shared vocabulary, taxonomic hierarchical dictionaries and concept definitions above the level of rigor possible with non‐ structured text. Modelling, being a non‐deterministic process dependent on the particular modeller, several guidelines need to be followed to achieve a viable level of inter‐subjectivity between a community of practicing modellers (Schuette, 1998): Correctness, Relevance, Economic Efficiency, Clarity, Comparability and Systematic Design.
5. Instrumentalization of education The Austrian philosopher Liessmann points out degenerative processes of instrumentalization of education (knowledge) in the context with the creation of knowledge. Because of the expected economic profit from education (due to its commodification), the education in the sense of knowledge is overshadowed, and also the so‐called inadequate practical orientation. “General education" and desire for the understanding of context requiring abstract systems thinking stay back. Which is not entirely new issue (Brouk, 1946), but today has new consequences. Even under the influence of informatization which enables unprecedented data availability, so many people resign to develop their thinking, because they have an idea that "all that is necessary can be found on the internet." They are satisfied with the passive and comfortable receiving of media interpretation and non‐deliberates the validity and credibility of information sources. Liessmann (2006) writes that "today ignorance is not an intellectual deficit, lack of information or defect of cognitive competence – although it will still exist – but it is resignation on trying to understand anything." Instead of the understanding, we learn the best practices that often fail in practice because they don't reflect new (modified) conditions. We are not able to create new solutions due to the aversion for thinking. "Management of knowledge" appears, i.e. something like the effective management of knowledge, which "does not offer much more than the cowardice in the language of an ambitious business consulting. The way of knowledge presentation today can also be understood as a growing disregard for knowledge. Often you can see a viewable nuisance not only in the business presentations, but also increasingly on scientific symposia and at universities; the speaker project simple sentences and pompous concepts through PowerPoint and then he simply reads them. On such occasions, there is a significant disparity between the technical and media equipment and a spiritual content. Not only the domination of technology overlaps words, it already does not allow real thoughts.” (Liessmann, 2006)
6. Conclusion The main aim of this paper was to critically discuss the problems arising from disregard of the individual nature of human knowledge (in context of informatization and in connection with the need of individual for orientation in complex environment). This has been done with regard to the form of this paper. The conclusion is that the abstraction (and therefore a certain “objectification”) is an important power of human mind and in this way the individual puts up with complexity of the world in which he lives. It is important to realize that we do not understand this world (reality) as it actually is (i.e. as a system per se), but we see it through the model (mental model of reality) which we created. This model depends on personal know
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Vaclav Reznicek et al. ledge of each individual. Of course, it does not mean that knowledge and interpretation (understanding) are completely different for each individual. Biological, historical and socio‐cultural determination enables conformity of understanding among individuals. However, to be able to interpret the information (and therefore the systems) in context of decisions of an individual, the level of knowledge is fundamental (i.e. how it corresponds with reality); this fact is not emphasized sufficiently. Thanks to the development of new technologies allowing us the unprecedented data availability, we have new ways for sharing that data. Data are externalized and "objectivised” knowledge and their meaning is dependent on the specific interpretation. So, knowledge sharing was defined as a process which starts with the externalization of knowledge into an explicit form and finishes with the interpretation of data obtained by externalizing originally implicit human knowledge that are essential of individual knowledge of all stakeholders. Attention should be paid not to externalized – shared – knowledge (data and their form), as commonly practiced in knowledge management, but to the development of (individual) knowledge and thinking of the people who create "shared knowledge" and those who interpret them. Presented reflection shows, that unfortunately, we observe a tendency towards instrumentalization of knowledge causing acquisition of fragmented (un‐) knowledge (ignorance). This leads to a kind of closed circle. If the understanding is not (and as it has been shown, it is often not) the aim, but it is just an economically utilitarian memorizing of mutually unrelated pieces of knowledge and an emphasizing the form of data presentation, the education will not provide sufficient conditions for understanding the increasingly complex world due to the massive application of ICT. Many people will not be able to distinguish the important from the unimportant. Instead of the orientation in dynamic environment thanks to the abstract thinking, the result of our own ignorance, which does not provide sufficient (knowledge) support for reasoning about context and evaluation in general, is chaos and uncertainty. Under these circumstances, the knowledge management will not help us. Perhaps, the presented text will encourage to thinking. That's what we tried.
References Bertschi, S. (2009) "Knowledge visualization and business analysis: meaning as media." Information Visualisation, 13th International Conference. Brouk, B. (1946) Zavaznost obecneho vzdelani. Svazky uvah a studii. No. 96. Prague: Vaclav Petr. 42 s. Dyer, J. and Nobeoka, K. (2002) “Creating and Managing a High Performance Knowledge‐Sharing Network: The Toyota Case”, [online], DSpace MIT, 2002‐07‐10, http://hdl.handle.net/1721.1/1441 Fiser, J. (2012) “Will we think in programming languages?” Acta Informatica Pragensia, Vol. 1, No. 1, pp 1–21. ISSN 1805‐ 4951. Fumerton, R. (2002) Understanding human knowledge. The Review of Metaphysics, 56(2), pp 461‐463. Gruber, T. R. (1993) “A translation approach to portable ontologies.” Knowledge Acquisition, Vol. 5, No. 2, pp. 199‐220. Guizzardi, G. (2005) Ontological Foundations for Structural Conceptual Models, Centre for Telematics and Information Technology, University of Twente. ISSN 1381‐3617. Benjamin, P. C., Menzel, C. P., Mayer, R. J., Fillion, F., Futrell, M. T., deWitte, P. S., Lingineni, M. (1994) IDEF5 Report: Information Integration for Concurrent Engineering, KBS Inc. Hendriks, P. (1999) “Why share knowledge? The influence of ICT on the motivation for knowledge sharing.” Knowledge and Process Management, Vol. 6, No. 2 pp 91–100. Huber, G. P. (1991) “Organizational learning: the contributing processes and the literatures”, Organization Science, Vol. 2 No. 1, pp. 88–115. Liessmann, K. P. (2006) Theorie der Unbildung: Die Irrtümer der Wissensgesellschaft. Paul Zsolnay Verlag. Nonaka, I. & Nishiguchi, T. (2001) Knowledge Emergence: Social, Technical, and Evolutionary Dimensions of Knowledge Creation. New York: Oxford University Press. Mendling, J. (2008) Metrics for process models: emprirical foundations of verification, error prediction, and guidelines for corectness. Berlin: Berlin Heidelberg Springer‐Verlag. Pan, S. L., & Scarbrough, H. (1999). “Knowledge management in practice: An exploratory case study”, Technology Analysis & Strategic Management, Vol. 11, No. 3, pp. 359‐374. Rosicky, A. (2010) Complex nature of man and its knowing. IDIMT‐2010, Information Technology – Human Values, Innovation and Economy,18th Interdisciplinary Information Management Talks. Trauner Verlag. ISBN 978‐3‐85499‐ 760‐3. Ruggles, R. L. (1997) Knowledge management tools, Butterworth‐Heinneman. ISBN 0‐7506‐9849‐7. Williams, M. (1996) Understanding Human Knowledge Philosophically. Philosophy and Phenomenological Research , Vol. 56, No. 2, pp 359‐378.
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DataTalks: A Unified Knowledge Pool in SaaS and Mashup Systems Sasha Mile Rudan1, Dino Karabeg1 and Alf Martin Johansen2 1 Department of Informatics at the University of Oslo, Norway 2 Induct AS, Norway sasharu@ifi.uio.no dino@ifi.uio.no amj@inductsoftware.com
Abstract: Research presented in this paper addresses the challenges of knowledge management in the environment consisting of a set of distributed services that do not all belong to the same vendor and where users collaboratively perform social interaction / business processes over the set of services that, together, constitute SaaS/Mashup. Clearly, users should not be aware of the heterogeneous structure of services that mashup seeks to unify. Therefore, from the userâ&#x20AC;&#x2122;s perspective, those mashups ideally ought to build frictionless systems. However, the knowledge footprint produced through the performance of social interaction processes usually ends up segmented all over the knowledge space of different service vendors. Even in the case of services offered by the same vendor, knowledge unity is far from reality today. Since users want to use knowledge over different usage patterns (offered through different services) they face the problem of knowledge redundancy. The same situation persists with the process of sharing knowledge. Handling even more complex knowledge processes is virtually impossible to manage given the distributed nature of knowledge. If we want to aggregate knowledge, we should be able to crawl over different services, interpret their specific knowledge representation and finally create semi-temporary aggregated presentations. Similar issues apply to knowledge navigation and knowledge landscaping, where at least the semi-temporary approach seems to provide a more feasible solution. Here we present a solution model based on our initial research on monolithic systems that later (through an increasing need for opening their platforms toward external developers/vendors) became PaaS/SaaS/mashup systems. Our DataTalks model is based on a unified knowledge pool, conceptually laying under the mashup of services in use. In our case, the top layer of the SaaS/mashup business logic is aware of the DataTalk environment which is much easier scenario of DataTalks use, but we also claim that similar usability results are achieved on SaaS/mashups unaware of the underlying DataTalks stack. Keywords: Unified knowledge pool, SaaS, SOA, Mashups, social interaction processes, Groupware, Dynamic knowledge repository, Business processes, BPEL, DataTalks
1. Introduction Research resulting with this paper is a part of a wider research initiative. Research initiative focus on shifting general information system architecture and system development paradigm toward socio-technical systems. Information systems are socio-technical systems in their nature of interaction and it is necessary to close the socio-technical gap (Whitworth, 2006). Focus of the research presented in this paper is investigating knowledge management of socio-technical systems as initial step toward Dynamic knowledge repository (Bieber, M., Engelbart, D., Furuta, R., Hiltz, S. R., Noll, J., Preece, J., et al., 2001). In more general perspective our research initiative resonates with Engelbartâ&#x20AC;&#x2122;s research on groupwares, with a primary strategic focus for allowing humans to address complex problem (Engelbart, D. C., 1992). In this paper we analyze problem of knowledge management and sharing in groupware environment with distributed team and SOA/SaaS/mashup system architecture (Erl, T., 2008). Our research relates to an enterprise solution, Induct Software that is used mostly as a collaboration service for bringing and managing innovation in big and spatially distributed institutions. Majority of research is done in the domain of knowledge management from the architectural and semantic perspective. There is a noticeable research in managing organizational knowledge (organizational memory), as well (Conklin, J., 1996). This was the initial input for our research. It helped us to organize the knowledge component of system. However, the acquired knowledge in the systems of our interest is not simply aggregated knowledge, but proactive knowledge used as a stimulus for other participants to collaborate and produce/expand knowledge, in this particular case, innovation knowledge. Therefore we were highly interested in social domain of knowledge activities; we wanted to find out what social interaction processes and social network analysis could tell us and help us in analyzing and supporting in
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Sasha Mile Rudan, Dino Karabeg and Alf Martin Johansen collaboration stimulation and proactive knowledge building. Opposite to the standard passive system observation with social-related tools (SNA, social-processes, etc) (Behrend, F. D., & Erwee, R., 2009), we are mostly interested in designing feedback loop and using social-related tools as part of rule based system component that will provide self-sustainability of socio-technical system. The second biggest challenge was wrapping heterogeneous services into uniform knowledge pool. The particular challenge was handling 3rd party components not aware of our architecture. Paper starts introducing Trans-Technical Layered Architecture (TTS-LA), an architecture that presents background of our research. Immediately after we presents DataTalks: Knowledge pool Architecture, that is the main actor of the research addressed with this paper. Knowledge initialization process explains a way how to introduce our solution in standard use. The next are two parts regarding knowledge creation processes followed with Knowledge motivation process as an important part of the system of interest; knowledge innovation system. Finally, we ends with conclusion and future work.
2. Trans-Technical Layered Architecture Our research is placed in the context of the Trans-Technical System Layered Architecture (TTS-LA). We introduce Trans-Technical Systems (TTS) as the next evolutionary step behind the Socio-Technical Systems (STS), modeled through socio-technical theory (Trist, 1981) that is trying to map the complexity of regular technical system and the necessary reference to the social component of any technical system. The model/theory of Trans-technical systems brings few important extensions to the socio-technical model.
Figure 1: Trans-Technical system layered architecture First of all, it clarifies terminology and processes that constitute the use of the system. Therefore we introduce Trans-Technical Processes (TTP) as processes that describe user usage of system and from the perspective of user they represent the unity of work that could bring benefits back to the user and could be recognized as rounded/finished system interaction. TTPs are further split and described with Trans-Technical Activities (TTA) that represents the minimal possible interaction of user and the TTS; interaction quants. TTPs include diverse classes of TTAs; among others economic, entrepreneurial, business, management, environmental, humanistic, and geo activities. Extension of activities is the second important extension of socio-technical systems. Presented TTP and TTA concepts strongly relates to social interaction processes (Taveter, 2001). TTSs are systems where TTPs and TTAs are the first-level-citizens. After the introduction of TTSs we present the Trans-Technical System Layered Architecture (TTS-LA).
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Sasha Mile Rudan, Dino Karabeg and Alf Martin Johansen For the sake of scientific and research expression we will model trans-technical systems as SOA systems (Service-Oriented Architecture Systems), and on the following picture we present the layered architecture of trans-technical systems. Types of trans-technical system we are focusing on in this research are mostly mashups and highlyheterogeneous and loosely orchestrated (distributed, collaborative, and multi-vendor) trans-technical systems. There are at least two reasons why those systems are our main focus. Firstly, they are the present and most likely the future of complex online systems. Secondly, we believe that those systems, being part of CSCW (Computer-supported cooperative work)/Groupware for many decades (starting with the legendary Douglas Engelbart’s vision and work presented through the “The Mother of All Demos”), are key changers of the complex society issues and one of the strongest candidates in helping solving global wicked problems. Something that we recognize as the most promising initiative that aligns with our architecture is a stack of Business Processes (BP) Languages (BPEL, BPMN, BPEL4People) and some bridging support (namely, BPEL and Java) supported with underlying orchestration and architecture modeling stack, like SoaML. If we continue with making parallels between BP (Business Process) and TTP (Trans-Technical Process), then our first focus is on mapping business processes into the domain of trans-technical systems, and recognizing BPMN-like modeling language as the-programming-in-large modeling language (DeRemer, F., & Kron, H. H., 1976) of trans-technical systems. To provide a unified and homogeneous picture of underlying highly heterogeneous (manyvendors/technologies/protocol/AAA...) systems we introduce the Service Unification Layer that provides the unified-system support for higher layers, but also provides the end-user with unified knowledge model and low-frictions usage of trans-technical systems. Finally to make everything feasible at the bottom of the TT Layered Architecture we have the Service Orchestration Layer with the SoaML modeling language to link all components of the TT system and describe their functionality, but also their dependencies and message flows between them.
Figure 2: Trans-technical System Stack
3. DataTalks: Knowledge pool Architecture DataTalks component is a fundamental component of the TTS Layered Architecture, especially the unification part of the TTS-LA. Two other unification components of the TTS-LA; Service Orchestration Layer and Friction reductor layers are mostly transparent for the system users, and are mainly at the place to make impression of
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Sasha Mile Rudan, Dino Karabeg and Alf Martin Johansen the unified system. However, DataTalks brings the real value to the user and the system usage. It is the main glue above the heterogeneous system that makes data flows between different system services transparent. In the following few paragraphs we will present just a few user benefits from the DataTalks model. Standard usage of a TTS gives a user impression of a homogeneous system. Visually user is presented with multiple interfaces and system services that apart of potentially different visual identities are not distinguished one from the each other. During the particular service usage, each piece of data marshaling between user and service is monitored and absorbed/mapped to the Knowledge Pool either through DASKI (DataTalks Aware Services Knowledge Interface) or DUSKI (DataTalks Unaware Services Knowledge Interface) depending on the level of integration.
Figure 3: Knowledge Pool Heterogeneous services integration We need to remark that in the most of the cases particular data/knowledge created through the services use is not migrated/copied to the knowledge pool. It is case only when the data is not possible to retrieve after its creation. In the most cases, the only necessary thing is to create a meta-wrapper that will be a tuple reference to the concrete service containing data, data context and the very data.
