10th International Conference on Intellectual Capital, Knowledge management ICICKM vol 2 by ACPIL

Proceedings of the 10th International Conference on Intellectual Capital, Knowledge Management and Organisational Learning The George Washington University Washington, DC, USA 24-25 October 2013

Edited by

Dr Annie Green Volume Two A conference managed by ACPI, UK www.academic-conferences.org

The Proceedings of The 10th International Conference on Intellectual Capital, Knowledge Management & Organisational Learning ICICKM-2013 The George Washington University Washington, DC, USA 24-25 October 2013 Edited by Dr Annie Green

Volume Two

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 yearâ&#x20AC;&#x2122;s proceedings can be purchased from http://academic-bookshop.com E-Book ISBN: 978-1-909507-79-1 E-Book ISSN: 2048-9811 Book version ISBN: 978-1-909507-77-7 Book Version ISSN: 2048-9803 CD Version ISBN: 978-1-909507-80-7 CD Version ISSN: 2048-982X 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 Paper Title

Author(s)

Page No.

Preface

Committee

Biographies

Volume One Knowledge Management Strategies Balanced Systems in Public Sector

Salwa Alhamoudi

The Linkages Among Intellectual Capital, Corporate Governance and Corporate Social Responsibility

Doğan Altuner, Şaban Çelik and Tuna Can Güleç

Knowledge Management in Support of Collaborative Innovation Community Capacity Building

Xiaomi An, Hepu Deng and Lemen Chao

Knowledge Management Systems for Attrition Control Activities in Private Higher Learning Institutions

Muthukkaruppan Annamalai, Kalsom Salleh and Salmiah Amin

Knowledge Management and Development of Entrepreneurial Skills Among Students in Vocational Technical Institutions in Nigeria

Stella Ify Anumnu

An Individual-Centred Model of Intellectual Capital

Teresita Arenas, Paul Griffiths and Alejandro Freraut

HEALTHQUAL International All Country Learning Network (ACLN): A Peer-Driven Knowledge Management Strategy and Community of Practice to Build Capacity for Sustainable National Quality Management Programs in Low- and Middle-Income Countries

Joshua Bardfield, Margaret Palumbo, Richard Birchard, Michelle Geis and Bruce Agins

Business Capability Modeling as a Foundation for Intellectual Capital Audits

Denise Bedford

Research Management at the Brazilian Agricultural Research Corporation (Embrapa): Development of an Information Management System

Marcelo Moreira Campos, Juarez Barbosa Tomé Júnior and Elizabeth Cristina de Oliveira Ferreira

Intellectual Capital and Its Influence on the Financial Performance of Companies in Under Developed Capital Markets – the Case of the Caribbean

Donley Carrington

Human Resource Practices and Knowledge Sharing: The Mediator Role of Culture

Delio Castaneda and Paul Toulson

Organizational Antecedents Shaping Knowledge Sharing Behaviors: Empirical Evidence From Innovative Manufacturing Sectors

Vincenzo Cavaliere and Sara Lombardi

Teaching Cases for Capturing, Capitalizing and Re-Using Knowledge: A Case Study in Senology Knowledge Sharing and Innovation: An Empirical Study in Iraqi Private Higher Education Institutions

Souad Demigha and Corinne Balleyguier

104

The Influence of ICT on the Communication of Knowledge in Academia

Natalia Dneprovskaya and Irina Koretskaya

114

The Learning Journey of IC Missionaries: A Staged Approach

John Dumay and Mary Adams

122

Knowledge Sharing and Innovation: An Empirical Study in Iraqi Private Higher Education Institutions

Sawasn Al-husseini and Ibrahim Elbeltagi

129

Big Data and Intellectual Capital: Conceptual Foundations

Scott Erickson and Helen Rothberg

139

Paper Title

Author(s)

Page No.

A Risk and Benefits Behavioral Model to Assess Intentions to Adopt Big Data

José Esteves and José Curto

147

Bridging Knowledge Management Life Cycle Theory and Practice

Max Evans and Natasha Ali

156

Intellectual Capital Disclosure in IPO Prospectuses: Evidence From Technology Companies Listed on NASDAQ

Tatiana Garanina and Alexandra Manuilova

166

Organisational Learning and Problem Solving Through Cross-firm Networking of Professionals

Mahmood Ghaznavi, Paul Toulson, Martin Perry and Keri Logan

177

Knowledge Orientation in Information Intensive Organisations: Is There a Change in Paradigm?

Paul Griffiths and Teresita Arenas

186

The Impact of HRM Practices on Knowledge Sharing Behaviour: Unexpected Results From Knowledge Intensive Firms

Salman Iqbal, Paul Toulson and David Tweed

195

Looking Further Into Externalization Phase of Organizational Learning: Questions and Some Answers

Palmira Juceviciene and Ramune Mazaliauskiene

205

Smart Development: A Conceptual Framework

Robertas Jucevicius and Laura Liugailaite – Radzvickiene

212

Big Data, Tacit Knowledge and Organizational Competitiveness

Nowshade Kabir and Elias Carayannis

220

The ADIIEA Cycle: Creating an Integrated Framework for Business Processes and Organizational Learning

John Lewis

228

Working Meetings as a Tool for Knowledge Management and Trust Building

Palmira Lopez-Fresno and Taina Savolainen

236

Knowledge Management: A Business Plan Approach

Elizandra Machado, Ana Maria Bencciveni Franzoni, Helio Aisenberg Ferenhof and Paulo Mauricio Selig

243

Relationship Between Knowledge Management and SME´s Performance in México

Gonzalo Maldonado Guzmán, Gabriela Citlalli López Torres and María del Carmen Martínez Serna

252

Knowledge Sharing and Intellectual Liabilities in a Global Perspective

Maurizio Massaro, Roland Bardy and Michael Pitts

259

Innovating Corporate Management: Introducing Environmental Aspects to Design Activities

Eunika Mercier-Laurent

267

Examining the Transfer of Academic Knowledge to Business Practitioners: Doctoral Program Graduates as Intermediaries

Madora Moshonsky, Alexander Serenko and Nick Bontis

272

The Influence of Cultural Factors on Creation of Organization’s Knowing

Vyda Mozuriuniene, Palmira Juceviciene and Kestutis Mozuriunas

282

A Structural Equation Model of Organizational Learning Based on Leadership Style in Universities

Fattah Nazem, Mona Omidi and Omalbanine Sadeghi

290

The Compilation of a Structural Model for Organizational Learning Based on Social Capital in Universities

Fattah Nazem, Omalbanine Sadeghi and Mona Omidi

298

Structural Equation Modeling of Intellectual Capital Based on Organizational Learning in Iran's General Inspections Organization

Faezeh Norozi, Fattah Nazem and Mina Mozaiini

304

The Construction of an Operational-Level Knowledge Management Framework

Jamie O’Brien

310

Facilitators, Inhibitors, and Obstacles – a Refined Categorization Regarding Barriers for Knowledge Transfer, Sharing and Flow

Dan Paulin and Mats Winroth

320

Paper Title

Author(s)

Page No.