Figure 4: Knowledge Mapping After the knowledge is mapped user is not necessarily aware of the knowledge origin; (s)he is free of using it and building on the top of it as it is integrative part of TTS. Knowledge stored in knowledge pool can be additional organized and landscaped to help and direct knowledge discovery (Rudan, S., & Rudan, S., 2008).
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4. Knowledge pool usage processes 4.1 Knowledge initialization process Albeit the fact that the knowledge is mostly transparent to the end user, in the most of the cases, mapping the services and corresponding data types is semi-automatic process. Thanks to the SOA system architecture most of the service communication and data types are clearly accessible to the knowledge types discovery component. Through the SOAP, WSDL protocols, or a little bit less structured RESTful set of protocols, JSON, JSON schema... we are fairly easy able to discover services and corresponding data streams and data types.
Figure 5: Knowledge Pool Initialization
4.2 Ontology Creation/Change process To understand the politic of ontology processes in TTSs we have to bear in mind the context of our system use and the context of our research. The main interest of this research is in the systems where knowledge ontology is created in the parallel with the system adoption. At the same time, we recognize the activity of knowledge ontology extension as an important trans-technical activity that should foster collaborative activities inside the system. Finally, TTSs address closed communities, where we do not necessary care about negotiating on ontologies or wide range of knowledge assimilation activities. More on the knowledge topics creation and organization we researched in the [Rudan 2008].
4.3 Knowledge Creation process As we already presented above, the act of creating knowledge in the repository of one of the TTS services, results in the creation of a semantic meta-wrapper in the Knowledge pool. In that way the created knowledge is available over the whole TTS and not only inside the service in question. This helps enriching relation between the multi-vendor services, and reduce the knowledge redundancy. It helps us in recycling the knowledge and building more complex knowledge.
4.4 Knowledge motivation process In order to observe the collaboration activities inside the TTS and effects of different activities on other collaboratorsâ&#x20AC;&#x2122; motivation we have to introduce a way of tracking activities and activities reactions. With this in place, we are able to use social-network analysis techniques and start observing nodes (TTS members/collaborators) and their activities. We are able to observe the effect of different activities and artefact-exchanges along the connections and other collaborator motivation, but also their effect on overall knowledge building process. Compared to business processes, there is one more difference in general nature of trans-technical processes, mostly on the semantic level, but crucial for understanding our focus on the social side of the process and motivation interest. While BPMN/BPEL tasks are mostly obligatory for their executors and the whole business process is more deterministic, in the case of TTPs system users have much more freedom and their decisions exude with fuzziness. At the same time, quality of work in our particular case of innovation processes depends much more on motivation of system users. Finally, at the perception level, â&#x20AC;&#x153;executionâ&#x20AC;? of BPMN/BPEL-like tasks will in general case affect the perception of TTS users and therefore make the system observation even more complex.
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5. Conclusion Research covered with this paper has focused mostly on integrative component of the knowledge creation over the set of heterogeneous services and on the social component of knowledge creation and collaborative motivation. We have shown it is possible to create unified trans-technical systems with introduction of unifying layers and particularly knowledge pool that was the main focus of our interest in this paper. We also opened questions regarding social interactions between collaborators and motivation in knowledge creation.
6. Future work Through this research we opened many new interesting questions we are looking forward researching on. Our strongest interest lies in the further addressing the proactive component of the TTS layered architecture. We would like to see how possible is to integrate TTAs monitoring component, with network analysis techniques and predefined rules to develop proactive system component that will support positive patterns in TTS collaboration and prevent negative effects or phenomena. This research will benefit a lot from our parallel research on complex systems and SNA related work. Other interest of our research is in introducing NLP components in TTS and observing positive effects in knowledge production based on semi-automatic knowledge extraction and collaborative recommendation/stimulation.
References Whitworth, B. (2006). Socio-technical systems. Encyclopedia of human computer interaction, 533-541. Bieber, M., Engelbart, D., Furuta, R., Hiltz, S. R., Noll, J., Preece, J., et al. (2001). Virtual community knowledge evolution. System Sciences, 2001. Proceedings of the 34th Annual Hawaii International Conference on. IEEE. Engelbart, D. C. (1992). Toward high-performance organizations: A strategic role for groupware. Proceedings of the GroupWare. Erl, T. (2008). Soa: principles of service design (Vol. 1). Upper Saddle River: Prentice Hall. Conklin, J. (1996). Designing organizational memory: preserving intellectual assets in a knowledge economy. Group Decision Support Systems, 1, 362. Behrend, F. D., & Erwee, R. (2009). Mapping knowledge flows in virtual teams with SNA. Journal of Knowledge Management, 13(4), 99-114. Trist, E. (1981). The evolution of socio-technical systems. Occasional paper, 2. Taveter, K., & Wagner, G. (2001). Agent-oriented enterprise modeling based on business rules. Conceptual Modeling—ER 2001, 527-540. DeRemer, F., & Kron, H. H. (1976). Programming-in-the-large versus programming-in-the-small. Software Engineering, IEEE Transactions on, (2), 80-86. Rudan, S., & Rudan, S. (2008). SocioTM–Relevancies, Collaboration, and Socio-knowledge in Topic Maps. TMRA, 2008, 285. Appelbaum, Steven H. "Socio-technical systems theory: an intervention strategy for organizational development." Management Decision 35.6 (1997): 452-463. Becker, M. C. (2001). Managing dispersed knowledge: organizational problems, managerial strategies, and their effectiveness. Journal of management studies, 38(7), 1037-1051. Faraj, S., Jarvenpaa, S. L., & Majchrzak, A. (2011). Knowledge collaboration in online communities. Organization science, 22(5), 1224-1239. Inkpen, A. C. (1996). Creating knowledge through collaboration. California Management Review, 39(1), 123-140. Reagans, R. E., & Zuckerman, E. W. (2008). Why knowledge does not equal power: the network redundancy trade-off. Industrial and Corporate Change, 17(5), 903-944. Ardichvili, A., Page, V., & Wentling, T. (2003). Motivation and barriers to participation in virtual knowledge-sharing communities of practice. Journal of knowledge management, 7(1), 64-77. Allen, J., James, A. D., & Gamlen, P. (2007). Formal versus informal knowledge networks in R&D: a case study using social network analysis. R&D Management, 37(3), 179-196. Lin, H. (2007). Effects of extrinsic and intrinsic motivation on employee knowledge sharing intentions. Journal of information science, 33(2), 135-149. Yang, S. J., & Chen, I. Y. (2008). A social network-based system for supporting interactive collaboration in knowledge sharing over peer-to-peer network. International Journal of Human-Computer Studies, 66(1), 36-50. Sveiby, K. E. (1997). The new organizational wealth: managing & measuring knowledge-based assets. Berrett-Koehler Pub. Argote, L., McEvily, B., & Reagans, R. (2003). Managing knowledge in organizations: An integrative framework and review of emerging themes. Management science, 49(4), 571-582. Hansen, M., Nohria, N., & Tierney, T. (2000). What’s your strategy for managing knowledge. The knowledge management yearbook, 2001, 55-69. Hildreth, P. M., & Kimble, C. (2004). Knowledge networks: Innovation through communities of practice. Igi Global.
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Data Mart With Lean Six Sigma Concept for Performance Level Assessment in Knowledge Management Framework Jevgeni Sahno, Eduard Shevtshenko and Tatjana Karaulova Tallinn University of Technology, Tallinn, Estonia Jevgeni.Sahno@gmail.com Eduard.Sevtsenko@ttu.ee Tatjana.Karaulova@ttu.ee Abstract: Nowadays, the rapid growth of competition in the market place, forces the companies to guarantee to their customers a reliable, sustainable and quality proofing production system. In this paper we consider KM framework that includes well known IS tools like PDM and ERP system, PDM‐ERP middleware and novel DM that is the core of our framework. The combination and application of different tools and methods in the general framework allows transparent data flow between different systems, analysis of production operations and failures occurring during the production process. Our DM will play the role of a company’s “dashboard” that is similar to the one that a pilot has in the cockpit that describe the performance of an airplane. A business or production system may have similar “dashboard” which shows the process performance. Here we consider the application in DM, the three first steps of DMAIC (Define, Measure, Analyse, Improve and Control) Six Sigma approach which characterise the nature of the problem to be solved. In Define step for the object or problem to be studied, we will use the combination of PR data, Faults Classification standard DOE‐NE‐STD‐1004‐ 92 and the data from FMEA table. For the Measure step we will use raw data from ERP system and specified algorithm in DM that allows assessing the Severity, Occurrence and Detection rating and then calculating RPN values in FMEA table. Further, the RPN values will be converted into the per cent values that present a sigma performance level for every operation, product type and common production system. In Analyse step it will be possible to perform analysis of operations and failures occurred, also this step prepares the base for the next two steps (Improve and Control) of Six Sigma approach where will be improvements of production process. The integration of the first three steps of Six Sigma in DM will ensure a company to monitor the changes of the production system in real time. Keywords: knowledge management (KM), product data management (PDM), enterprise resource planning (ERP), PDM‐ERP middleware, data mart (DM), production route (PR) card, faults classification, failure mode and effect analysis (FMEA), six sigma, sigma performance level
1. Introduction The internal issues that many manufacturing companies face today are often surround the idea that companies know the problems that lie in front of them: not reliable production processes, bad product quality, problems with product On‐Time‐Delivery (OTD) and ect., but they do not understand the roots of these problems. The Pareto principle states that roughly 80% of the problems are called by 20% of the causes (Koch et al. 2004). Hence, all problems in manufacturing start from small causes, e.g., low labour qualification and unbearable working conditions and old technologies applied which lead to the bad quality of product. As the result these causes may lead, to the loss of customers’ expectations and consequently position on the market. To survive in the competitive market companies should be expedient in technological resources and they should be able to demonstrate to a customer innovativeness, the quality of products and a functional quality proofing production system (Lõun et al. 2011), (Riives et all. 2012). A customer who is satisfied by the first buying experience needs to be satisfied again. Most customers care about quality willing to pay more than average market price to obtain product and see “the extras” as worth the additional expense (Matzler et al. 1998). Today Six Sigma is a well‐known methodology for quality and process improvement with an emphasis on defect prevention rather than defect detection. It is intended on reducing of variation and waste in the process. The bottom line results and competitive advantage are to be improved. Knowledge Management (KM) on the other hand is aimed at creating competitive advantage too, by creating new knowledge in an organisation (Arendt 2008). This paper is aimed at introducing the general idea of using DMAIC Six Sigma approach not only to improve the production processes but also to use it to create new knowledge within an organization too.
2. Literature review Organizational knowledge is now recognized as a key resource and a variety of perspectives suggest that the ability to marshal and deploy knowledge dispersed across the organization is an important source of organizational advantage (Teece 1998). KM is performance of the activities involved in discovering, capturing,
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Jevgeni Sahno, Eduard Shevtshenko and Tatjana Karaulova sharing, and applying knowledge in terms of resources, documents, and people skills to enhance, in a cost‐ effective fashion, the impact of knowledge on the company´s goal achievement (Becerra‐Fernandez et al. 2004). KM initiatives in organizations are becoming important and firms are making significant Information Technology (IT) investments (Davenport et al. 1998). As knowledge is only created by individuals, managing it requires a framework (Nonaka et al. 1995). Therefore, we apply in our KM framework well known IS tools that accept data as input and process them into information products as output (O’Brien et al. 2008). With the help of IS technology, a company can become competitive in all phases of its customer relationships (Blake et al. 1984).
2.1 Data mart overview, concepts and tools applied Data Mart (DM) is a repository of data gathered from Operational Data Stores (ODS) and other sources. The goal of the DM is to meet the specific demands of a particular group of knowledge in terms of analysis, content, presentation, and ease‐of‐use. DM present the data in terms that are familiar or in a format user want to see (Berson et al. 1997). Implementations of DM in several domains such as commerce, telecommunication and medicine have been thoroughly researched. It was proven to be useful and effective in the particular application domain of clinical research in heart surgery in Germany (Arnrich, et al. 2004). A similar research has not been done in manufacturing, despite of the potential benefits. Different reasons can be outlined. The majority of researchers in manufacturing are not familiar with DM methods and tools; many IT researchers are not familiar with manufacturing; the few researchers that have skills in both DM and manufacturing area may not have an access to manufacturing enterprise data (Wang 2007). 2.1.1 Six sigma Six Sigma is a project‐driven management approach intended to improve the organization’s products, services, and processes by reducing defects in the organisation. It is a business strategy that focuses on improving customer requirements understanding, business systems, productivity, and financial performance (Kwak et al. 2004). Six Sigma’s DMAIC method offers a thorough roadmap for analysis and diagnosis, driven by powerful tools and techniques (Heuvel et al. 2006).
Define is the first step of the Six Sigma process. During this step, a problem is identified and quantified in terms of the perceived result. The product and/or process to be improved is identified, resources for the improvement project are put in place, and expectations for the improvement project are set. The focus of the problem‐solving strategy is kept on the customer's primary requirements.
Measure step enables an organization to understand the present condition of its work process before it attempts to identify where they can be improved. It provides the substance for the problem statement. During this step, the critical to‐quality (CTQ) characteristics are defined, as well as the defects in the process or product and a physical model of the process is developed through graphical analysis. All the factors that the outputs are evaluated, and potential effects they have on failure modes are identified. The Measure step is based on valid data, so it eliminates guesswork about how well a process is working.
Analyse step adds statistical strength to problem analysis. Statistical analysis identifies a problem´s root cause by determining which factors contribute to the observed variation and how much of the total variation is explained by these factors. It can be used to calculate how much variation each dominant factor contributes to the overall problem. Interaction effects among the process variables can be observed through statistical testing.
Improve step aims to develop, select and implement the best solutions with controlled risks. The effect of the solutions that are then measured with the Key Performance Indicators (KPI) developed during the Measure step.
Control step is intended to design and implement a change to effect improvements based on the results demonstrated during the Improve step. The human element of the process is engaged to implement and manage changes in daily work activities required to achieve the targeted result of the change project. The Control step involves monitoring the process to ensure it has the discipline required to implement the change, capture the estimated improvement benefits, and maintain performance gains over the long term (Gregory 2004).
Six Sigma utilizes analytical tools and processes to measure quality and eliminate variances in processes. The objective of Six Sigma is to produce near perfect products and services that will satisfy customers (Joanna et al.