Volume Two The Influence of Intellectual Capital on Firm Performance Among Slovak SMEs

Anna Pilková, Jana Volná, Ján Papula and Marián Holienka

329

Indicators for Assessment of Innovation Related Intellectual Capital

Agnieta Pretorius

339

Voluntary Sector Organisations: Untapped Sources of Lessons for Knowledge Management

Gillian Ragsdell

349

Proposal of Indicators for Reporting on Intellectual Capital in Universities

Yolanda Ramírez, Ángel Tejada and Agustín Baidez

355

10 Years of IC and KM Research – a Content and Citation Analysis

Vincent Ribiere and Christian Walter

367

Towards an Anthropological-Based Knowledge Management

Francis Rousseaux and Jean Petit

377

Intelligence in the Oil Patch: Knowledge Management and Competitive Intelligence Insights

Helen Rothberg and Scott Erickson

387

To Study the Relationship Between Knowledge Utilization and Learning Capability in a Team

Manasi Shukla

394

Competency-Based HRM and Lifelong Learning in Poland

Lukasz Sienkiewicz, Agnieszka ChłońDomińczak and Katarzyna Trawińska-Konador

401

Following Traces of Collective Intelligence in Social Networks: Case of Lithuania

Aelita Skarzauskiene, Birute PitrenaiteZileniene and Edgaras Leichteris

411

Relational Capital and Social Capital: One or two Fields of Research?

Kaisa Still, Jukka Huhtamäki and Martha Russell

420

The Personalised Computer Support of Knowledge Management

Stefan Svetsky, Oliver Moravcik and Jana Stefankova

429

The Moscow State University of Economics, Statistics and Informatics (MESI) on the way to Smart Education

Vladimir Tikhomirov

434

The Management of the Intellectual Capital in the Russian Industrial Networks

Elena Tkachenko and Sergey Bodrunov

440

Intellectual Capital Practices of SMEs and MNCs: A Knowledge Management Perspective

Mariza Tsakalerou and Rongbin Lee

447

Managing Knowledge and Overcoming Resistance to Change: A Case Study at Firat University

Nurhayat Varol and Serkan Varol

452

Organizational Learning Rate Dependence on National Wealth: Case Study of Business Schools

Karen Voolaid and Üllas Ehrlich

457

Ready For Open Innovation or not? An Open Innovation Readiness Assessment Model (OIRAM)

Naphunsakorn Waiyawuththanapoom, Thierry Isckia and Farhad Danesghar

465

PHD Research Papers

473

Cultural Influences on Knowledge Sharing Behaviours Through Open System Vs. Closed System Cultures: The Impact of Organisational Culture on Knowledge Sharing

Hanan Abdulla Al Mehairi

475

Knowledge Management as a Competitive Advantage of Contemporary Companies

Andrijana Bogdanovska Gjurovikj

482

The Importance of Knowledge Waste for Intellectual Capital Management and Enterprise Performance

Helio Aisenberg Ferenhof and Paulo Mauricio Selig

489

Dissemination of Professional Routines, a Case Study in the Automotive Industry

Johanna Frances, Stéphane Robin and JeanLouis Ermine

499

iii

Paper Title

Author(s)

Page No.

Cluster Analysis of the European Countries: The Europe 2020 Point of View

Adela Anca Fucec and Corina Marinescu (Pirlogea)

507

Literature Review: The Role of Intangible Resources in Improving Quality of Care in Hospitals: A Framework to Evaluate Technical and Functional Quality

Hussain Hamed and Simon De Lusignan

514

Organizational Employee Seen as Environmental Knowledge Fractal Agents as a Consequence of the Certification With ISO 14001

Ionut Viorel Herghiligiu, Luminita Mihaela Lupu, Cristina Maria Paius, Christian Robledo and Abdessamad Kobi

524

Career and Knowledge Management Practices and Occupational Self Efficacy of Elderly Employees

Chandana Jayawardena and Ales Gregar

533

Easy, Economic, Expedient – an Effective Training Evaluation Model for SMEs

Sajid Khan and Phil Ramsey

540

An Exploratory Study of Knowledge Management in the UK Local Government Planning System for Improved Efficiency and Effectiveness

Nasrullah Khilji and Stephen Roberts

551

Characterization of Knowledge Sharing Practices in a Project Based Organization

Irene Kitimbo and Kimiz Dalkir

561

Person-Organisation fit as an Organisational Learning Tool in Employee Selection

Jana Makraiova, Paul Woolliscroft, Dagmar Caganova and Milos Cambal

569

Models for Describing Knowledge Sharing Practices in the Healthcare Industry

Negar Monazam Tabrizi

576

The Systemic “Learning by Sharing” Diamond: How to Implant it Concretely in Private Organization?