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Jevgeni Sahno, Eduard Shevtshenko and Tatjana Karaulova 2007). Dating back to the mid of 1980s, applications of the Six Sigma methods allowed many organizations to sustain their competitive advantage by integrating their knowledge of the process with statistics, engineering, and project management (Anbari 2002). Motorola was the first company who launched a Six Sigma project in the mid‐1980s (Rancour et al. 2000). Today Six Sigma has made a huge impact on industry and yet the academic community lags behind in its understanding of this powerful strategy. It lacks a theoretical underpinning and hence it is academic responsibility to bridge the gap between the theory and practice of Six Sigma (Antony 2004). From the statistical point of view, the term Six Sigma is defined as having less than 3.4 defects per million opportunities or a success rate of 99.9997% where sigma is a term used to represent the variation about the process average (Antony et al. 2002). If a company is operating at three sigma levels for quality control, this is interpreted as achieving a success rate of 93.32% or 66807 defects per million opportunities. Therefore, the Six Sigma method is a very rigorous quality control concept where many organizations still performs at three sigma levels (McClusky 2000). 2.1.2 Lean manufacturing Lean Manufacturing is a comprehensive philosophy for structuring, operating, controlling, managing and continuously improving industrial production systems (Detty et al. 2000). 2.1.3 Lean six sigma Lean Six Sigma is an integration of Six Sigma and Lean Manufacturing, both quality improvement programs originating from industry. Until recently, they were implemented as separate programs. The advent of Lean Six Sigma allows companies for the first time to combine the power of these methods into a single integrated toolkit (Aon Consulting, 2003). Nowadays these tools are highly complementary. Lean Six Sigma promotes continuous improvement of processes by both analysing sources of waste and reducing waste (Stephens 2007). Lean and Six Sigma contain a complementary range of tools and techniques which in reality will inevitably require a range of both of them. DMAIC is an effective problem solving structure that helps to be clear about what you are trying to achieve. For that reason, it is recommended to combine these techniques within a DMAIC structure (Brook 2010). 2.1.4 Production route card It is a card that gives the detail of an operation to be performed in a production line it is used to instruct the production people to take up the production work. The content and formats of the PR card vary from a company to company. In general it contains: an item and quantities to be produced; production time; dimensions; any additional information that may be required by the production worker. PR card traces the route to be taken by a job during a production process (PR Card 03.2013). 2.1.5 Faults classification standard
Figure 1: Faults classification for a machinery enterprise
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Jevgeni Sahno, Eduard Shevtshenko and Tatjana Karaulova Reliability engineering is dealing with an analysis of the causes of the faults in factories. In our case study it was developed a Faults Classification based on DOE‐NE‐STD‐1004‐92 standard shown in Figure 1. There are seven major cause categories that have its subcategories. The basic goal for using this standard is to define the problems or causes that occurred during production process for each operation in order to further correct them (DOE‐NE‐STD‐1004‐92 03.2013). This standard was adapted and modified for the machinery enterprises. 2.1.6 Failure mode and effect analysis methodology For the past few years, companies have been trying to enhance the reliability of their products to grasp such opportunity for business development. Therefore FMEA was born (Sharon 1998). It is a systematic method of identifying and preventing product and process problems before they occur. It is focused on preventing defects, enhancing safety and increasing a customer’s satisfaction. The risk of a failure and its effects are determined by three factors:
Severity (S) – the consequence of the failure that should occur during process.
Occurrence (O) – the probability or frequency of the failure occurring.
Detection (D) – failure being detected before the impact of the effect realized.
Every potential failure mode and effect is rated in three factors on a scale ranging from 1 to 10. By multiplying the rating for the three factors (S×O×D), a Risk Priority Number (RPN) will be determined for each potential failure mode and effect. The RPN will range from 1 to 1000 for each failure mode or operation. It is used to rank the need for corrective actions to eliminate or reduce the potential failures (Robin et al. 1996). All FMEAs are team based and the purpose of an FMEA team to bring a variety of perspectives and experience to the project. The team should be made of five to nine members. All team members must have some knowledge of group behaviour, they must be cross‐functional and multidiscipline, to handle the problem to be discussed (Stamatis 2003).
3. Knowledge management framework description The presented KM framework shown in Figure 2 starts from a CAD system where a design engineer creates a new Item and/or a BOM structure ‐ product. Along with the CAD models and drawings, the engineer defines an item data in the PDM system. The item data contains different attributes that are “packed” under a general Designation Code, for example ‐ (“XYZ”). This Designation Code is an item key that is used to logically connect the data between the PDM system (Saaksvuori et al. 2008) and ERP systems (Shevtshenko et al. 2007) through PDM‐ERP middleware (PDM‐ERP Middleware 03.2013). Each specific item and/or a group of items have its own unique Designation Code.
Figure 2: Knowledge management framework The PDM‐ERP Middleware transfers Designation Code and the accompanying the Item Data from the PDM (arrows labelled by 1 and 2), finds a Reference Item Code by a matching Designation Code (along with the set of Reference Item Data from ERP) and copies them into ERP Master Data (arrows 3 and 4). The final step (arrows 5 and 6) describes a similar process where the middleware finds PR in the DM (matched by a Reference Item Code) for the given item and copy it into ERP Master Data (Sahno et al. 2012). Finally, the PR is released into a production floor in the form of a PR card.
3.1 DM structure development for KM framework The purpose of our DM is to store a product data that are used in manufacturing for a specified order, analyse them, eliminate problems and assess sigma performance level for product type as well as for common
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Jevgeni Sahno, Eduard Shevtshenko and Tatjana Karaulova production system (Sahno, et al. 2013). The functionality of the DM is provided by the PR card combined with the Faults Classification standard DOE‐NE‐STD‐1004‐92 and FMEA methodology that is served by a common database. Figure 3 illustrates the fact table of the DM structure, based on the Kimball bottom‐up data warehouse design methodology (Kimball et al. 2002). This dimensional model contains data about concepts that are part of the manufacturing process. The dimensional model provides an easily communicable medium between people who understand the manufacturing process and IT workers who develop the software. It also provides the actual database structure of the DM. The developed DM enables management to access the data required for business reports from single location.
Figure 3: Fact table of the data mart structure
4. Case study Our case study will describe the functionality of our DM with Six Sigma DMAIC approach with the first three steps. These three steps will provide the basis for implementation of last two steps. Also we will present the benefits of new approach: the process of data collection from ERP system to DM, data processing in real time (data from the previous day) and sigma performance level assessment.
4.1 Data mart structure description with the define, measure and analyse steps In our example we will define the failure for each unique operation by assessing Severity, Occurrence and Detection and its RPN value in every product type that occurs during production process and the average RPN value per operation for common production system. This calculation of RPN value will allow an engineer to define the most critical operation in product or in common production system. The same assessment will be done for each Failure Group and Failure Cause mentioned in the Faults Classification. In other words, it will be a bi‐directional analysis of a unique production operation and the failures that occurred. The Figure 5 presents an example of our DM where RP card, Faults Classification and a FMEA table were combined. We will consider our DM in the logical flow of the first three steps (Define, Measure, Analyse) of Lean Six Sigma DMAIC problem solving approach. Below is discussed each step that applied in DM. 4.1.1 Define In our example, in Define step, the object or problem we consider is: Failure Group and/or Cause that belongs to an Item (Item Name and its Designation Code (described in framework)) and Production Route (Work Centre, Operation Sequence, Operation Name and Operation Time). Other words, we define the problems that we need to solve in Improve and Control step.
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Jevgeni Sahno, Eduard Shevtshenko and Tatjana Karaulova 4.1.2 Measure To measure the problem in DM, in our example we will use FMEA approach that will allow us to calculate Severity, Occurrence, Detection and its RPN value. Further, thanks to the calculated RPN value, an engineer can define the most critical process in production. Usually the Severity, Occurrence and Detection ratings in FMEA are assessed in a team, but from the author point of view this is some kind of “tricky” process because the team members can be differently educated and they can have different experience. During the discussion there could be different opinions and argues that makes it difficult to make right decision. Therefore, in this case study, during Measure step, we will measure the changes of these ratings in online mode (based on the data from the last day). In Figure 2 (arrow 7) represented the transfer of raw data from ERP system, that was collected from production floor system, to DM where they will be processed according to the specified algorithms. To attain more precise results that correspond to the real‐time production status in production floor, we propose to assess Severity, Occurrence and Detection in the following way:
Severity: This rating will be assessed in finance value. In Figure 4 below presents the example of Severity measurement. Here is presented the product – the BOM of electrical generator that consists of three main assemblies (Connected Stator, Frame and Balanced Rotor). These assemblies contain another sub‐ assemblies and components and so on until the lower level. From the right side of each component or sub‐assembly we can overview the value‐added cost (in per cent value) from the total cost of final product. From the left side we can see the total value‐added cost (in per cent value) of components or sub‐assemblies that contain lower level components or sub‐assemblies. If a component equals to 10% or less from final product cost and it does not influence on entire product quality e.g. the scrap component could be replaced or demounted from design point of view, in this case the rating of Severity will equal to 1 point. Another example if we have a component that can influence on the entire product quality e.g. the scrap component could not be replaced or demounted from design point of view from the final product, as the result the entire product may go to scrap. Here we have “Stator Coils” that has high severity rating. We can see that it costs 5% from total product, but in case it has some undetected failures it can cause to burning of entire product, loss of 100% of product. In this case the rating of Severity will equal to 10 points.
Figure 4: Generator BOM structure and its cost assessment
Occurrence: This rating will be assessed according to the data or statistics collected from ERP system and specified algorithm. For example, suppose that production floor produces 100 units of a component type per month. From 100 units we have 10 units (Occurrences) of scrap, that means that we have 10% of scrap and that can be equal, for example to 1 point of Occurrence. In reality 10% of scrap per month it may be too much, therefore a company can develop its own algorithm or standard of scrap value assessment. For example if it occurs 5% of scrap or even more, we can define that it will equal to 10 points of Occurrences.
Detection: As the purpose of this rating intended on assessment of failure detection before it happens, we propose to assess the rating of measurement tool, for example during production of some specified quantity of units. The weld crack detection or Non‐Destructive Test (NDT) can be done by magnetic flow detection or ultrasonic test or even by radiography detection; surface measurement can be done by
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Jevgeni Sahno, Eduard Shevtshenko and Tatjana Karaulova simple measurement tool, sliding calliper or laser tracker; voltage test can be tested by voltage tester; or inspection can be done simply visually. If we use the high level detection tool like radiography device or laser tracker it will equal to 1 point, if we use visual inspection, it can reach up to 10 points. Also, the rating of every measurement tool should be assessed according to the statistics gathered from production floor. For instance, if we use some measurement tool to measure 100 units, and we have not detected failure or scrap, the Detection should be equal to 1 point, if we have detected for example 10 failures, the Detection should equal to 10 points. 4.1.3 Analyse Thanks to the fact table of the DM in Figure 3, it can be possible to start the Analysis step of a product and common production system, according to the calculated data in Measure step, in the bi‐directional way by:
Operation. We shall group/sort equal operations that happened in a specified product or in a common production system to see the variability of the RPN value. Further, we shall calculate an average RPN value for a product and/or for a common production system to see an average variability and occurrence of different failures by a specific operation.
Failure Group and/or Failure Cause. It is similar to the Operation case. We shall group similar Failure Causes (sub‐groups) according to their main group category – Failure Group. From that it can be possible to see the variability of the RPN value, calculate an average RPN, see the frequency and quantity of the failure occurred. Also it can be analysed every Operation per product as well as per common production system.
When such analysis results are complete this helps in identifying the area on which to focus when building a final solution during the Improve step. The first three steps of the DMAIC process (Define, Measure, Analyse) characterize the nature of the problem to be solved. Upon completion of these steps, the problem and its root cause(s) are known and the project charter becomes a finalized document. On the basis of these steps should be created chart for implementation of Improve and Control steps of Six Sigma approach. Calculation of Sigma Performance Level We have the production process or the PR card with 12 operations where 20 failures occurred. The maximum possible RPN value for those 12 operations equal to (20x1000) 20000. In our case we will use this maximum RPN value to define the scope of the entire production process performance level, for a product, and for a common production system. This maximum value will equal to 100%. Further we shall calculate the actual percentage of the process capability for every produced product and also the average capability percentage of the common production system. As the result of it, according to the calculated percentage, we will define the sigma performance level for each product and for the common production system. To calculate the RPN percentage for both cases, we shall use the following Equation 1:
PPR =
∑ RPN ∑ RPN
PC
× 100%
Total
(1)
where: PRP – per cent of a failure by operation or a failure group/cause,
∑ RPN ∑ RPN
PC – RPN value for a particular failure group/cause or operation, Total – total RPN value by product or by a common production system.
As the result, according to the calculations for the given case (product) we have got the total RPN value of 2000 that makes 10% of the total theoretical RPN value of 20000. In order to calculate a sigma performance process yield we use the following calculation: 100% ‐ 10% = 90%. Hence, we have got 90% of the process yield
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Jevgeni Sahno, Eduard Shevtshenko and Tatjana Karaulova or positive performance level from the current product. On the basis of received results (10%) and according to the Table 1 that represents the sigma performance scale we can define the sigma performance level for the current example. Here we have got 2.78 δ or around 3 δ. Based on developed calculation it is possible to monitor on which sigma level a company operates at the current moment. The result of this example represented the input data for possible “dashboard” that monitors the company sigma performance level. Thanks to this result, the company management (Black Belt or Master Black Belt) should develop Six Sigma Improve and Control steps to improve the way of costs decreasing, product quality, product On‐Time Delivery (OTD) and a customer’s satisfaction.
Figure 5: Combination of PR card, fault classification standard and FMEA table in DM Table 1: Sigma performance scale (Gregory 2004) Sigma Perfor‐ mance Level
Defects per Million Opportunities
Process Yield
Process Capability (Cp)
1.0 δ 2.0 δ 3.0 δ 4.0 δ 5.0 δ 6.0 δ
670000 308537 66807 6210 233 3.4
33% 69.2% 93.32% 99.38% 99.9767% 99.99966%
Not capable Not capable 1.0 1.33 1.67 2.0
Process Capability (Cpk) Not capable Not capable 0.5 0.83 1.17 1.5
Estimated Cost of Poor Quality (% Revenue) >40% 30‐40% 20‐30% 15‐20% 10‐15% <10%
5. Conclusion In this paper we have presented a new Knowledge Management (KM) framework where were considered the item/product data flow between Product Data Management (PDM) and Enterprise Resource Planning (ERP) systems using a special middleware that integrated and synchronized the data between those systems and our novel Data Mart (DM). The integration provided to unite different data that generated the needed information for a product manufacturing. The framework allowed engineers to refuse from every day routine work and pay more attention to specific production issues. We have also presented the integration of the first three steps of Six Sigma DMAIC problem solving approach (Define, Measure, Analyse) into the DM. In the Define step we have defined the object or problem to be solved combining the Production Route (PR) card, Faults Classification standard DOE‐NE‐STD‐1004‐92 and Failure Mode and Effect Analysis (FMEA) methodology into a common database. For the Measure step, the data from production floor was collected into the ERP system
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Jevgeni Sahno, Eduard Shevtshenko and Tatjana Karaulova and then transferred into the DM where each rating ‐ Severity, Occurrence and Detection was processed according to the specified algorithm that allowed calculating RPN value for every process in online mode. Further, the RPN value was converted into the percentage value that finally showed us production process yield which enabled to estimate sigma performance level for the product and common production system. The combination of different concepts into the common database ensured us to perform Analysis step for production operation and the failures that occur during a production process on a product, and on a common production system level in bi‐directional way. On the basis of this three steps in DM, it can be implemented the last two steps (Improve and Control) of Six Sigma approach. The presented DM provides the report, on the basis of data from production floor that shows on which sigma performance level a company operates. The higher sigma level the higher a customer’s trust. The application of such approach in DM can be very useful for elimination of production failures, for a company when it is to be audited by a customer and for managers to make the right decision about further strategic development.
Acknowledgements This research was supported by Estonian Ministry of Education and Research for targeted financing scheme SF0140113Bs08 and Grant ETF9460 and European Social Fund’s Doctoral Studies and Internationalisation Program DoRa, which is carried out by Foundation Archimedes.
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Jevgeni Sahno, Eduard Shevtshenko and Tatjana Karaulova Rancour, T. and McCracken, M. (2000) Applying six sigma methods for breakthrough safety performance, American Society of Safety Engineers, pp. 31‐4. Riives, J., Karjust, K., Küttner, R., Lemmik, R., Koov, K. and Lavin, J. (2012) Software development platform for integrated manufacturing engineering system, 8th International Conference of DAAAM Baltic Industrial Engineering 19‐21st April 2012, Tallinn, Estonia, pp. 555‐560. Robin, E, and Mcdermott Raymond, J, and Mikulak Michael R, Beauregard. (1996) The basics of FMEA, Resources Engineering, Inc. USA. Saaksvuori, A. and Immonen, A. (2008) Product Lifecycle Management. 3rd Edition. Springer‐Verlag, Berlin, Heidelberg. Sahno, J., Opik, R., Kostina, M., Paavel, M., Shevtshenko, E. and Wang, Y. (2012) Knowledge Management Framework For Production Route Selection In Manufacturing Enterprises, 8th International DAAAM Baltic Conference Industrial Engineering, Tallinn, Estonia, pp. 567‐572. Sahno, J., Sevtsenko, E. and Karaulova, T. (2013) Knowledge Management Framework for Six Sigma Performance Level Assessment, Advances in Information Systems and Technologies, Springer‐Verlag Berlin Heidelberg, pp. 255‐256. Sharon, K.J. (1998) Combing QFD and FMEA to optimize performance, ASQC Quality Congress, Vol. 52, pp. 564‐75. Shevtshenko, E., Karaulova, T., Kramarenko, S. and Wang, Y. (2007) IDSS used as a framework for collaborative projects in conglomerate enterprises, Journal of Achievements in Materials and Manufacturing Engineering, pp. 89–92. Stamatis, D.H. (2003) Failure mode and effect analysis: FMEA from theory to execution, 2nd Edition. ASQ Quality Press. Stephens, J. S. (2007) Lean Six Sigma, Journal of Organizational Leadership & Business. Teece, D. J. (1998) Capturing Value from Knowledge Assets, California Management Review. Vol. 40, No 3, pp. 55‐79. Brook, Q. (2010) Lean Six Sigma & Minitab, Third Edition, OPEX Resources Ltd. Wang, K. (2007) Applying data mart to manufacturing: The nature and implications, Journal of Intelligent Manufacturing. Vol. 18, pp. 487–495.
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What is Your Organization’s IQ? – A Practical Tool to Gauge Enterprise Intelligence Evren Satıcı and Özalp Vayvay Marmara University, Istanbul, Turkey evrensatici@gmail.com ozalp@marmara.edu.tr Abstract: Every day, organizations create thousands of invaluable information but many of them just fly away with leaving no mark behind. To avoid this undesirable loss, organizations build knowledge management (KM) systems to keep the information recorded in the organization’s knowledge base. With increasing investments in KM implementations, measuring organizational benefits of KM initiatives has become an important agenda among KM practitioners. Measuring knowledge provides a mechanism to evaluate, control, and improve on existing performance and forms essential linkages between strategy and actions. The current study proposes that organizations have intelligence which is defined as ability to acquire and apply knowledge skills. They create information from data, identify new information adding value to its processes through analysing the information gathered and use the information in its processes. All these indicate a need for a scale to measure Enterprise Intelligence Index (EII) in order for better understanding of an organization’s ability to keep and manage information. To develop EII, total 42 item scale was constructed. 144 mid or senior level IT, HR, CRM, sales, marketing managers of leading finance, IT, telecommunications, FMCG companies in Turkey responded to the survey. Due to the composite structure of the index, the factorial structure was analysed with explanatory and confirmatory methods. The results indicated that there were 8 broad factors of EII ‐ Leadership and Vision, Organization and Structure, Culture, Partnerships, Processes, Measures, Competencies, Technology and Infrastructure with significant level of item loadings. These factors explained the total 53% of overall EII with acceptable levels of inter‐item consistency. The study was one of the first steps in understanding the difference between what organizations is currently doing and what it needs to do in order to maintain and improve its performance level regarding with EII. At the macro level the index will enable organizations to compare themselves with each other. At the micro level, it calls attention to areas needing improvement in current and future initiatives. In either case, the index provides robust indicator and basis for decision making and organizational support and development. By the help of EII, organizations will be able to monitor their own progress among years as well as they can benchmark their status compared to the competition at any given time. Keywords: enterprise intelligence index; corporate knowledge management; assessing knowledge management capabilities
1. Introduction Organizations face many problems ranging from damaged customer relations to meagre employee satisfaction levels, due to lack of effective knowledge management. It is commonly acknowledged that inadequate knowledge management increases costs, as neither best practices nor past mistakes or underperforming solutions are recorded, analysed, and organization‐wide shared. Furthermore, slow organizational learning can result in delayed product development or missed opportunities, both of which ultimately affect an organization’s profits. Above all, unavailability of such knowledge resources could demotivate employees and harm customer relationships. Inspired by recent literature, we decided to develop an index to measure enterprise intelligence, the ability of an organization to “create information from data it has; identify new information which adds value to its processes by analysing recorded information; and deploy this information in its processes” (Dinçmen, 2004). Our work was driven by the belief that better awareness, sharing and application of existing knowledge, including that originating outside a given organization, help it in converting ideas into products and processes more effectively.
2. Background Although firms have always been oriented toward accumulating and applying knowledge to create economic value and competitive advantage (Lee et al, 2005), there is no consensus in the literature on how should knowledge and knowledge management (KM) be defined. According to the American Productivity and Quality Center (Trees, 2013), KM refers to “a collection of systematic approaches to help information and knowledge flow to and between the right people at the right time (in the right format at the right cost) so they can act more efficiently and effectively to create value for the organization.” KM primarily seeks, in the view of Anantamula and Kanungo (2005), “to utilize information technology and tools, business processes, best
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Evren Satıcı and Özalp Vayvay practices, and culture to develop and share knowledge within an organization and to connect those who possess knowledge to those who do not”. With increasing investments in KM implementations in many organizations, measuring organizational benefits of KM initiatives has become an important agenda among KM practitioners. To maintain continued support of decision makers, practitioners need to both ensure and demonstrate that KM strategies have contributed to business activities. This is perhaps the main reason beyond increasing interest in KM performance measurement. Academics and practitioners sought to develop frameworks for evaluating the KM practices’ performance. Bose (2004) mentioned the need for standardized KM metrics so as to quantify the knowledge and convince stakeholders of the value of KM initiatives. He also suggested that each organization ought to create its own “unique standards for measuring intellectual capital and KM initiatives.” It is agreed that knowledge measurement provides mechanisms to evaluate, control and improve on existing performance; and has an essential role in efforts to assess the extent of alignment between strategy and actions. According to Tobin and Volasek (2006), knowledge measurement is important, since to measure facilitate management; determine what needs to be improved; provide a scoreboard for self‐monitoring; give an indication of the cost of poor implementation; provide a standard for comparisons; align efforts with business objectives. Crucially, establishing criteria for KM measurement is an imperative aspect of devising it, since the design itself inescapably partly determines or distorts the results. To improve their performance, many organizations have relied on KM. Many models and tools have been developed but no consensus has been reached on the most efficient among them. Skyrme (1999) presented one of the holistic models for assessing the sufficiency of an organization’s KM practices. He introduced a three‐layered toolkit for an evaluation of the effectiveness of KM implementation. The top layer of his proposed knowledge initiative framework comprises enablers, i.e. the organization’s vision, structure, culture, environment and leadership. The second layer consists of a set of levers which strengthen the contribution of knowledge. Among these, he listed processes, measures, hubs and centres, market leverage. Finally, foundation layer concerns the organization’s capacity and capability to embed knowledge into its infrastructure. It includes both the hard (intranet, groupware, tools, etc.) and soft (roles, skills, rewards, etc.) elements. Another approach is presented by the Knowledge Management Maturity Model (KMMM) which consists of development and analysis models. The development model defines five maturity levels and prescribes steps to be taken at a particular level for the sake of KM enhancement. The analysis model allows one to take into consideration all the KM’s important aspects. It thus helps reveal the dimensions to focus on in the future. European Foundation for Quality Management (EFQM), in partnership with CIBIT and the American Productivity & Quality Center (APQC), undertook a Benchmarking Study Project aimed at identifying good practice in the area of KM. Together, they developed a framework based on below principles:
“Knowledge Management is not an end in itself, but a means to an end … KM is a key process in increasing the intellectual capital … of the organization.
KM is most effective when it is approached holistically. This is achieved through a series of integrated initiatives aligning human resource issues, ICT [information and communication technology] infrastructures and informal learning interventions that enable the organization to improve the quality of the knowledge it holds, enhance access to and the retrieval of the knowledge.”
On the basis of the above three models and a review of other relevant literature (most importantly Tseng, 2008; Kulkarni, 2004; Chen and Chen, 2006; Crnkovic, 2005), the present research focused on the following factors:
Leadership (LEA): leaders’ involvement, guidance and “role model” image with respect to the organization’s KM
Vision and Strategy (VIS): clear strategy aligned with all functions, gathering regular feedback
Organization and Structure (ORG): organizational structure supporting and enabling KM success
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Evren Satıcı and Özalp Vayvay
Culture (CUL): organizational culture encouraging KM activities
Partnerships (PAR): involvement of customers and other external parties in KM cycle
Processes (PRO): clearly established processes ensuring effective KM
Measures (MEA): monitoring the success of KM activities
Competencies (COM): implementing trainings for and coaching employees to increase the effectiveness of KM
Technology and Infrastructure (TEC): technological infrastructure enabling effective and efficient KM system aligned with end‐user needs
The study attempts to construct an index to measure organization’s intelligence considering the dimensions mentioned above. Then it explores whether there is a relationship between the index and the performance of the organization. This research focuses on knowledge intensive industries in Turkey.
3. Methodology 3.1 Participants The target group are middle or top managers responsible for, or highly related to, KM activities in departments such as Information Technology, Human Resources, Customer Relationship Management, Sales, and Marketing. The main industries that have been focused are finance, telecommunications and information technology.
3.2 Scales Initially, an extensive questionnaire with 85 items was prepared. Open‐ended interviews with five senior managers were used to determine the validity of the questionnaire items. These interviewees were invited to comment on their clarity and understandibility. On the basis of their feedback, 42 items were selected for the final questionnaire. We used five‐point Likert scale for importance and performance, ranging from 1 (not important/successful at all) to 5 (very important/successful). The questionnaire had also multiple choice questions to understand the structure of the organization: private or government; international or local; industry; years in operation; number of employees; annual turnover of the company and the department of the participant.
3.3 Procedure Participants responded an online survey containing 42 items of EII. The participants filled the survey items considering with importance and performance dimensions of EII. The survey was open between January and March of 2012.
4. Research results 4.1 Factor analyses The factorial structure and internal consistencies of all scales used the study were investigated. Since it was a composite index, it will be good to test the factorial structure with explanatory and confirmatory methods. The overall fitness of factorial structure of each research variable was also tested with the confirmatory factor analysis (CFA) after completing explanatory factor analysis (EFA). Possible misspecifications as suggested by the so‐called modification indexes and standardized residuals values were looked for and eventually a revised, re‐specified model was fitted to the data. 4.1.1 EFA of enterprise intelligence index Before conducting explanatory and confirmatory factor analyses, Kaiser – Meyer – Olkin (KMO) and Bartlett’s test of sphericity were examined. The KMO measure of sampling adequacy revealed a value more than .50 which means that it is statistically appropriate to rely on the sampling to see if the correlations are meaningful (KMO=.92). Bartlett’s test of sphericity was significant, χ² (561) =3600,34, p < .001 and yielded a significant result. These two tests showed that the data was fit to conduct factor analysis.
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Evren Satıcı and Özalp Vayvay Table 1: Results of principal components analysis of enterprise intelligence index scale (N = 144) ITEM
F1
F2
F3
F4
F5
F6
LEA2
0,75
LEA1
0,73
LEA3
0,72
VIS5
0,71
VIS4
0,67
VIS3
0,66
VIS1
0,64
VIS2
0,53
TEC5
0,78
TEC3
0,76
TEC1
0,75
TEC6
0,74
TEC4
0,69
TEC2
0,63
CUL3
0,61
PRO2
0,75
PRO1
0,72
PRO5
0,61
MEA4
0,56
MEA5
0,52
MEA3
0,51
PRO4
0,50
ORG4
0,43
MEA1
0,75
COM1
0,72
COM5
0,68
CUL2
0,47
PAR4
0,79
PAR3
0,75
PRO3
0,68
PAR2
0,49
ORG3
0,73
ORG1
0,69
ORG5
0,65
Eigen value Variance explained
15,74
1,95
1,77
1,44
1,30
1,13
15,96
14,95
11,95
9,60
8,18
7,95
The analysis of 42 item scale revealed that there were 7 factors. However, 8 items of several dimensions were eliminated from the analyses due to either cross or low (below .30) factor loadings were discharged from the
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Evren Satıcı and Özalp Vayvay analyses. The final EFA was conducted 32 items which loaded under 6 factors of Enterprise Intelligence Index (EII) construct (See Table 1) with the Eigen values of 15,73, 1,95, 1,76, 1,44, 1,30, and 1,12.
4.2 CFA of enterprise intelligence index Although EFA ended up with six factor solution, the CFA analysis was conducted in order to test the factorial structure of overall EII. In the CFA, different factorial predictions were investigated and comparative goodness‐ of‐fit indexes were calculated. In the analysis, one‐factor solution represented the model in which all items loaded under single EII construct. 6‐Factor solution is the result of the EFA. In addition to these solutions, the predicted model of 9 factor solution of EII– Leadership, Vision & Strategy, Organization & Structure, Culture, Partnerships, Processes, Measures, Competencies, and Technology & Infrastructure – was tested and compared with other alternative models. The results of the CFA yielded that, one factor solution did not fit the data well. The iteration of the model did not produced acceptable scores. In addition, 6‐factor model (the result of the EFA) and original 9‐factor solutions were also tested and compared with each other. The results of 6‐factor model was nor fit the data well, χ² (9, N=144)= 29.81; χ²/DF = 3.13; GFI = .93; AGFI =.85, CFI=.96; NFI=.95; RMSEA=.13. The comparison of 9‐ factor solution showed a good fit with the data. However, when the modification indexes were analysed thoroughly, it was observed that the factors of Leadership and Vision were converged. Therefore, the CFA analyses were run again for 8‐factor solution. The final analysis resulted that, 8‐factor solution showed excellent fit to the data, χ²(20 N=144) = 38.53; χ²/DF = 1.92, p>.05; GFI=.93; AGFI=.88; CFI= .98; NFI = .96; RMSEA=.08. The χ² difference test was also conducted in order to check whether the difference between 9‐ factor and 8‐factor models were significant. The analysis resulted that, 8‐factor solution is significantly better 2 than the 9‐factor solution, ∆χ =28.84, p<0.05. (See Table 2) Table 2: The CFA results of EII (N = 144) Model Six Factor Solution Eight Factor Solution Nine Factor Solution
χ² 29.81 38.53 64.37
DF 9 20 27
P >.05 >.05 >.05
χ²/DF 3.13 1.92 2.49
GFI .93 .93 .90
AGFI .85 .88 .83
CFI .96 .98 .93
NFI .95 .96 .94
RMSEA .13 .08 .10
Note: GFI = Goodness‐of‐Fit Index; AGFI = Adjusted Goodness‐of‐Fit Index; RMSEA = Root Mean Square Error of Approximation; CFI = Comparative Fit Index. Six Factor Solution= The model of EFA's result; Eight Factor Solution= Predicted dimensional structure of EII.; Nine Factor Solution= Predicted dimensional structure of EII. All factor loadings were significant and above the critical value of .70 (Figure 1). These factors explained the total 53% of overall EII. The inter‐item consistency levels of the subscales were all at acceptable level (Cronbach’s α levels were .90 for Leadership and Vision; .90 for Organization and Structure; .77 for Culture; .80 for Partnerships; .87 for Processes; .83 for Measures; .89 for Competencies; and .92 for Technology and Infrastructure). As a result, EII was found out as an eight dimensioned construct and further analyses were conducted with these dimensions.