Alexandru-Ionuţ Pohonţu, Camille Baulan and Costache Rusu

585

A Leadership Framework for Organizational Knowledge Sharing

Hong Quach

596

Project Context and its Effect on Individual Competencies and Project Team Performance

Mikhail Rozhkov, Benny Cheung and Eric Tsui

602

The Influence of Emotional Intelligence on Employees’ Knowledge Sharing Attitude in Organizations in Thailand

Chanthawan Sathitsemakul and Francesco Calabrese

612

Quality of Higher Education Institutions as a Factor of Students’ Decision-Making Process

Petr Svoboda and Jan Cerny

622

Business Clusters and Knowledge Management: Information Flows and Network Concepts

Mariza Tsakalerou and Stefanos Katsavounis

632

An Analysis of Mobile Applications for the Purpose of Facilitating Knowledge Management

Serkan Varol and Ryan Underdown

638

The Implications of Tacit Knowledge Utilisation Within Project Management Risk Assessment

Paul Woolliscroft, Marcin Relich, Dagmar Caganova, Milos Cambal, Jana Sujanova and Jana Makraiova

645

Non Academic Paper

653

Managing Learning Style Across Generation in Workplace

Juli Purwanti, Fadillah Rizky and Wahyudi Handriyanto

Masters Research Papers

655 665

A Critical Analysis of Intellectual Capital Reports in Banking Industry from 1994 to 2011

Linlin Cai, Eric. Tsui and Benny Cheung

667

Research on Intellectual Capital Elements Synergy in Research Organizations

Li Ya-nan, Xiao Jian-hua, Cao Liu and Zhu Linlin

674

Preface These proceedings represent the work of researchers participating in the 10th International Conference on Intellectual Capital, Knowledge Management & Organisational Learning - ICICKM 2013, which this year is being held at The George Washington University. The Conference Chair is Dr Michael Stankosky and the Programme Chair is Dr Annie Green, both from The George Washington University, Washington, DC, USA. The conference sessions are being held at the The George Washington University and the conference dinner is being held at the Smith & Wollensky restaurant in Washington. The conference will open with keynote speaker Debra Amidon, from ENTOVATION International, Ltd. Wilmington, Massachusetts, USA who will address the topic of The IC Bretton Woods: A Global Innovation Frontier. The second keynote presentation will be by Jonathan Low, from PREDICTIV, USA on the topic of Competitive Advantage in the Age of Intangibles. The ICICKM Conference constitutes a valuable platform for individuals to present their research findings, display their work in progress and discuss conceptual advances in many different branches of intellectual capital, knowledge management and organisational learning. At the same time, it provides an important opportunity for members of the KM community to come together with peers, share knowledge and exchange ideas. ICICKM has evolved and developed over the past nine years, and the range of papers accepted in this year's conference ensures an interesting two-day event. Following an initial submission of 219 abstracts that have undergone a double blind peer review process, 57 research papers, 21 PhD research papers, 2 Master's research papers, and 1 non-academic papers are published in the ICICKM 2013 Conference Proceedings, representing work from Australia, Barbados, Brazil, Canada, Chile, Colombia, Czech Republic, Denmark, Estonia, Ethiopia, Finland, France, Greece, Hong Kong, India, Indonesia, Iran, Kazakhstan, Lithuania, Macedonia, Malaysia, Mexico, New Zealand, Nigeria, Poland, Romania, Russia, Russian Federation, Saudi Arabia, Slovakia, South Africa, Spain, Sweden, Thailand, Turkey UK, United Arab Emirates and the USA. I hope that you have an enjoyable conference. Dr Annie Green Programme Chair October 2013

Conference Executive Dr Michael Stankosky, The George Washington University, USA Dr Annie Green, The George Washington University, USA Vincent Ribiere, Bangkok University, Bangkok, Thailand Kevin O’Sullivan, New York Institute of Technology, New York, USA Mark Addleson, George Mason University, USA Denise Bedford, Kent State University, USA Dr. Sebastián Díaz, West Virginia University, USA Dr. William “Bill” E. Halal, The George Washington University, USA Patrice Jackson, Lockheed Martin, USA Jim Lee, Knowledge Management, APQC, USA Dr. John Lewis, Kent State University, USA Dr. Arthur J. Murray, CEO, Applied Knowledge Sciences Inc., USA Dr. Alfredo Revilak, The George Washington University, USA Dr. Anthony J. Rhem, A.J. Rhem & Associates, Inc, USA Douglas Weidner, International Knowledge Management Institute, USA Ellen Ensel, Information Services, United States Institute of Peace Dr Anne L. Washington, George Mason University, USA Mary Adams, Trek Consulting, USA Verna Allee, President, ValueNet Works, USA Mini track chairs Mary Adams, Smarter-Companies, USA John Dumay, University of Sydney, Australia Dr G. Scott Erickson, Ithaca College, Ithaca, New York, USA Dr Helen N. Rothberg, Marist College, Poughkeepsie, NY, USA Professor Eunika Mercier-Laurent, IAE Lyon, France Camilo Augusto Sequeira, Institute of Energy of PUC-Rio, Brazil Dr Susanne Gretzinger, University of Southern Denmark, Sønderborg Dr Kalsom Salleh, Universiti Teknologi MARA, Shah Alam, Malaysia. Dr Anthony J Rhem, Knowledge Systems Institute (KSI), Illinois, USA Dr Mark Addleson, George Mason University, Virginia, USA Dr. John Lewis, Kent State University, USA Conference Committee The conference programme committee consists of key people in the intellectual capital, knowledge management and organisational learning communities; the list includes leading academics, researchers, and practitioners from around the world. The following people have confirmed their participation: Mohd Helmy Abd Wahab (Universiti Tun Hussein Onn Malaysia, Batu Pahat, Malaysia); Prof Marie-Hélène Abel (Compiegne University of Technology, France); PROF. PHD. Maria-Madela Abrudan (University of Oradea, faculty of Economics, Romania); Dr. Bulent Acma (Anadolu University, Turkey); Mary Adams (Trek Consulting, USA); Mark Addleson (George Mason University, USA); Faisal Ahmed (FORE School of Management, New Delhi, India); Prof. Dr. Dr. Ruth Alas (Estonian Business School, Tallin, Estonia); Dr Joao Pedro Albino (UNESP, , Brazil); Mulhim Al-Doori (American University in Dubai, United Arab Emirates); Tahseen Al-Doori (American University in Dubai, United Arab Emirates); Dr Alex Alexandropoulos (American University in Dubai, United Arab Emirates); Verna Allee (ValueNet Works, USA); Prof. Mohammed Allehaibi (Umm Alqura University, Makkah, Saudi Arabia); Dr Luis Alvarado (Universidad Catolica del Norte , Chile); Dr/Prof Xiaomi An (Renmin University of China, China); Dr. Gil Ariely (School of Government, Interdisciplinary Center Herzliya, Israel);Dr Fátima Armas (CISUC, Coimbra University , Portugal); Dr Yousif Asfour (Injazat Data Systems, Abu Dhabi, United Arab Emirates); Derek Asoh (Ministry of Government Services, Ontario, Canada); Bijan Azad (AUB school of Business, Lebanon); Mahjabin Banu (Jayoti Vidyapeeth Women's University, India);Professor Michael Banutu-Gomez (Rowan University, USA); Dr. Neeta Baporikar (Sultanate of Oman Ministry of Higher Education, Oman); Dr Bob Barrett(American Public University, USA,); Dr. Tomas Gabriel Bas (Pontificia Universidad Catolica de Chile, Chile); Dr Belghis Bavarsad (Shahid chamran University, Ahvaz, Iran); Abdullah Bayat (University of the Western Cape, Bellville, South Africa); Prof. Dr. Aurilla Bechina Arnzten (Hedmark University College, Norway);DR Denise Bedford (Kent state University, USA); Esra Bektas (TU Delft, The Netherlands); Diana Belohlavek (The Unicist Research Institute, Argentina); Dr David Benmahdi (Laboratoire Paragraphe EA349, Paris, France); Prof. Galiya Berdykulova (International IT university, Kazakhstan); Prof Constantin Bratianu(Academy of Economic Studies, Bucharest, Romania, Romania); Jean Pierre Briffaut (UTT, Université de Technologie de Troyes, Troyes, France); Sheryl Buckley (Unisa, South Africa); vii