Figure 1: The CFA model of the EII
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Evren Satıcı and Özalp Vayvay Table 3: Descriptive statistics and bivariate correlations among the EII dimensions No
EII Dimension
Mean
SD
1
2
3
4
5
6
7
1
Leadership and Vision
3,33
0,84
2
Organization and Structure
3,04
0,86
,734**
3
Culture
3,39
0,70
,680**
,660**
4
Partnerships
3,33
0,76
,606**
,557**
,595**
5
Processes
3,31
0,75
,696**
,646**
,712**
,686**
6
Measures
3,35
0,79
,691**
,610**
,639**
,632**
,714**
7
Competencies
3,28
0,85
,701**
,702**
,697**
,624**
,717**
,752**
8
Technology and Infrastructure
3,27
0,90
,676**
,582**
,723**
,628**
,730**
,609**
,675**
*p<0.05; **p<0.01
4.3 Correlation between enterprise intelligence index and performance measures In order to test if there is any correlation between EII and organization’s overall performance, we have identified two parameters: Price earnings ratio (P/E) and customer satisfaction. We have run analysis with data of 15 companies out of 60 companies which have stocks in Istanbul Stock Exchange (IMKB) and we have used 2012 P/E data by December 31st, 2012. We have also used Customer Satisfaction Index results of 15 companies participated to this research. Customer Satisfaction Index results have been published quarterly by Turkish Quality Association (Kal‐Der) focusing on different industries every quarter. Analysis showed that both of the performance measures have significant correlation with EII. (See table 4) Table 4: Correlation between EII and performance measures Performance Measures Correlation with EII Price Earnings Ratio
,719*
Customer Satisfaction
,733*
*p<0.01
4.4 Industry specific enterprise intelligence index results The EII helps industries to see company specific or industry specific results This benchmark is likely to encourage best practices among companies and industries. Based on the results, Pharmaceutical companies have the highest score among all industries. (See Figure 2)
Figure 2: Comparison of EII of different industries
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Evren Satıcı and Özalp Vayvay EII also provides opportunity for companies to identify a prioritized action plan in order to achieve better results with KM. Again, based on the survey results “Technology and Infrastructure” and Leadership and Vision” factors are prior action areas since their performance ratings are relatively low although their importance ratings are higher compared to other factor. (See Figure 3)
Figure 3: Quadrant analysis of dimensions based on average performance and importance ratings covering all responses
5. Conclusion Initially, we have structured our model with nine factors, after explanatory and confirmatory factor analysis; we have finalized our model with eight factors merging “Leadership” and “Vision and Strategy” factors. We have also examined the correlation of the index with customer satisfaction and price earnings ratios. The significant correlations indicates companies having higher “enterprise intelligence index” is more likely to have higher customer satisfaction and price earnings ratio. Comparison of industries showed us Additionally, quadrant analysis has showed us, “Leadership and Vision” and “Technology and Infrastructure” are prior action areas since they have higher importance and lower performance compared to other factors. This analysis can be done at industry and company level and comparisons can provide insightful results. Action points at company level can help to define KM strategies and planning. This paper proposed a new index for assessing enterprise intelligence. On the basis of EFA, CFA and correlation analysis, we can conclude that the EII can not only measure the level of KM capabilities of an organization adequately, but also that its values significantly correlate with performance measures (P/E and customer satisfaction). If implemented, the EII would be a useful tool assisting organizations in prioritizing their KM efforts in order to increase their intelligence level through improving their KM systems. The EII would also allow them to keep track of the trends in KM performance over the years.
References Anantatmula, .V and Kanungo, S. (2005), “Establishing and Structuring Criteria for Measuring Knowledge Management Efforts”, Paper read at 38th Hawaii International Conference on System Sciences Bose, R., 2004, “Knowledge management metrics”. Industrial Management & Data Systems, Vol. 104, No. 6, pp. 457‐468. Chen, M.Y and Chen, A.P. (2005) “Integrating option model and knowledge management performance measures: an empirical study”, Journal of Information Science, Vol 31 No. 5, pp. 381–393 Chen, M.Y and Chen, A.P. (2006) “Knowledge management performance evaluation: a decade review from 1995 to 2004”, Journal of Information Science, Vol 32 No .1, pp. 17–38 Crnkovic, J, Belardo, S, and Asoh, D.A, (2005), “Exploring the Knowledge Management Index as a Performance Diagnostic Tool”, Systemics, Cybernetics And Informatics, Vol 3 No. 2 Dinçmen, Murat, 2004, “Kurumsal Zeka için Çerçeve Konu: Bilgi Yönetimi”. 9. Enformasyon ve Yönetim Sempozyumu‐ Business Intelligence&Data Mining, 28 Mayıs 2004, Marmara Üniversitesi, İstanbul.
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Evren Satıcı and Özalp Vayvay EFQM (2005), “The EFQM Framework for Knowledge Management ‐ Assessing the Organisation’s Knowledge Management Capabilities” Knowledge Management Maturity Model official web site, www.kmmm.org Kulkarni, U and Freeze, R. (2004) “Development and Validation of a Knowledge Management Capability Assessment Model” Paper read at 25th International Conference on Information Systems Lee, K.C, Lee, S., and I.W. Kang, 2005, “KMPI: measuring knowledge management performance”. Information & Management, Vol.42, No. 3, pp. 469‐82. Skyrme, David J, 1999, Knowledge networking: creating the collaborative enterprise. Butterworth‐Heinemann, Oxford, UK, 311 p. Tobin, Peter K. J., Volavsek, Peter, 2006, “Knowledge Management Measurement in South African Organisations”. Mousaion, Vol. 24, No. 1, pp. 96‐118. Trees, Laureen. (2013) “Now in the Knowledge Base Knowledge Management”, [online], APQC, http://www.apqc.org/knowledge‐base/download/226176/a%3A1%3A%7Bi%3A1%3Bi%3A2%3B%7D/inline.pdf Tseng, Shu‐Mei (2008), “Knowledge management system performance measure index” Expert Systems with Applications Vol 34 pp734–745
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Intra‐Organizational Cooperation and Knowledge Sharing: A Comparison of Slovak LIS University Departments Peter Steranka Department of Mediamatics and Cultural Heritage, Faculty of Humanieties, University of Žilina, Žilina, Slovak Republic peter.steranka@mediamatika.sk Abstract: Two decades ago, western society was characterized by increasing globalization, technological advances on the field of ICT and knowledge revolution. These terms could not reach current popularity without development in ICT and knowledge management. Knowledge management helps companies in optimization of the processes that are associated with creating, gathering, storage, distribution and what’s the most important ‐ use of knowledge and experience, that supports and accelerates the creation of new knowledge, which leads to innovations. Success of knowledge management in institutions depends mainly on the processes of knowledge sharing. Today’s knowledge based economies require not only technological resources but also human capacity for a knowledge‐intensive global economy. This leads to two the different paradigms of knowledge sharing ‐ one being technologically oriented and the second one oriented socially. The western countries focus on technological advances. Point of view on electronic communication tools changed and they are no longer considered as the resource within knowledge management but are considered as collaborative tools of knowledge‐driven organizations of today. The knowledge sharing can be improved not only by customizing e‐communication tools, but also by taking social aspects into account and implementing proper techniques of managing the human resources. By customization and/or enhancement of those e‐communication or collaboration tools, there is higher chance of their successful acknowledgement and implementation by users. Basically meaning – the more comfortably used, the more knowledge sharing you will get. At the beginning of this study we propose a brief experience and some hints on the implementation of Google Apps at Department of Mediamatics and Cultural Heritage. The aim of this research is to discuss and investigate whether the implementation of e‐communication and collaboration tools – Google Apps, did increased knowledge sharing in the selected organization. The objective was to measure indicators of scientific activity of two selected organizations and to compare the results taking different variables into account. The survey took place at two Slovak LIS faculties. The utilized research methods were bibliometry and comparison research. Keywords: knowledge management, knowledge sharing, collaboration, ICT, e‐communication tools
1. Introduction Today’s developed parts of the world can be characterized by words like knowledge society, knowledge based economy, innovations, competitiveness, long term growth, sustainability, green economy and/or smart enterprise, where managing knowledge has significant impact on the success of organizations. Hitt, Ireland & Lee (2000) argue that at the 21st century dawn, number of factors including increasing globalization, technological developments, the increasingly rapid diffusion of new technology and the knowledge revolution are transforming business environments and the general societies that provide their context (Ireland & Hitt, 1999; Hitt, Ireland & Lee, 2000). This transformation is causing firms to reconsider their behavior and traditional methods of competition to create value in the globalized business environment (Hitt et al., 1999; Hitt, 1998a). Knowledge always played important role in the evolution of mankind. This idea isn’t new; many theorists in the field of economy, sociology and/or politics developed numerous constructs and various labels and terms are used to identify the 21st century’s new competitive era. The most prominent and frequently used terms are hypercompetitive environments (D’Aveni, 1994), the postindustrial society (Lowendahl and Revang, 1998) or the new frontier (Hitt et al., 1998b; Hitt, Ireland & Lee, 2000). These constructs evolved into what we know today known as the more common term – knowledge society. Hornidge (2013) states that the terms ‘knowledge society’ and ‘information society’ packs the increasing importance of different types of knowledge for further development of economies and societies, that was originally assessed and conceptualized mainly by academics from Japan, the USA and Europe. According to Nico Stehr a majority of jobs in the knowledge society involves working with knowledge. ”Contemporary society may be described as a knowledge society based on the extensive penetration of all its spheres of life and institutions by scientific and technological knowledge.” (Stehr, 2003) Despite this, Qvortrup (2006) argues
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Peter Steranka that any explicit, sociologically relevant definition of knowledge is missing in most of the knowledge society theories. Drucker identifies knowledge as the central, key resource. He argues that the main working class will become “knowledge workers” (Drucker et al., 2008). According to OECD, almost two centuries before Drucker, Smith defined new layers of “specialized workers” who are essential in the process of creation economically useful knowledge. List also studied this phenomenon. Unlike Smith, List emphasized the importance of infrastructure and institutions in the mechanisms of creation and distribution of the knowledge, which subsequently reflects in the society development and growth of productivity. Modern scholars like Galbraith, Goodwin and Hirschman further developed the idea of innovation as a key force of economic dynamics. “And economists such as Romer and Grossman are now developing new growth theories to explain the forces which drive long‐ term economic growth.” (OECD, 1996) Very important is also the culture of organization, which represents the sum of shared values, attitudes, relations and methods of their creation, has a significant impact on the process of knowledge sharing. Culture can be perceived as collective knowledge that’s in our subconscious and significantly, although often unconsciously, influence our behavior (Rumizen, 2002; Katuščáková, 2009). Culture supporting knowledge sharing is characterized by the willingness to share their experience and knowledge, as well as trust, mutual respect, acceptance of mistakes, getting together, teamwork, live debates, common language, willingness to take risks and accept ideas from outside and also virtue of knowledge sharing (Dologová, 2001; Katuščáková, 2009). Katučšáková pointed out that the myth persists that the transfer of valuable information or own know‐how can mean losing a job. The cause is incorrect evaluation of workers for their individual success and not for the teamwork. The correct organizational structure, methods leadership and stressing out the importance of communities is equally important (Katuščáková, 2009). New forms of electronic communication and vast array of collaborative tools in conjunction with new media eliminate mainly geographical barriers and enables remote collaboration in nowadays globalized world. It’s a paradox, or a new paradigm, that these new tools and practices involving knowledge sharing are popular also within organizations that do not face the challenges of geographically distributed R&D. Organizations and firms may appear under pressure to transform and follow this trend in order to competitively engage the globalized markets.
2. Research problem Hylton (2002) argues that more than 80% of KM initiatives fail. It was predicted that in 2003 about 500 organizations spent about 31.5 billion USD at the KM initiative that fortunately have failed. According to Katuščáková (2010) researches shows that first implementers of KM initiatives turned their attention to explicit knowledge, how the knowledge is captured, documented and stored in the knowledge management systems. However, the majority of organizations (in little extent) believe that KM creating a refined information platform is the key. Too often organizations just waste huge financial capital by deploying KM technologies without previous knowledge audit realization (Hylton, 2004). Our research problem can be seen through our main objective which is to investigate and discuss the impact and successfulness of implementation and utilization of communication and collaboration tools (in form of Google apps) in the selected organization.
3. Research settings The survey took place at two Slovak library and information science/study (LIS) departments. Department of Library and information science at Comenius University (for better orientation purposes we’ll use KKIV abbreviation or (Case A) tag) served as a control. KKIV has more than 50 year tradition as a part of Comenius University and it’s the oldest and most prestige university department within the LIS branch in Slovakia which provides education in the field of library and information studies and sciences in all three levels of university education. Department of mediamatics and cultural heritage (KMKD for short and with (Case B) tag) at University of Žilina is the only department in Slovakia, offering the opportunity to study at the same level and in the related field
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Peter Steranka of education, but with unique focus. The KMKD department, with only 10 year tradition, seeks to progressively nudge forward. With such a short life, organizational knowledge and wisdom are arguably absent. KMKD has implemented new forms of electronic communication and collaborative tools with the aim of improving communications and establish an environment for successful and meaningful knowledge sharing. At the present, department is the only institute among Slovak universities that is officially using Google apps.
4. Implementation of collaboration tools Management of the KMKD department identified contemporary trends, values and potential gains associated with processes of knowledge sharing and organizational learning; and initiated steps for improvement by implementation of technologies and practices to support knowledge sharing. Many research inquiries have been made by KMKD academics to gather sufficient understanding in the subject. Numerous software solutions are available on the market offering specialization, customization or open access but often are difficult to implement, utilize, maintain or administrate and often have compatibility issues. On the other hand, it’s possible to use complex solutions that offers multiple services, applications and tools simultaneously and are inexpensive or even free, easy to obtain, implement and use in everyday live. In 2010, based on actual needs and previous research efforts, KMKD decided to implement Google apps. According to Guttenová et al. (2011) Google apps exceptionally fulfilled the requirements for complex, simple to use and maintain communication and collaboration tool. Google apps offer additional benefits such as no acquisition costs (applies only to educational institutions), no infrastructure costs (whole service runs on Google's servers), high level security and large storage space. It was expected that Google apps would improve internal communication and enable additional, advanced forms of collaboration. Jakubíková has pointed out “if personal meetings are not always possible, the advantage of Google’s communication and collaboration tools becomes apparent especially for distance learning students.” (Jakubíková, 2013) Also Collaboration Suite Recommendation published by Chair, L.J., et al. (2010) from the University of Michigan proved that organization get substantial returns by Google apps implementation.
5. Google apps achieved results In the confines of systematic implementation of the communication and/or collaboration tools the decision has been approved by the KMKD department to introduce and systematically utilize three basic Google apps. Gmail (e‐mail) serving as a main channel for asynchronous communication; includes GTalk (chat, video and audio conferencing) enabling synchronous communication. Google Docs (online collaborative document production) also includes Google Disc service (data storage and sharing platform) and most handy web application Google Calendars (time management and workflow). Following results are available after the first months of usage by teachers and students:
more than 900 email accounts have been created (in the first year),
more than 600 users uses the services regularly (in the first year),
calendars enables planning of events, schedules or other activities,
streamlines communication and creates mutual dialogue,
enables sharing of research results, study materials, or drafts associated with research or educating process (Guttenová et al., 2011).
Google apps were introduced mainly in order to improve communication between distance learning students. As mentioned earlier, the paradox is that Google apps become also popular among the full time students and even more popular between KMKD academics. In this role, Google apps fulfill their primary purpose as communication and collaborative platform and perfectly complement Moodle an e‐learning system of University of Žilina.
6. Data collection and methods Katuščáková (2010) states that defining and evaluating the success of remote scientific collaboration is a complex and unresolved problem. In the literature we can find different perceptions of success ranging from
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Peter Steranka the revolutionary new scientific ideas to testing of new software solutions, technologies and so on. Often, as the success of collaboration is considered: influence on the science and scientific careers, education and public perception as well as inspiration to develop new collaboratories and new collaboration tools. The initial objectives of the collaboration include efforts to increase productivity and the number of participants, as well as efforts to democratize science to help improve access to elite researchers and laboratories (Finholt and Olson 1997; Hesse et al. 1993; Walsh and Bayma 1996). Traditional methods of measuring success in science are aimed at individuals and include, for example measuring productivity (e.g. number of publications, presentations, patents and number of graduates) awards and honors, and the impact of labor determined prestige of publication output or the number of citations of publications by other scientists (Prpic 1996; Shrum, Chompalov, and Genuth 2001). Some of the methods of measurements mentioned above can be used to evaluate the results of large‐scale, distributed and interdisciplinary collaborations, but most of them are not suitable to assess the full range of objectives of many current projects (Katuščáková, 2010). In this study, we focused our attention only on the factor of productivity and on the partial mapping of cooperation of the KMKD academic staff. To obtain a holistic view of the research topic, we also attempted to apply the inquiry methods on the KKIV to obtain a control. Since we were unable to collect the necessary data from indexed databases, we decided to use a Central register of publication activity of Slovak Republic. One of the parameters of redistributing funds to universities, which utilizes the Slovak Ministry of Education, is summary publication activity of all individual departments of the universities from previous years. Because of this fact, we think it’s safe to consider this data to be valid. Because this database works only since 2007, we collected the data for the years 2007 ‐ 2012 (since 2013 is current and still open) and therefore collected data will be considered as representative. By analyzing the results, we will try to answer the following questions:
What’s the publication activity trend of surveyed institutions?
What impact did the introduction of Google apps have for publication activity?
Who are the most active cooperating authors during the reporting period?
Is it possible to determine the quality of cooperation only from results of bibiometric analysis?
Analyzing the results of publication activity for the years 2007 – 2012, we found the names of all authors involved. Following, we made excerptions of the most cooperating authors. Glänzel (2003), for example, lists four factors that affect the publishing activity of scientists described by Lotkas law: field of science, author age, social status of the author, the reporting period. In this study, we used the method of bibliometic analysis, while focused on measuring productivity through the number of publications. Ondrišová (2011) argues that bibliometrics deals with the quantitative aspects of the production, distribution and use of recorded information; and mainly utilize mathematical and statistical methods. Researches in bibliometrics utilize simple statistical reports obtained from the bibliography or citation databases (number of publications, citations, etc.) or methods of descriptive statistics. The bibliometric research scope is very broad and each type requires not only adequate methods and techniques, as well as an appropriate representation of the data being processed (Glanz, 2003). In this study, we used the representation of processed data by tables, graphs, the incidence matrix of collaboration and their graphical representation in the personal brain.
7. Results and findings Detailed analysis and correct interpretation of the data collected on publications is essential and can help in mapping the situation in the terms of publication productivity and in the hindsight of co‐authorship. The analysis can reveal some of the factors that can affect overall culture of selected organizations and the occurring practices in a culture of sharing. In order to statistically evaluate data, the publications were classified according to several aspects. The main aspect was the year in which the publication was registered. We focused only on years from 2007 to 2012 because of practical reasons and limitations of data repository, from which we collected the data. Another criterion for sorting was the category of publications. With regard to the possibility of further work with the
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Peter Steranka data and following evaluation, we created two categories of documents (professional and scientific). This task was partially simplified because already registered entries in the database of publications (in order to be catalogued) had undergone evaluation/assessment by competent library department. After the approval by library department, the publication could be included in the central repository of publications. Other relevant classification criteria were to distinguish whether the publication had one or multiple authors and again distinction between the categories of the publications. Based on the data analysis of publication activity of both departments for the years 2007 and 2012, we prepared transparent tables. Based on these data, we abstracted summary calculations which are presented in the following tables 1 & 2, which represents the number of all publications on one author from the mentioned organization, as well as, the number of scientific publications per person (all numbers are averaged to correspond actual personnel matrices). We would like to point out, that in each year we worked with actual personnel matrices because there was hardly a year without personnel fluctuations that would misrepresent the results of analysis. Table 1: Comparison of averaged no. of all publications and all scientific publications of KKIV (Case A) All publications Sc. publications
2007 2008 2009 2010 2011 2012 2,636 3,363 3,727 3,917 3,917 2,500 1,272 1,454 1,909 1,750 1,833 1,700
Table 2: Comparison of averaged no. of all publications and all scientific publications of KMKD (Case B) 2007 2008 2009 2010 2011 2012 All publications 2,636 1,750 3,083 2,438 2,500 3,316 Sc. publications 1,272 0,750 1,750 1,563 1,125 3,053
Based on these tables, we generated graphs in which we created a polynomial trend curves used in cases with fluctuating values. This way, it will be easier to monitor any activity and publications trend tendencies.
Figure 1: Comparison of averaged no. of all publications and all scientific publications of KKIV (Case A)
Figure 2: Comparison of averaged no. of all publications and all scientific publications of KMKD (Case B) As can be seen from table 1 & figure 1 average publication activity per one KKIV (case A) scholar (for the period 2007‐2009) reached a peak in 2009, but in subsequent years began to decline slightly. The trend curve of averaged scientific publications to some extent follows the trend line of all publications. Interesting is the sudden drop in the category of all publications in 2012, while the average number of scientific publications,
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Peter Steranka despite a slight decline, remained in the same levels. After closer analysis, we found out, that in 2012 KKIV has lost its key professor, who noticeably published in the professional category. In the case of KMKD (case B) fluctuations in publication activity can be observed from table 2 & figure 2. Here, in the contrast to KKIV (case A), is interesting to see the substantial increase in scientific publications in 2012, which almost equaled the total publishing production of the department. Closer analysis revealed, that the Department KMKD (case B) undergone in 2010 restructuring changes, most significant of which had been the introduction of Google applications which was the main component of integrated communication and collaboration tools and practices. It took some time for students and especially staff to get used for the new integrated communication and collaboration tools/practices, which is why we can’t see noticeable increase in 2011and in fact there is a decrease in case of total scientific publications. In 2012 also staff changes were made and greater emphasis on publishing in scientific journals and conferences was stressed out. Subsequently, we analyzed the co‐authored publications, where we also monitored the types of publications, as well as author names, so that we could determine the clusters of co‐authors and identify scholars that favor and follow the collaboration philosophy. We monitored both the total number of co‐authored publications, as well as co‐ authored scientific publications. In this case, we came up on an interesting situation. Table 3, 4 & figure 3, 4 shows the development of co‐authorship publications in observed time period. Table 3: Comparison of averaged no. of all co‐authored and all scientific co‐authored publications of KKIV (Case A) 2007 2008 2009 2010 2011 2012 All co‐autorship pub. 0,182 0,272 0,545 0,833 1,167 1,100 Sc. co‐autorship pub. 0,182 0,181 0,363 0,333 0,833 1,000
Table 4: Comparison of averaged no. of all co‐authored and all scientific co‐authored publications of KMKD (Case B) All co‐autorship pub. Sc. co‐autorship pub.
2007 2008 2009 2010 2011 2012 1,090 0,667 0,917 1,063 1,063 1,211 0,727 0,333 0,750 0,625 0,563 1,211
Table 3 & figure 3 shows that not only the total but also the scientific co‐authored publications of KKIV (case A) during the observed period gradually increase despite the fact that the department doesn’t utilize knowledge sharing technologies in such extent as the KMKD (case B) can. The exception is 2012, where the averaged total number of co‐authored publications began decline. Despite the overall decline in the mentioned publication class, scientific co‐authored publications retained an increasing trend line. The ratio between the total and the scientific co‐authored publication activity reduces, while the amount of professional co‐authored publications is dropping.
Figure 3: Comparison of averaged no. of all co‐authored and all scientific co‐authored publications of KKIV (Case A) For KMKD (case B) (table 4 & figure 4) the overall and the scientific co‐authored publication activity oscillates, but in the last year the scientific co‐authored publications recorded steep increase and even equaled the total publications. The ratio between the overall and the scientific co‐authored publications is zero, meaning that from all co‐authored publications produced, all are classified as scientific. In case B, we see a significant increase in the efficiency of co‐authored publications published. The interesting results have come from a combination of the gathered data. For example, we compared the averaged numbers of the total publications produced with the averaged numbers of the overal scientific co‐authored publications (tables 5, 6).
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Figure 4: Comparison of averaged no. of all co‐authored and all scientific co‐authored publications of KMKD (Case B) Table 5: Comparison of averaged no. of all publications and all scientific co‐authored publications of KKIV (Case A) 2007 2008 2009 2010 2011 2012 All pub. 2,636 3,363 3,727 3,917 3,917 2,500 All co‐autorship pub. 0,182 0,272 0,545 0,833 1,167 1,100
Table 6: Comparison of averaged no. of all publications and all scientific co‐authored publications of KMKD (Case B) 2007 2008 2009 2010 2011 2012 All pub. 2,636 1,750 3,083 2,438 2,500 3,316 All co‐autorship pub. 1,090 0,667 0,917 1,063 1,063 1,211
The trend curves of the co‐authored scientific publication activity in both cases essentially follows the curves of the total (averaged) number publications. For KMKD (case B) has the summary publications trend line oscillating character. We would like to point out that years 2008 and 2011 are very important to compare in context of surounding years. In the year 2008 we found out that there was a decline in the overall scientific co‐ authored publications, but in 2011 (the year after the introduction of Google applications and where by folowing the trend line we could expect decrease) the value of scientific co‐authored publications is the same as in the previous year (2010). Meaning that knowledge sharing tools may have helped or even stop the depresion to ocure in 2011.
Figure 5: Comparison of averaged no. of all publications and all scientific co‐authored publications of KKIV (Case A) It is possible to determine the average number of publications per member, from the data in tables 1, 2 & figures 1, 2, which in the case of KMKD (case B) in the last year (2012) more than doubled over the previous year (2011). The increase in the number of publications does not necessarily prove that the organization has improved cooperation between the authors themselves or that the introduction and implementation of Google applications (as a tool to facilitate and support communication, collaboration and knowledge sharing, at the end of 2010), has brought an increase in the number of co‐authored publications. Tables 7 & 8 are showing the index of co‐authorship.
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Figure 6: Comparison of averaged no. of all publications and all scientific co‐authored publications of KMKD (Case B) Table 7: The co‐authorship index of KKIV (Case A) 2007/2009 2009/2010 2011/2012 The sum of co‐pub. authors/no. of pub. Co‐authorship index
4/4 1
19/8 2,375
19/12 1,583
Table 8: The co‐authorship index of KMKD (Case B) 2007/2009 2009/2010 2011/2012 The sum of co‐pub. authors/no. of pub. Co‐authorship index
19/8 2,735
26/8 3,25
34/15 2,266
While in the years 2007‐2008 was the co‐authorship index 1 (KKIV, case A) and 2,735 (KMKD, case B), in 2009‐ 2010 rose to 2.375 (case A) and 3.25 (case B) and in the last measured period the index fell in both cases to 1,583 (KKIV) and 2,266 (KMKD) respectively. It is obvious that KMKD (case B) has more advanced culture of cooperation even before the Google apps implementation. In the next step we examined the cooperation of scholars from both departments by analyzing the co‐ authorship patterns. The result was the incidence matrix of cooperation that shows a network of co‐publishing authors and a number representing intensity of occurred cooperation. For easier and more transparent view of the relationships between authors, we attempted to create a graphical representation of the incidence matrixes and the following figures 7‐12 is the result. We worked with the data of total co‐authored of publications and we found occurrence and multitude of natural co‐authorship clusters. To be able to process and work with the data, we divided the observed time period of 6 years into 3 stages. Most important and determining for us was the end of the year 2010 when the KMKD (case B) introduced Google apps within the organization.
Figure 7: The incidence matrix visualization Figure 8: The incidence matrix visualization of KMKD (Case of KKIV (Case A) ‐ years 2007 ‐ B) ‐ years 2007 ‐ 2008 2008
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Peter Steranka In figures 7, 9, 11 we can see not only a gradual development but also the intensity resp. frequency of cooperation in the publication activity of KKIV (case A). We can conclude that the co‐authorship is steadily increasing in each of the 3 stages, while the most active member in the "spider web" of collaboration is labeled as "other authors". This group consists mostly of PhD students and external partners. A deflection occurs at the last stage, where we can see improvement of both frequency but also quality of cooperation because numerous links are emerging between the members themselves.
Figure 9: The incidence matrix visualization of Figure 10: The incidence matrix visualization of KMKD (Case KKIV (Case A) ‐ years 2009 – 2010 B) ‐ years 2009 ‐ 2010
Figure 11: The incidence matrix visualization of Figure 12: The incidence matrix visualization of KMKD (Case KKIV (Case A) ‐ years 2011 ‐ 2012 B) ‐ years 2011 ‐ 2012 For KMKD (case B & figures 8, 10, 12) is the total co‐authorship publication activity increased from the initial stage, while in the second period (2009‐2010) remains constant; and remarkable is the final stage where the co‐authorship publication activity almost doubled. Notice that at the end of 2010 KMKD (case B) implemented communication and collaborative tools in the form of Google apps. If we synthesize data from previous tables and figures 2 & 4 with data arising from figure 12, we can conclude that in 2011 members KMKD only acquainted with the possibilities Google apps offered and actual contribution is partially reflected in the year 2012.
8. Discussion and conclusion Despite data gathering problems, it’s possible to implement bibliometry methods even on relatively small R&D organizations and get interpretable results. Although we acknowledge that if we wouldn’t be part of the KMKD department, the results would be difficult or even impossible to interpret without additional inquiry investigations. Investigation and measurement of the impact of the Google apps implementations was the key objective of this study. Analysis revealed that even thou studied organization did implemented technologies that enables and enforces the knowledge sharing and creating processes, effective knowledge management still absents. From visualization of incidence matrix data we can conclude that the knowledge is not flowing within the organization rather is “leaking out”. We managed to determine the trends in publication activity as well as identify members who actively promote the collaboration philosophy in the surveyed organization and can confirm that quantitative methods proved to be partially useful. But not all questions proposed in the Data collection and methods section can be
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Peter Steranka answered with satisfying degree of depth or certainty. Even thou we did measured increase in publication and overall co‐authorship activity; we can’t exactly quantify the effect of Google apps implementation. Some other factors (like changes in the personal matrixes) affected the overall results. To get better and more precise results, other methods (e.g. citation analysis…) needs to be incorporated. This study, created as a showcase for quantitative measuring of successfulness of knowledge management implementation efforts (especially with the hindsight on technological paradigm) is only a small part of bigger and more complex research project and in overall can be evaluated positively. Methods employed in this study can prove very useful in the productivity and efficiency measurement of the forming collaboratory – National centre of excellence Memory of Slovakia. We can only agree that “the process of implementing KM into practice is difficult, very broad, and the first results will not show in the near future. Nevertheless, we suppose that the energy invested in the effort to detect and resolve bottlenecks of collaboration and in the effort to implement the idea of KM in scientific research collaboration is the correct investment.” (Katučšáková, Katuščák, 2012)
Acknowledgements This publication is a result of implementing the Memory of Slovakia: National centre of excellence in Research, Preservation and Accessibility of Cultural and Scientific Heritage” Project (ITMS:26220120061) supported by Research & Development Operational Program funded by the European Regional Development Fund.