Dr Acma Bulent (Anadolu University, Eskisehir, Turkey); Dr Francesco Calabrese (Institute for Knowledge and Innovation (GWU) - USA, USA); Dr. Delio Castaneda (Pontificia Universidad Javeriana, Colombia,); Saban Celik (Yasar University, Turkey); Eric Chan (Knowledge Management Development Centre, Hong Kong , Hong Kong); Fernando Chaparro (Universidad del Rosario, Bogotá, Colombia); Prof David Chapinski (Rutgers, The State University of New Jersey: Newark, United States) Daniele Chauvel (SKEMA Business School , France); Prof Phaik Kin Cheah (Universiti Tunku Abdul Rahman, Malaysia); Dr Benny Cheung (The Hong Kong Polytechnic University, Hong Kong); Dr. Vikas Choudhary (National Institute of Technology,Kurukshetra, India); Rashid Chowdhury (Independent University, Bangladesh, Chittagong, Bangladesh); Dr Reet Cronk (Harding University, USA); Prof. Marina Dabic(University of Zagreb, Croatia); Professor Pablo da Silveira (Catholic University of Uruguay, Uruguay); Raymond D'Amore (The Mitre Corporation, McLean, VA , USA); Geoffrey Darnton (Bournemouth University, UK); Dr. Kandy Dayaram (Curtin University of Technology, Perth, Australia); John Deary (Independent Consultant, UK & Italy); Prof Paola Demartini (University of Rome 3, Faculty of Economics, department of Management and Law, Italy); Dr Michael D'Eredita(Syracuse University, New York, USA); Dr Charles Despres (Skema Business School, Sophia-Antipolis, Nice , France); Dr Sebastián Díaz (Learning & Culture at West Virginia University, USA); Dr John Dumay (University of Sydney, Australia); Dr Neeraj Dwivedi (Indian Institute of Management Lucknow, India); Jamal El Den (Charles Darwin University, Australia); Ellen Ensel (United States Institute of Peace, USA); Dr. Scott Erickson (Ithaca College, USA); Jean-Louis Ermine(Institut National des telecommunications, Evry, France); Geoff Erwin (Independent Consultant, South Africa); Mercy Escalante (Sao Paulo University, Brazil);Dr. Ibrahim Fahmi (Glasgow Caledonian University, UK); Nima Fallah (University of Strasbourg, France); Tony Feghali (AUB school of Business, Lebanon);Prof Liliana Feleaga (Academy of Economic Studies, Romania,); Dr Silvia Florea (Lucian Blaga University, Romania,); Dr Ines Friss de Kereki (ORT Uruguay University, Montevideo, Uruguay); Stan Garfield (Global Consulting Knowledge Management Group, USA); Dr Liza M. Gernal (American Collegeof Dubai, United Arab Emirates); Dr. Nasim Ghanbartehrani (IMI, Iran,); Dr John Girard (Minot State University, , USA); Dr Marco Giuliani (University of The Marche, Ancona, Italy); Prof. Dr. Adriana Giurgiu (University of Oradea, Faculty of Economic Sciences, Romania); Dr Andrew Goh (International Management Journals, Singapore); Gerald Goh (Multimedia University, Melaka, Malaysia); Dr. Sayed Mahdi Golestan Hashemi (Faculty of Industrial Engineering - MA University & center for Creatology & triz & innovation Manage, Iran); Farshid Golzadeh Kermani (University of Sheffield, UK); Liliana Gomez (Universidad del Rosario, Bogotá, Colombia); Ken Grant (Ryerson University, Toronto, Canada); Dr Annie Green (The George Washington University, Washington, DC, USA); Prof. Dr. Susanne Gretzinger (Department for Border Region Studies, Denmark); Paul Griffiths (Director, IBM, Santiago, Chile); Prof Veronica Grosu (Stefan Cel Mare University Of Suceava,Romania); Michel Grundstein (Lamsade Paris Dauphine University, France); Dr Daniel Guevara (KM-IC Research, Mexico); Giora Hadar (Federal Aviation Administration, USA); Dr Anne Hakansson (Uppsala University, Sweden); Dr William Halal (George Washington University, USA); Dr Leila Halawi (American University in Dubai, United Arab Emirates); Igor Hawryszkiewycz (University of Technology, Sydney, Australia); Dr Ilona Heldal(University of Skovde, Sweden); Liaquat Hossain (Syracuse University, USA); Dr. Yassaman Imani (University of Hertfordshire, UK); Patrice Jackson(Lockheed Martin, UK); Prof. Brigita Janiunaite (Kaunas University of Technology, Lithuania); Dawn Jutla (University of Halifax, Canada); Prof Konstantinos Kalemis (National Centre of Local Goverment and Administration, Greece); Dr Amrizah Kamaluddin (Universiti Teknologi MARA, Malaysia); Dr SilvaKarkoulian (Lebanese American University Beirut Campus, Lebanon); Dr. Jalil Khavandkar (Zanjan Science & Technology Park, Iran); Dr Prof Aino Kianto(Lappeenranta University of Technology, Finland); Hans-Peter Knudsen (Universidad del Rosario, Bogotá, Colombia); Dr Andrew Kok (University of Johannesburg, South Africa); Eric Kong (University of Southern Queensland, Australia); Prof. Dr. Richard Lackes (Institute of Business Informatics, TU Dortmund, Germany ); Jim Lee (APQC, USA); Prof. Rongbin W.B. Lee (The Hong kong polytechnic university, Hong Kong); Rene Leveaux (University of Technology, Sydney, Australia); Dr John Lewis (Kent State University, USA); Dr Antti Lönnqvist (Tampere University of Technology, Finland); Professor Ilidio Lopes ( Polytechnic Institute of Santarém; University of Coimbra, Portugal, Portugal); Dr. Palmira Lopez-Fresno (Unniversity of East of Finland, Finland); Dr Fergal McGrath (University of Limerick, Ireland); Prof Eunika Mercier-Laurent (University Jean Moulin Lyon, France,); Kostas Metaxiotis (National Technical University Athens, Greece); Dr Marina Meza (Universidad Simón Bolívar, Venezuela,); Dr Ian Michael (Zayed University, Dubai, United Arab Emirates);Associate Prof. Ludmila Ml dkov (University of Econimics Prague, Czech Republic); Dr Sandra Moffett (University of Ulster, UK); Dr Kavoos Mohannak (Queensland University of Technology, Australia); Muhammad Izwan Mohd Badrillah (UITM, Malaysia); Dr. Alunica Morariu, (“Stefan cel Mare" University of Suceava, Faculty of Economics and Public Administration, Romania); Maria Cristina Morariu (The Academy of Economic Studies, Romania); Elaine Mosconi (Université Laval, Quebec, Canada); Dr Claudia Mueller (Innsbruck University School of Management, Austria); Hafizi Muhamad Ali (Yanbu University College, Saudi Arabia); Aroop Mukherjee (King Saud University, Saudi Arabia); Dr Arthur Murray (Applied Knowledge Sciences, Inc., USA); Maria Mylopoulos (University of Toronto, Canada); Prof. Nader Nada (College of Computing, AAST, Egypt); Dr Atulya Nagar (Liverpool Hope University, UK); Tasawar Nawaz (Kozminski University, Poland); Dr Artie Ng (The Hong Kong Polytechnic University, Hong Kong); Prof Emanuela Alisa Nica (Petre Andrei University from Iasi, Romania); Dr Chetsada Noknoi (Thaksin University, Songkhla, Thailand); Dr. Kevin O’Sullivan (New York Institute of Technology, USA); Reese Olger (USMC, USA); Dr Abdelnaser Omran (School of Housing, Building and Planning, Universiti Sains Malaysia, Malaysia); Professor Ibrahim Osman (American University of Beirut, Lebanon); Kevin O'Sullivan (School of Management, USA); Dr. Jayanth Paraki (Omega Associates, Bangalore, India); Prof Robert Parent (Université de Sherbrooke, Quebec, Canada); Dr Shaun Pather (Cape Peninsula University of Technology, , South Africa); Dan Paulin (Chalmers University of Technology, Göteborg, Sweden); Dr Parag Pendharkar (Pennsylvania State University at Harrisburg, USA); Pramuk Perera (Aviareps FZ LLC , Dubai, UAE); Milly Perry(The Open University of Israel, Isviii

rael); Dr. Monika Petraite (Kaunas University of Technology, Lithuania ); Dr Prapon Phasukyud (The Knowledge Management Institute (KMI) - Thailand, Thailand); Rajiv Phougat (IBM Corporation, USA); Dr. V. Nguyen Phuc (Asian Institute of Technology and Management, Vietnam,); Dr John Politis (Neapolis University, Pafos, Cyprus); Dr Siwarit Pongsakornrungsilp (Walailak University, Thailand); Dr Agnieta Pretorius (Tshwane University of Technology, Witbank, South Africa); Dr. Devendra Punia (Wipro Consulting Services, New Delhi, India); Dr Mohamed Rabhi (Saudi Basic Industries Corporation (SABIC), Saudi Arabia); Dr Bilba Radu (George Bacovia University, Romania); Dr Gillian Ragsdell (Information Science, Loughborough University, UK); Azlina Rahim (Universiti Teknologi MARA, Malaysia,); Dr Lila Rajabion (Penn State University, Mont Alto , USA); Prof Subashini Rajagopal (VIT University, India);Dr Siriwan Ratanakarn (Bangkok University, Thailand, Thailand); Dr Alfredo Revilak (George Washington University Institute for Knowledge and Innovation (IKI), USA); Dr Anthony Rhem (A.J. Rhem & Associates, Inc, USA); Dr Vincent Ribière (The Institute for Knowledge and Innovation Southeast Asia (IKI-SEA) of Bangkok University, Bangkok, , Thailand); Waltraut Ritter (Asia Pacific Intellectual Capital Centre, Hong Kong ); Eduardo Rodriguez (IQ Analytics, Ottawa, Canada); Prof Goran Roos (Cranfield University, UK); Mustafa Sagsan (Near East University, Nicosia, Northern Cyprus, Cyprus); Randa Salamoun Sioufi(American University of Beirut, Lebanon, Lebanon); Dr Kalsom Salleh (University Technology MARA, Malaysia); Dr. Antonio Sandu (Mihail Kogalniceanu University, Romania); Assoc.Prof.Dr. Tulen Saner (Near East University, North Cyprus); Prof. Taina Savolainen (Universiy of Eastern Finland, Dpt. of Business, Finland); Professor Giovanni Schiuma (Universita dela Basilicata, Matera, Italy); Camilo Augusto Sequeira (Catholic University, Rio De Janeiro, Brazil); Dr. Enric Serradell-Lopez (Open University of Catalonia, Barcelona, Spain); Amanuddin Shamsuddin (Universiti Tenaga Nasional, Malaysia); Dr Mehdi Shariatmadari (Islamic Azad University, Central Tehran Branch, Iran,); Dr Michael Shoukat (UMUC, USA); Dr Sharad Sinha (R.B.S. College of Education, Rewari, India); Guy St.Clair (SMR Intel, USA); Dr Michael Stankosky (The George Washington University, Washington, DC, USA); Michael Stelzer (Knowledge Management Services & Associates, USA); PhD. Jukka Surakka (Arcada-University of Applied Science, Helsinki, Finland); Dr Marzena Swigon (University of Warmia and Mazury, Poland); Dr Cheng Ling Tan (Universiti Sains Malaysia, Malaysia); Nya Ling Christine Tan (Multimedia University, Malaysia); Paul Toulson (Massey University , New Zealand); Ana Treviño (ITESM, Mexico); Dr Nachiketa Tripathi (Indian Institute of Technology Guwahati, India); Dr Edward Truch (Lancaster University Management School, UK); Prof Eric Tsui (Knowledge Management Research Centre ,The Hong Kong Polytechnic University, Hong Kong); Dr Geoff Turner (University of Nicosia, Cyprus); Ass.Prof.Dr. Lucian Unita (University of Oradea, Faculty of Medicine and Pharmacy, Romania,); Mathias Uslar (OFFIS, Oldenburg, Germany); Dr Herman van Niekerk (Suritec Pty Ltd, Cape Town, South Africa); Prof Asaf Varol (Firat Univeristy, Elazig, Turkey); Nurhayat Varol (Firat University, Turkey); Francisco Vasquez (Universidad Jorge Tadeo Lozano, Bogotá, Colombia); Jeannette Vélez (Universidad del Rosario, Bogotá, , Colombia); Professor Jose Maria Viedma (Polytechnic University of Catalonia, Spain); Dr Anne L Washington (George Mason University, USA); Douglas Weidner (International Knowledge Management Institute, USA); Dr Ismail Wekke (State College of Sorong, West Papua); LaNae Wheeler ( Johnson Controls, UK); Tanakorn Wichaiwong (Kasetsart University, Thailand); Dr Roy Williams (University of Portsmouth, UK); Dr Tiparatana Wongcharoen (Bangkok University, Thailand, Thailand); Dr Lugkana Worasinchai (The Institute for Knowledge and Innovation Southeast Asia (IKI-SEA) of Bangkok University, Bangkok , Thailand); Dr Les Worrall (University of Coventry, UK); Dr An Xiaomi (Renmin University of China, China); Dr Mohammad Hossein Yarmohammadian (Health Management and Economic Research Center, Isfahan University of Medical Sciences, Iran); Dr. Pitipong Yodmongkon (College of Arts Media and Technology, Chiang Mai University, Thailand); Aw Yoke Cheng (The Asia Pacific University of Technology and Innovation(A.P.U) UNITAR International University, Malaysia); Dr Malgorzata Zieba (Gdansk University of Technology,Poland); Philip Zgheib (American University of Beirut , Lebanon); Prof. Qinglong Zhan (Tianjin University of Technology and Education, China) Dr Suzanne Zyngier (Latrobe University, France)