References Bradford W. Hesse, B.W., Sproull, L.S., Kiesler, S.B., Walsh, J.P. 1993. Returns to science: Computer networks in oceanography. In: Communications of the ACM 36 (8), pp 90–101. Chair, L.J., et al. 2010. Recommendations on a strategy for adoption of a contemporary collaboration environment for the University of Michigan community. <http://nextgen.umich.edu/collaboration/U‐ M_Collaboration_Suite_Recommendation.pdf>. D’Aveni, R.A. 1994. Hypercompetition: Managing the Dynamics of Strategic Maneuvering. New York: The Free Press. Dologová, M. 2001. Znalostný manažment a praktiky podporujúce zdieľanie znalostí a procesy učenia sa. In: Znalostný manažment – kľúč k úspechu. Bratislava: Dom techniky ZSVTS. Drucker, P. F., Collins, J., Kotler, P., Kouzes, J., Rodin, J., Rangan, V. K., Hesselbein, F. 2008. The Five Most Important Questions You Will Ever Ask About your Organization. San Francisco: Jossey‐Bass. Finholt, T.A., Olson, G.M. 1997. From laboratories to collaboratories: A new organizational form for scientific collaboration. In: Psychological Science 8, pp 28–36. Glänzel, W. 2003. Bibliometrics as a research field: A course on theory and application of bibliometric indicators. Course handouts. <http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.97.5311&rep=rep1&type=pdf>. Guttenová, O., Kozoková, E., Rusinková, J., Tornáryová, I. 2011. Google aplikácie pre vzdelávanie ako nástroj pre zvyšovanie mediálnej gramotnosti pedagógov a študentov. In: Mediálna výchova v kontexte celoživotného vzdelávania pedagógov, Žilina: KMKD FHV ŽU Hitt, M.A. 1998a. Twenty‐first‐century organizations: business firms, business schools, and the academy. Academy of Management Review 23, pp 218–224. Hitt, M.A., Ricart, J.E., Nixon, R.D. 1998b. The new frontier. In: Hitt, M.A., Ricart, J.E., Nixon, R.D. (Eds.), Managing Strategically in an Interconnected World. New York: Wiley, pp. 1–12. Hitt, M.A., Ireland, RD., Hoskisson, R.E. 1999. Strategic Management: Competitiveness and Globalization, 3rd Edition. Cincinnati: Southwestern College Publishing. Hitt, M.A., Ireland, D.R., Lee, H. 2000. Technological learning, knowledge management, firm growth and performance: an introductory essay. Journal of engineering and technology management 17, pp 231–246. Hornidge, A.K., 2013. Knowledge’, ‘Knowledge Society’ & ‘Knowledge for Development’: Studying Discourses of Knowledge in an International Context. In: Methodologie und Praxis der Wissenssoziologischen Diskursanalyse, Theorie und Praxis der Diskursforschung 2013, pp 397‐424. Hylton, A. 2002. A KM initiative is unlikely to succeed without a knowledge audit. <http://www.providersedge.com/docs/km_articles/km_initiative_unlikely_to_succeed_without_a_k_audit.pdf>. Hylton, A. 2004. The Knowledge audit is first and foremost and audit. <http://hosteddocs.ittoolbox.com/KAuditpaper.pdf>. Ireland, D.R., Hitt, M.A. 1999. Achieving and maintaining strategic competitiveness in the 21st century: the role of strategic leadership. Academy of Management Executive 13, pp 43–57. Jakubíková, B. 2013. Personal communication dated 21. 3. 2013. Katuščáková, M. 2009. Znalostný manažment. In: ITlib. Informačné technológie a knižnice, 2009 (4). Katuščáková, M. 2010. Manažment znalostí vo vedeckej kolaborácii [Dizertačná práca]. Bratislava: FFUK.
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Peter Steranka Katučšáková, M, Katuščák, M. 2012. Recommendations for Scientific Collaboratories: Application of KM Findings to a Scientific Collaboratory. In: Proceedings of the 13th European Conference on Knowledge Management 1, pp 576‐ 583. Lowendahl, B., Revang, O. 1998. Challenges to existing strategy theory in a postindustrial society. Strategic Management Journal 19, pp 755–773. OECD. 1996. The knowledge‐based economy. Organization for economic co‐operation and development, Paris. Ondrišová, M. 2011. Bibliometria. Bratislava: STIMUL. Prpic, K. 1996. Scientific fields and eminent scientists’ productivity patterns and factors. In: Scientometrics 37 (3), pp 445– 471. th Qvortrup, L. 2006. The Concept of “Knowledge” in the Knowledge Society and Religion as 4 Order Knowledge. ISA conference, Sociocybernetic section 2006, Durban. Rumizen, M.C. 2002. The Complete Idiot´s Guide to Knowledge Management. Indianapolis: Alpha Books. Shrum,W. , Chompalov, I. ,Genuth,J. 2001. Trust, Conflict and Performance in Scientific Collaborations. In: Social Studies of Science 31 (5), pp 681‐730. Stehr, N. 2003. A world made of knowledge. Lecture at the conference “New knowledge and new consciousness in the era of the knowledge society". Budapest. Walsh, J. P. Bayma, T. 1996. The virtual college: Computer‐mediated communication and scientific work. In: Information Society 2 (4), pp 343–363.
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The Role of Individual Factor in Knowledge Sharing Behavior Among Profit Oriented Webloggers Ruzleeta Zakaria, Nor Intan Saniah Sulaiman, Haslinda Ibrahim, Mohd Syazwan Abdullah, and Nerda Zura Zabidi Universiti Utara Malaysia, Sintok, Malaysia ruzleeta@uum.edu.my norintan@uum.edu.my linda@uum.edu.my syazwan@uum.edu.my nerda@uum.edu.my Abstract: Knowledge management involves a variety of important processes such as knowledge creation, knowledge sharing and knowledge dissemination. There are two objectives that must be considered in order to achieve knowledge management processes; it is getting the right knowledge to people, thus enabling them in engaging with it and learning from it. However, it must go through the process of socialization as these involve social interactions to accomplish knowledge sharing activities. This social interaction consists of individual interactions and participation occurs when both important elements are involved, the knowledge sharing becomes effective. Knowledge sharing has been the most discussed topic and a challenge for organizations because of its importance in the success of knowledge management efforts. In general, knowledge sharing is related to an action which refers to people’s behavior or actions in sharing or not sharing knowledge, donating and collecting knowledge. In this paper, the discussion about knowledge sharing relates to peoples’ behavior, and this behavior must be assisted and therefore it leads to the term knowledge sharing behavior. Many researchers believed that weblogs are an innovative knowledge sharing technology. The increase use of weblogs each year has shown that weblogs are an effective social networking medium and can be extended beyond simple communication within a community. Many research studies in Malaysia are focused on knowledge sharing behavior within the organization and community. A few studies were conducted to explore knowledge sharing behavior among webloggers. However, research study that focuses on profit oriented webloggers still has not been explored in Malaysia. Thus, this paper discusses our present research that intends to explore the role of individual factor in knowledge sharing behavior among profit oriented webloggers. The paper will highlight the problems related to knowledge sharing behavior from the perspective of profit oriented webloggers and how these problems can be addressed. In addressing these problems, a few interviews will be conducted with identified webloggers. This study will adapt knowledge sharing behavior theories such as Theory of Planned Behavior, Social Cognitive Theory, Social Capital Theory and Social Exchange Theory. This will help us to understand the individual factors that can influence knowledge sharing behavior among profit oriented webloggers in Malaysia. Keywords: knowledge sharing behavior, webloggers, individual factor
1. Introduction In June 2012, the Internet World Statistics reported that there were about 17.7 million internet users in Malaysia which accounted for almost 60.7% of the population and stood at the third place among Asian Developing Countries after South Korea 82.5% and Singapore 75%. Changes in technology and the increasing number of internet users have also encouraged the Malaysian citizen to share knowledge through weblog. Weblog is similar to book/diary online entry in reverse chronological order (newest to oldest post). Weblogs are typically free and easy to register where one can create a variety of blogs in a short time. In February 2007 there were about 10 000 weblogs in Malaysia and this number has increased dramatically to 500 000 by the year 2008 (Ahmad, 2011). As reported by Minister of Information, Communication and Culture of Malaysia, the usage of weblogs in 2010 is as high as two millions (Lim, Diaz, & Dash, 2013). The increasing number of weblogs each year has shown that weblogs can be considered as one of the effective ways of knowledge sharing and can be used as an important communication tool nowadays. For instance, many have started to create weblog with a variety of purposes, such as a diary of daily lives, discussion on political matters, current issues, entertainment (Ahmad, 2011), and also sources of income and business (Elahi and Mushtaq, 2011). Weblogs can be fully utilized as a low cost and effective marketing tools especially in developing country (Elahi and Mushtaq, 2011). Writing a blog can bring real side income, but the amount of profit depend on the originality, professionalism, the variety of target market and the update frequency of content (Chen and Chen, 2010). The growth of weblogs indirectly increase the emergence of profit oriented webloggers in Malaysia and in some way can expand online business activities. This can also indirectly help to
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Ruzleeta Zakaria et al. reduce the unemployment problem faced by the Malaysian government that is due to the growth of higher education which has produced high number of graduates (Ismail, 2011). According to Elahi and Mushtaq (2011), weblog is a medium to facilitate rapid communication of ideas, knowledge and disseminate information to the reader extensively. There has been an increase number of literature on the topic of knowledge sharing through weblogs a few years ago. Researchers believed that weblogs are an innovative knowledge sharing technology (Hsu and Lin, 2008; Hwang and Kim, 2009; Du and Wagner, 2006) but many research studies in Malaysia are more focused on knowledge‐sharing behavior in organizations and communities (Aliakbar, Md Yusoff, and Nik Mahmood, 2012; Othman and Siew, 2012; Singh, Dilnutt, and Lakomski, 2008). Reluctance to share is one of the main obstacles in implementing knowledge sharing in Malaysia (Singh et al., 2008). Sometimes, individuals are reluctant to share knowledge due to a sense of insecurity (Elahi and Mushtaq, 2011). This problem can also occur to webloggers when they fear that their ideas or entries in weblog will be stolen (Ahmad, 2011). As a result, successful knowledge sharing can be difficult to achieve when open sharing of knowledge by webloggers is limited across social network. Therefore, Malaysian citizen need to be more open in accepting weblogs as one of the latest communication tools in sharing knowledge and one of the way to generate their income. Thus, the general goal of this study is to overcome the problem of knowledge sharing behavior in weblog specifically in the context of profit oriented webloggers. In this regard, this study aims to fill this gap by exploring the individual factor in knowledge sharing behavior among profit oriented webloggers. Findings were expected to expand the field of research in knowledge sharing behavior, thus allowing weblog users especially profit oriented webloggers to have knowledge of the successful factors in managing their blog, and at the same time promoting blog usage in order to generate income and profit for other webloggers.
2. Knowledge management and knowledge sharing According to Alavi and Leidner (1999), knowledge is “a justified personal belief that increases an individual’s capacity to take effective action”. Knowledge is often regarded as an information handling problem and it deals with the creation, management and also exploitation of knowledge (Mårtensson, 2000). In order for that knowledge to create value, it must be shared. To utilize the knowledge through organization effectively and efficiently, the term of knowledge management (KM) emerged. In the past decade, managers and organization have been sharing great interest in knowledge management (Alavi and Leidner, 2001). Research in the field of KM has also grown consistently and is gaining importance in the academic and business (Lee and Chen, 2012). KM is an important business model that deals with all aspects of knowledge within the context of the organization, such as knowledge creation, knowledge transfer, and knowledge sharing, and these activities can promote learning and innovation (Debowski, 2006). Alavi and Leidner (2001) stressed that the focus of KM is based on the flow of knowledge in order to enhance learning and understanding via the provision of information and via the process of knowledge creation, sharing and distribution. Individual plays an important role in KM as indicated by Nonaka, Umemoto, and Senoo (1996) sinse new organizational knowledge is created by human interaction among individuals beginning with the process of sharing experience. This is supported by Cong and Pandya (2003) who concluded that the success of KM depends upon individual’s motivation, their willingness and ability to share knowledge and also adapt the knowledge from others. Furthermore, the analysis done by Heisig (2009) proved that human factors represented more than 90 percent of critical success factor for KM. Thus, there is a general agreement that the human factor is the root of success of KM. In this regard, this study will consider the role of individual factor in determining the success factor in knowledge sharing behavior. In general definition, knowledge sharing is related to an action which refers to individual’s behavior in sharing or not sharing knowledge, donating and collecting knowledge (Sulaiman, 2010). Knowledge sharing has been the most discussed topic and a challenge for organizations because of its importance in the success of KM efforts. Past researches have highlighted variety of factors that affect the willingness of individuals in knowledge sharing, such as incentive system, costs and benefits, and also intrinsic and extrinsic benefits (e.g., Bock, Zmud, Kim, and Lee, 2005; Kankanhalli, Tan, and Wei, 2005; Wasko and Faraj, 2005). Hsu, Ju, Yen, and Chang (2007) reasonably assumed that individuals’ behavior in sharing knowledge will be based on the personal characteristics and environment they are in.
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3. Knowledge sharing behavior In attaining the KM process, the right knowledge must be presented to the right person and the person is willing to learn. Thus, it must go through the process of socialization and involve social interactions in order to achieve knowledge sharing behavior (Sulaiman, 2010). This social interaction consists of individual interactions and participation and when both of these important elements are involved, knowledge sharing turn out to be more effective. For instance, Bock et al. (2005) stated that “the stream of knowledge across individuals and organizational boundaries, and into organizational practices relies heavily on individual employees’ knowledge sharing behavior”. In online social network, knowledge sharing behavior cannot be forced but can only be encouraged and facilitated because it concerns the willingness of individuals in online social communities to share the knowledge with others through active participation (Yu, Lu, and Liu, 2010). Hence, this study is concentrated on the discussion of knowledge sharing related to peoples’ behavior. Peoples’ behavior must be assisted and it leads to the term individual knowledge sharing behavior. Based on this study, we want to focus on individual knowledge sharing behavior through profit‐oriented webloggers.
3.1 Individual factor Individual attitude in sharing knowledge is vital because it is the first factor in human behavior (Elahi & Mushtaq, 2011). Attitude reflects the important beliefs of individual behavior. Attitude is defined as the tendency to respond favorably or unfavorably to yourself, others and the environment (Bock et al., 2005). Based on this statement, we can indicate that individual behavior and attitude is an important part in knowledge sharing process. This statement is further strengthened by several researches focused on the behavior of individuals in knowledge sharing. They have argued that individuals play a vital role in a successful knowledge sharing. Research conducted by Wasko and Faraj (2005) indicates that the contribution of knowledge by individuals occur when they believe that participation increase their professional reputation, when they have necessary expertise to share, and when they become part of the network structure. Hsu et al. (2007) argued that the biggest challenge in nurturing an individual’s knowledge sharing behavior in virtual communities is the willingness to share knowledge with others. They concluded that self efficacy and trust play important roles in guiding individuals’ behavior and personal outcome expectations have significant influence in knowledge sharing behavior. Liang, Liu, and Wu (2008) went further by investigating the contribution of individual cognition (perceived benefit and organizational commitment), interpersonal interaction (social interaction and trust), and organizational effort (organizational support and reward system) towards individual knowledge sharing behavior. They also used information technology support and organizational type as moderators. Their study confirmed the role of Social Exchange Theory in interpreting knowledge sharing behavior. The result showed that all construct except organizational support have significant effect on individuals’ knowledge sharing behavior. Furthermore, the research on the factor of internet self efficacy affecting the individuals’ knowledge sharing behavior based on Theory of Planned Behavior has been conducted by Teh, Ho, Yong, and Yew (2010). They concluded that expertise or capability to utilize an internet application help to increase confidence levels of an individual and this factor can motivate individuals’ knowledge sharing with others. Papadopoulos, Stamati, and Nopparuch (2012) focused on factors that influence the intention to share knowledge through employee weblogs in Thai organizations. They proposed model based on individual factor and focused on social influence, technology acceptance, and Social Cognitive Theory. Finding showed that self efficacy and attitude towards knowledge sharing influenced the intention to share knowledge, while perceived enjoyment and personal outcome expectation is significant to attitude toward knowledge sharing. Othman and Siew (2012) examined the individual, organizational and technology factor that affect the intention to share knowledge by using blog in Malaysian organization. The results show that individual participation in organization blog is produced from positive outcome expectation and also their enjoyment in helping each other. An interesting finding of this study is that individual factor makes the strongest contribution to knowledge sharing through blogs.
4. Preliminary investigation and theoretical foundation This study highlights the problems associated with knowledge sharing behavior from the perspective of profit‐ oriented webloggers. There are some theories that can help the authors in determining the individual factors
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Ruzleeta Zakaria et al. that can play an important role in performing knowledge sharing behavior specifically among profit‐oriented webloggers in Malaysia. Hence, this study propose four theories; Social Exchange, Social Capital, Social Cognitive and Theory of Planned Behavior. These theory have been initiated in previous research (e.g., Sulaiman, 2010) to explain the individual factors affecting knowledge sharing behavior among profit oriented webloggers.