Biographies Conference Chair Dr Michael Stankosky obtained his doctorate from George Washington University (GW) by researching organizational effectiveness. His subsequent research focuses on how to engineer and manage a global enterprise in a knowledge-based economy. He created the theoretical constructs required for the master’s and doctorate in knowledge management (KM) – a first in academia. He is Editor Emeritus of VINE: The Journal of Information and Knowledge Management Systems - part of Emerald Group Ltd.

Programme Chair Dr Annie Green is a Knowledge Strategist/Architect and has led several KM initiatives. Her initial research efforts were focused on intangible asset valuation. Her subsequent research efforts are focused on the development of two methodologies: 1) PLANT (Plan, Layout, Actualize, Nourish, Transition) a performance based Knowledge Management methodology, and 2) BRAIN (Business Reasoning, Analytics and Intelligence Network) an intangible asset valuation methodology and measurement tool.

Keynote Speakers Debra Amidon is a global innovation strategist and founder of ENTOVATION International Ltd, and is considered an architect of the Knowledge Economy demonstrating how theories can be applied for practical results. An international author and thought leader, she has published 8 books in foreign translations, including a trilogy on Knowledge Economics and The Innovation Superhighway – acclaimed as the “Innovation Book of the Decade”. She has 45 years of experience in academic administration, serving as Assistant Secretary of Education for the Commonwealth of Massachusetts, Dean at Babson College and as a corporate executive in the Office of the President. Debra has delivered hundreds of articles and keynote presentations in 38 countries on 6 continents. Her seminal research has focused on intellectual capital, stakeholder innovation, knowledge innovation zones and collaborative advantage. She advised the first student entrepreneur association [1972], established the first corporate office of technology transfer [1982]; and last year hosted the World Summit on Innovation and Entrepreneurship in Boston [WSIE 2012]. With a Network of 200+ across 68 countries, her clients include Fortune 50 companies, government agencies and enterprises such as the EU, OECD, IADB, Confederation of Indian Industries, Arab Knowledge Economy Association, UN and The World Bank. She has received several honors including Outstanding Young Professional of New England, Pi Lambda Theta Scholar, the Sigma Kappa Colby Award, selected for the Festival of Thinkers, and IC2 Institute Global Fellow for the University of Texas at Austin. She’s taught courses at IPADE, Tartu University, the Banff Center and Tilburg University. Debra holds degrees from Boston University, Columbia University and the MIT where she was an Alfred P. Sloan Fellow. Jonathan Low is a Partner and Co-founder of Predictiv Consulting and PredictivA sia. Predictiv assists corporations, government agencies, family-owned businesses and not-for-profits improve management performance, organizational effectiveness, marketing and strategy. Predictiv has particular expertise in evaluating the impact on financial results of factors such as strategy execution, reputation, organizational effectiveness, brand, innovation and post-merger integration. Clients have included Southwest Air, Pfizer, Major League Baseball, Petrobras, General Motors, UPS, United Technologies, Trump Holdings, the US Joint Chiefs of Staff, Novartis and Visa. Jon has served in a number of positions related to his work such as Co-Chair for Strategic Organizational Issues of The Brookings Institution’s Task Force on Intangible Sources of Value. He has presented his findings to the US Securities and Exchange Commission, the Financial Accounting Standards Board, the European Commission, Chinese Ministry of Technology and the New York Federal Reserve Bank. His work has appeared in Forbes, the Wall Street Journal, Harvard Management Update, New York Times and Business Week. Jon has appeared on ABC, CNN, CNBC, PBS and other electronic media. He was co-editor of Enterprise Value in the Knowledge Economy, a joint publication of the OECD and Ernst & Young in 1997. He co-authored the book Invisible Advantage, published by Perseus Press in 2002. He has contributed chapters to Business Power; Creating New Wealth from IP Assets (Wiley, 2007) and From Assets to Profits (Wiley 2009). He blogs for The Low-Down. Jon currently serves on the Board of the Center for International Understanding at Dartmouth College whose Nominating Committee he chairs; is a Director of the Athena Alliance, a Washington, DC-based policy research organization; Chairman of the Board of Classical South Florida, an NPR affiliate radio station; a Director of IPTI, a research and innovation NGO in Brazil; a Faculty member of the Reputation Institute Management Training Program; an Executive Committee member of the Palm Beach County Ethics Initiative and a member of the Advisory Committee to the Baccalaureate Degree Program at Palm Beach State College. He is a graduate of Dartmouth College and Yale University’s School of Management. x