4.1 Initial findings An interview between two profit‐oriented webloggers were conducted as a first step in identifying the role of individuals for sharing knowledge in the weblogs. Semi structured interviews with open‐ended questions were used to cover the research idea and allowed the respondents to elaborate on their real life encounters, which gives us a basic understanding on the issue at present. Based on the conducted interviews, the authors summarize the problems faced by them as three major problems (1) How to enhance blog recognition?; (2) How to draw the attention of followers to purchase product? and (3) How to maintain followers loyalty in buying product?. Both webloggers begin to create blog on the basis of interest to share their stories and life experiences. After realizing weblogs can generate income, they began to sell products. They believed that by regularly updating their post in weblogs, they can maintain strong relationship with readers/followers and other bloggers. Most of the followers on the blog are close contacts and the increase of new followers will occur when they are interested in buying the product, sharing the same ideas/interests, and also exchanging opinion with other profit oriented webloggers (e.g., Jo and Sanders, 2006). Indirectly, more people will be aware of the blog existence. At the same, time they also enjoy helping other bloggers to promote their products, this can be referred to the term of ‘altruism’ (e.g.,Hung, Durcikova, Lai, and Lin, 2011) and have mutual agreement on exchanging promotion of their product in the blog. Kankanhalli et al. (2005) stated that altruism is derived from the intrinsic enjoyment of helping each other. As described in Social Exchange Theory that an individual’s exchange behavior depends on the reciprocal and equivalent rewards gained in return (Lin and Lin, 2006). Both webloggers also agreed that this mutual understanding between profit‐oriented webloggers can attract new followers, many people will recognize their blog, and it can also ensure an ongoing sharing with other profit‐ oriented webloggers as related to the term of outcome expectation in Social Cognitive Theory (Hsu et al., 2007; Chiu, Hsu, and Wang, 2006). Profit‐oriented webbloggers have to be aware of changes in fashion as well as market demand. Therefore, they must have knowledge about the latest updates and know how to share this knowledge with their followers. This can be related to the term self efficacy which is applied in Social Cognitive Theory (Hsu et al., 2007) and also Theory of Planned Behavior (Chen, Chen, and Kinshuk, 2009). Regularly updating their post in the blogs, replying to comments, and willingness to accept ideas or comments from their followers can also attract the followers to buy products. These factors can be related to Theory of Planned Behavior which states that individual attitude towards knowledge sharing can influence individual willingness to share knowledge in social network (Chen et al., 2009). According to both webbloggers, trust is a very important factor in the success of online transactions. Profit‐ oriented webloggers have to considered mutual trust factor in order to maintain good relationship and loyalty with their followers in the future. Trust is one of the factors contributing to Social Capital Theory. Prior studies pointed out that trust among people was a critical element in fostering knowledge sharing in network communities (Chai and Kim, 2010; Hsu et al., 2007).
4.2 Social exchange theory Social Exchange Theory is normally used as a theoretical background for knowledge sharing concept (Bock et al., 2005; Liang et al., 2008; Lin and Lin, 2006). The theory supports that individuals may develop their knowledge sharing behavior based on their future expectation and when positive returns are expected. In addition, the concept of Social Exchange Theory states that member involved with social interaction expected social rewards such as approval, status as well as respect (Liang et al., 2008). Research has established extrinsic and intrinsic rewards as motivators of human behavior including knowledge sharing (Kankanhalli et al., 2005). Knowledge sharing can be considered as a type of social exchange (Bock et al., 2005) where people sharing
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Ruzleeta Zakaria et al. their knowledge and skills with their social group and reciprocally assuming to receive the knowledge of others in return. Many researches have been applied this theory as a way to investigate the personal behavior in knowledge sharing (Bock et al., 2005; Kankanhalli et al., 2005).
4.3 Social capital theory The Social Capital Theory perspective can promote knowledge sharing between social group if they share common values thus facilitating them to create mutual trust (Liu, 2011). This theory has been proved as capable in motivating individual to share their knowledge in social networks (Wasko and Faraj, 2005; Chiu, Hsu, and Wang, 2006; Hall and Wulff, 2008). According to Hung et al. (2011), this theory argues that cooperation and tacit understanding are formed over a long period of time, leading to the development of mutual trust and long‐term relationships in groups. In the context of their study, individuals will reciprocate others’ effort to share knowledge by contributing more knowledge.
4.4 Social cognitive theory Social Cognitive Theory indicates that an individual will take an action that has personal cognition in a social environment. An individual’s cognition to act in a certain way consists of two determinants: self‐efficacy and outcome expectation (Hsu et al., 2007). Self‐efficacy is a judgment made by the individual's ability to perform an action towards their performances. It is associated with the assessment made by individuals on ability and not on expertise (Bandura, 1997). This is one of the important factors influencing the decision to share knowledge (Hsu et al., 2007; Kankanhalli et al., 2005). Based on outcome expectations, if members of online communities believe that they would receive extrinsic benefits such as promotion or monetary rewards by sharing their knowledge, they would build up more positive attitude toward knowledge sharing. Furthermore, if members believe that they would receive intrinsic benefits such as social recognition, self‐satisfaction, or power, they would also have enjoyment in knowledge sharing (Hsu et al., 2007).
4.5 Theory of planned behavior Theory of Planned Behavior can be used to explain and predict individuals’ attitude. This model is accepted in social psychology and posits that individuals’ behavior is determined by perceived behavioral control and behavioral intention. Behavioral intention is determined by attitude towards behavior, subjective norm, and perceived behavioral control. Attitude towards behavior indicates individual’s positive or negative feeling about performing a behavior. Subjective norm indicates that individual perception is accepted, encouraged and implemented by others. Perceived behavioral control indicates that individual perception of the availability of resources or opportunities is necessary in performing a behavior (Ajzen, 1991). Some researchers claimed that Theory of Planned Behavior can be used as a theoretical guideline in explaining knowledge sharing intention (Bock et al., 2005) or online behavior. For example, Chen et al. (2009) provide a framework for understanding online knowledge sharing behavior in virtual learning communities using this theory. Findings showed that attitudes, subjective norm, self efficacy and social network are good predictors of knowledge sharing intention, and they are significantly associated with knowledge sharing behavior.
5. Conclusion Weblog appears to represent a form of knowledge sharing that is arising in Asian Developing Countries in the 21st century. Similarly, the increased use of weblogs has caused a phenomenon in Malaysia and indirectly increased the number of profit oriented webloggers. An initial investigation of the individual factor for profit oriented webloggers highlights certain characteristic with regard to webloggers and knowledge sharing behavior such as trust, enjoyment in helping each other (altruism), outcome expectation, self efficacy and also mutual reciprocity. For future research, these aspects must be studied more closely which can contribute to understanding the role of individual behavior especially in the context of profit oriented webloggers. It can serve as a basis for the role recognition of knowledge sharing behavior by individuals and can be support by knowledge sharing behavior theories such as Theory of Planned Behavior, Social Cognitive Theory, Social Capital Theory and Social Exchange Theory as the theoretical framework for future findings. This study will enhance the knowledge of webloggers to reach their full potential as profit oriented webloggers in order to improve their ways of life by getting the income or profit through weblogs. Understanding these issues can also contribute to the existence of long‐term sustainability of profit oriented webloggers in developing country and at the same time to overcome the unemployment problem faced by Malaysians.
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Chiu, C.‐M., Hsu, M.‐H., and Wang, E. T. G. (2006) "Understanding Knowledge Sharing in Virtual Communities: An Integration of Social Capital and Social Cognitive Theories", Decision Support Systems, Vol 42, No.3, pp 1872–1888. Cong, X., and Pandya, K. V. (2003) "Issues of Knowledge Management in the Public Sector", Electronic Journal of Knowledge Management, Vol 1, No. 2, pp 25–33. Debowski, S. (2006) Knowledge Management, John Wiley & Sons Ltd, Australia. Du, H. S., and Wagner, C. (2006) "Weblog Success: Exploring the Role of Technology", International Journal of Human‐ Computer Studies, Vol 64, No.9, pp 789–798. Elahi, A. A., and Mushtaq, R. (2011) "Probing Factors Affecting Knowledge Sharing Behavior of Pakistani Bloggers", Electronic Journal on Information System in Developing Countries, Vol 45, No.6, pp 1–14. Hall, H., and Wulff, G. W. (2008) "Social Exchange, Social Capital and Information Sharing in Online Environments: Lessons From Three Case Studies", USE, pp. 1–21. 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A Meta‐Analysis", International Conference on Information Systems. Lim, R., Diaz, J. A., and Dash, D. (2013) "Making Shops from Blogs: An Emerging Online Retailing Phenomenon in Southeast Asia", Asia Pacific Business Research Conference, pp. 1–21. Lin, C. S., and Lin, T. (2006) "Exploring Knowledge Sharing in Virtual Teams: A Social Exchange Theory Perspective", Hawaii International Conference on System Sciences, pp. 1–10. Liu, Y. C. (2011) "Applying Social Capital and SECI Model to Explore Gender Differences in Knowledge Sharing Behaviors" international Journal of Digital Society, Vol 2, No. 3, pp 512–519.
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A Knowledge Sharing System Based On Structured And Unstructured Knowledge Leandro Ramos da Silva and Nizam Omar Universidade Presbiteriana Mackenzie, S達o Paulo, Brasil lramos@gmail.com nizam.omar@gmail.com
Abstract: One of the promises of model representation indicated by the Semantic Web is its potential to enable and facilitate knowledge interchange, because if different applications use the same set of well-defined terms to describe their domain it will be much easier for them to "talk" to each other, since the domain model defines OWL features precisely. The ontology used for context has been focused mainly on sources of unstructured knowledge such as text, images, etc, probably due to the model behind the structured knowledge, which despite not being ontology itself, reveal some semantic. On the other hand, there are proposals dealing with great heterogeneity regarding the correspondence between ontology model, where the common point between the existing methods involve similarities between entities (concepts and properties) of different ontologies. Some guidelines composing a method that considers a case where the automated ontology serve as conceptual models for mediating communication between people without the need for a system. This allows even inexperienced users to collect linguistic resources available on the web to describe concepts in a domain, implementing collective intelligence. The user involvement does not decrease the efficiency of the matching concept, improves the quality of results, reducing the likelihood of unwanted or incorrect results. The objective of the research is to produce a system of ontology-based knowledge sharing between structured and unstructured knowledge. And so standardized semantics would emerging from the analysis of user interaction on these ontologies. Keywords: Knowledge, Sharing, System, Social, Sharing
1.
Contextualization
Sources of unstructured knowledge such as text are often the focus of studies on ontology, since some structured model reveals semantics, despite not being ontology itself. (Jacinto 2012) Thus, several approaches in the area of work aimed at dealing with unstructured knowledge, but only a few were dedicated to other types of sources. In this scenario, a series of studies on the editing tools of ontologies and ontology-based have been proposed for automatic processing of knowledge, for example: i) structure of collaboration between end users and ii) the reuse of ontologies. (Hwang 2009) A summary of the current scenario can be seen in Figure 1 below:
Figure 1: Current research in ontology for knowledge sharing 2.
Problem
Undoubtedly, there is no single methodology for the development of an ontology learning. Ontology Project is a creative process where two learning ontologies designed by different people, are not necessarily the same. Ontology design choices are affected by the potential applications of ontology and domain understanding and designer vision. (Kanellopoulos 2006) For example, in some forms of categorization in the ontology all concepts belonging to an ontology are disjoint sorted into categories by analyzing the taxonomy. Thus, a concept taxonomy is typically described in an ontology according to their importance, and this means taking into account the contributions of concepts in relation to all other concepts of the ontology through their relationships. (Hai 2012). This is already a reality
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Leandro Ramos da Silva and Nizam Omar through the collection of language resources available on the web to describe concepts in a domain, even for novice users who can implement collective intelligence. Thus, share semantics can also capture emerging semantic knowledge. (Hwang 2009.). In this way, emerge a hybrid approach that deals with user involvement and structural comparison. (Umer 2012)
3. Proposal The idea is to offer a methodology that considers both the case in which the ontology is automated and in which case it serves as a conceptual model to mediate and support the communication of people without the need of a system (Fernandes 2011). Thus, comparison of the structure of the concepts improves results of the corresponding concept (Umer 2012), and user involvement improves the quality of results, reducing the likelihood of undesirable or incorrect results, and does not decrease the efficiency of the matching concept. Figure 2 illustrates the proposed hereinafter described:
Figure 2: Proposal for knowledge sharing through ontology Matching concept is used to discover the relationship of concepts where the concepts are taken as words in any natural language. Techniques as Natural Language Processing (NLP) are utilized to identify the meaning of the words used for a concept, from the linguistic relationship between, for example, words synonymous. (Umer 2012) Thus, the proposal is from various sources on the same concept confronted by an ontological structure from Natural Language Processing (NLP), ontology establish a common, shared contributions from readers. As a result of this work, ontology can be downloaded in one of the following structured, open, and for the purpose of concept dissemination: ď&#x201A;§
Semantic Web, where their potential is to enable and facilitate interoperability: if different applications use the same set of well-defined terms to describe your domain, will be much easier for them to "talk" one with another. (Aquin 2012) aiHaada Explicar melhor esse diagrama
ď&#x201A;§
Features are precisely defined in OWL domain model, and the transition of the domain model in OWL for a model based on a list of resources, or functionalities (FDD) is defined directly in transformation rules. (Siddiqui, 2010)
ď&#x201A;§
A document that also acts as a router, operating by means of structured and organized social tagging with use of existing ontologies. The LD (Living Document) provides an environment where users can manage papers and related information, share their knowledge with their peers and discover hidden associations among the shared knowledge. (Castro 2010)
As a result, you can use these correspondences in various semantic integration tasks (Noy 2004), and dealing with large-scale semantic heterogeneity with ontology matching, (Hai 2012) as the semantic integration is an active area research in various disciplines, such as databases, data integration and ontologies. (Noy 2004).
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References Aquin, M., and Noy, N.F. (2012) Where to Publish and Find Ontologies? A Survey of Ontology Libraries. Media Institute (KMi), The Open University, Milton Keynes, UK. Center for Biomedical Informatics Research, Stanford University, Stanford, CA, USA Castro, A.G. and Labarga, A. (2010) Semantic Web and Social Web heading towards Living Documents in the Life Sciences Fernandes, P.C.B. and Guizzardi, R.S.S. (2011) Using Goal Modeling to Capture Competency Questions in Ontology-based Systems. Ontology and Conceptual Modeling Research Group (NEMO). Federal University of EspĂrito Santo (UFES), Brazil Hai, D.T. (2012) Ontology Integration by Propagating a Context in Priorly Matchable Concepts Hwang, D.; Lee, I.; Jung, J. (2009) Ontocs: A Web-Based System For Collaborative Ontology Construction. Yeungnam University. Dae-Dong, Gyeongsan, Korea Jacinto, C. and Antunes, C. (2012). User-driven Ontology Learning from Structured Data. Department of Computer Science and Engineering. Instituto Superior TĂŠcnico. Lisbon, Portugal Kanellopoulos, D., and Kotsiantis, S. and Pintelas, P. (2006). Ontology-Based Learning Applications: A Development Methodology Noy, N.F. (2004) Semantic Integration: A Survey Of Ontology-Based Approaches. Stanford Medical Informatics. Stanford University. 251 Campus Drive, Stanford, CA 94305. Siddiqui, F. and Alam, M. A. (2010) Ontology Based Feature Driven Development Life Cycle. IJCSI International Journal of Computer Science Issues, Vol. 9, Issue 1, No 2, January 2012. ISSN (Online): 1694-0814. www.IJCSI.org Department of Computer Science, Hamdard University, New Delhi -110025 India Umer, Q. and Mundy, D. (2012) Semantically Intelligent Semi-Automated Ontology Integration . Proceedings of the World Congress on Engineering 2012 Vol II. WCE 2012, July 4 - 6, 2012, London, U.K.
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Santarém, Portugal
The 15th European Conference on Knowledge Management Escola Superior de Gestão e Tecnologia, Instituto Politécnico de Santarém, Portugal 4-5 September 2014
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