Mini Track Chairs Mary Adams is the co-author of Intangible Capital: Putting Knowledge to Work in the 21st Century Organization and the founder of Trek Consulting, a firm that helps private companies improve their performance and value. Mary is also the author of the Smarter Companies blog and creator of the IC Knowledge Center, a global community of 350+ IC thought leaders. Prior to starting Trek in 1999, she spent fifteen years as a high-risk lender at Citicorp and Sanwa Business Credit. Dr Mark Addleson is on the faculty at George Mason University, Virginia, USA. He has taught the knowledge management course in the OD and KM Masters program for nearly 20 years and consults in the area of organizational change. His book, Beyond Management: Taking Charge at Work, about organizing knowledge-work, was published by Palgrave in 2011. Dr John Dumay is a Senior Lecturer at the University of Sydney Business School and a leading international scholar and academic author on the topic of intellectual capital. His research questions and critiques IC theory by focussing on understanding the impact of IC in practice and whether it “makes a difference Dr Scott Erickson is Professor in the Marketing/Law Department in the School of Business at Ithaca College, Ithaca, NY. He holds a PhD from Lehigh University and Masters degrees from Thunderbird and SMU. He served as Fulbright Visiting Chair at The Monieson Centre for the Study of Knowledge-Based Enterprise at Queen’sSchool of Business, Kingston, ON in 2010/2011. He has published widely on intellectual property, intellectual capital, and competitive intelligence. Dr Susanne Gretzinger is Associate Professor (PhD) at the University of Southern Denmark, Sønderborg. Her research interest is in the areas of: Social Capital, Innovation-Management, Cooperative Networks, Value Adding Webs. Susanne Gretzinger is teaching Strategic Marketing Management, Business Marketing and Consumer Behaviour. Susanne Gretzinger took her PhD from University of Paderborn (Germany) and was studying one Semester as PhD student at the IllinoisState University at Urbana/Champaign (USA). She was appointed as Marketing Manager at the BWIBau GmbH, Düsseldorf, Germany before she was appointed to the University of Southern Denmark. Dr John Lewis is an accomplished leader, author, and consultant in Knowledge Management, Strategic Management, and Performance Improvement, within multiple industries, education, and the government. He frequently presents at conferences, and has been a Masters Series speaker at ISPI and a Thought Leader speaker at CSTD. He holds a Doctoral degree in Educational Psychology from the University of Southern California, with a dissertation focus on mental models and decision making. John teaches Organizational Learning as a Knowledge Management Faculty Associate at Kent State University, and is the founder and president of Explanation Age LLC, a management consulting company that focuses on knowledge management and strategic planning. John is a proven leader with business results, and was acknowledged by Gartner with a “Best Practice” paper for a knowledge management implementation. Professor Eunika Mercier-Laurent is Global Innovation Strategist and President of Innovation 3D, researcher with IAE Lyon and professor of "knowledge innovation". Her previous positions include: research in computer architecture at INRIA, computer designer, artificial intelligence methods and tools and innovative applications with Groupe Bull. She holds degrees from Politechnika Warszawska (electronic engineer), PhD in Computer Science (Paris Diderot University) and HDR (University Jean Moulin, Lyon). She's author of over 80 publications, her last book: Innovation Ecosystems was published by Wiley2011. She is member of Institut F.R. Bull, multidisciplinary group working on influence of IT on various fields, of New Club of Paris, on board of French Association for Artificial Intelligence, expert for ANR and European Commission and Chairman of IFIP group devoted to Knowledge Management. Dr Anthony J Rhem, PhD. is an Information Systems professional with thirty (30) years of experience and currently serves as the CEO/Chief Scientist of Tacit Ware, Inc., a Knowledge Management Software company located in Chicago, Illinois. As a Knowledge Management (KM) consultant and software engineer Dr. Rhem has worked with fortune 500 corporations in retail, communications, financial, Insurance and the military in implementing KM programs, policies and KM software solutions. Dr. Rhem serves on the Board of Trustees at the Knowledge Systems Institute (KSI), where he also teaches and heads the Rexi

search Department within KSI’s Computer Science Masters program. Dr Helen Rothberg is Professor of Strategy in the School of Management at Marist College, Poughkeepsie, NY. She holds a PhD and MPhil from City University GraduateCenter, and an MBA from Baruch College, CUNY. She is on the faculty of the Academy of Competitive Intelligence and principal of HNR Associates. She has published extensively on topics including competitive intelligence and knowledge management. Helen & Scott’s latest book is Intelligence in Action: Strategically Managing Knowledge Assets, published by Palgrave Macmillan in 2012. Dr Kalsom Salleh is Associate Professor (PhD) is a senior lecturer at the Faculty of Accountancy, Universiti Teknologi MARA, Shah Alam, Malaysia. Her research areas of interest include Knowledge Management, Intellectual Capital, Accounting and Auditing. She has published many of her research papers in international refereed journals, conference proceedings and book chapters as well as sitting on the editorial board and reviewing committee members of several journals. Camilo Augusto Sequeira has a Master’s degree in Electronic Engineering from Catholic University, Rio de Janeiro, and has taught in both undergraduate and graduate programs. He has an MBA from Salford University, England. Sequeira has been top executive for multinational companies and international lecturer. He is currently a consultant and a researcher for the Institute of Energy of PUC-Rio.

Biographies of Presenting Authors Natasha Ali BA, MA is a PhD student at the Faculty of Information at the University of Toronto. Her research interests include organizational behavior, information-seeking and the strategic management of information and knowledge. Salwa Alhamoudi is an Assistant Professor and High Level Programs Coordinatora in Institute of Public Administration in Saudi Arabia. Salwa is serving as a Lecturer and Consultant specializing in Public Administration, Strategic Management, knowledge management, Balance Scorecard and Electronic Governments. She got her Ph.D from University of Portsmouth, UK. She had Msc double major in Public Administration and Research Methods. Sawasn Al-Husseini Is currently a PhD candidate at Plymouth University School of Management in the UK and a lecturer at Foundation Technical Education, Baghdad, Iraq. Shehas published five journal papers in innovation, leadership style, organizational loyalty, knowledge management, and sharing in Iraq. Recently she published three papers on knowledge sharing in proceedings of the 10th European conference on E-learning in the UK, leadership and knowledge sharing in proceedings of the 4th and 5th European conference in intellectual capital in Finland and Spain. Hanan Abdulla Al Mehairi is currently doing her PhD at Wollongong University in Dubai. She acquired a Distinction with honours Bachelor degree in applied sciences and in applied media communications from Dubai Women's College in June 2008. Hanan earned her Master's in strategic human resources management at Wollongong University in Dubai. Mrs Al Mehairi has participated in many academic conferences in different parts of the globe as she is progressing in her PhD such as Chicago, Greece, Turkey, and Las Vegas. Doğan Altuner is a professor of finance who has been working as a head of department of international trade and finance in Yaşar University. Previously, he worked as a tenured professor in East Carolina University and head of financial analyst in NATO. His primary research interests are corporate finance, behavioral finance, investment analysis. Xiaomi An is a professor of records and knowledge management at School of Information Resources Management, Renmin University of China (RUC). She is leader of Knowledge Management Team at Key Laboratory of Data Engineering and Knowledge Engineering, Ministry of Education at RUC. She is Fulbright Research Scholar of University of California, Los Angeles, at California. Stella Anumnu is a lecturer in the Department of Educational Foundations, Federal College of Education (Technical) Akoka, Lagos, Nigeria. She teaches Educational Management and Research. Her areas of interest are Human Resource management,

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Entrepreneurship and Gender studies. She has published articles in professional journals and authored books. She belongs to various professional associations. Teresita Arenas Is a Professor at the Department of Industry, at the Technical University Federico Santa Maria (UTFSM), Finance and Accounting courses. She has had senior positions in the academic administration of the University, as Head of Career and Academic Director of Campus Santiago. Her research has focused on knowledge management and intellectual capital. has developed some studies in regions, particularly in the V Region of Valparaiso, along with other authors wrote the book "Towards a new concept of Cluster "and has participated in several international conferences related to the subject. Joshua Bardfield, MPH has over a decade of public health communications, research and writing experience. He currently leads communications and knowledge management strategies for HEALTHQUAL, a capacity building initiative to build sustainable national and local quality management programs to improve population health in low- and middle-incomes countries. Denise Bedford is currently the Goodyear Professor of Knowledge Management at the College of Communication and Information, Kent State University. She is currently engaged in expanding the M.A. program and the future Ph.D. program in Knowledge Management, as well as outreach and support to the national and international knowledge management communities. Kiranmai Bhamidi is a Masters student in the Department of Management Studies, IIT Madras. She received her Bachelorâ&#x20AC;&#x2122;s from Osmania University, Hyderabad in Textile Engineering. Her other research interests include Strategic Management, Technology and Innovation management and Organizational Behaviour. Andrijana Bogdanovska Djurovic is a Sheffield MBA graduate. Andrijana is a Researcher in the area of Strategic Management. Mrs. Djurovic has more than 5 years of work experience in managing and administering international donor projects, 3 years in lecturing at a University level and 3 years in leading research projects. Donley Carrington Lecturer in Accounting and Coordinator MSc. Investment and Wealth Management programme at the University of West Indies (UWI), Cavehill Campus, Barbados. He is a Graduate of UWI, Iowa State University, USA, Institute of Management Accountants USA and University of Hull, UK. Has published articles on Intellectual Capital in the Caribbean and is co-author of two books on cost accounting. Linlin Cai graduated from Jilin University in July of 2011. Her major is archive science. When she was a junior, she got a good opportunity to go to Taiwan as an exchange student. This trip has greatly broadened her horizons. Now she is a master of Philosophy of HK PolyU. Her areas of interest are intellectual capital. Delio I Castaneda is an Associate Professor of HRM and KM, School of Management, Pontificia Universidad Javeriana, Colombia. Vincenzo Francesco Cavaliere is an Associate Professor of Business Organization at Department of Business Administration, University of Florence. His research interests include entrepreneurship and organization learning in SMEs, knowledge sharing and strategic human resource management. He is member of AIDEA (Accademia Italiana di Economia Aziendale). He served as a referee for The International Journal of Knowledge, Culture & Change Management and for Nonprofit and Voluntary Sector Quarterly. Agnieszka Chlon-Dominczak, Ph. D. is an Assistant Professor in the Educational Research Institute in Warsaw as well as at Institute for Statistics and Demography in Warsaw School of Economics. Previously she was a Deputy Minister and Head of Department of Economic Analyses and Forecasting in the Ministry of Labour and Social Policy. Her research interest include: demography, pension systems, labour markets, social policy, health and education. Souad Demigha. Has a PhD in computer science from the Sorbonne-University. Souad is a Lecturer in computer science at the University of Paris XI and researcher at the CRI (Research Department of Computer Science) of the Sorbonne University. Her research lies in the area of information systems, educational systems, medical imaging and data warehousing systems. Dagmar Caganova, assoc. prof. in the field of Industrial Engineering, is the Vice director for Foreign Affairs and International Projects of the Institute of Industrial Engineering, Management and Quality at the Slovak University of Technology, Bratislava, Slovakia. Her professional interests lie in Human Resource Management, Intercultural Management and Gender Diversity. She is a tutor on PhD study programmes in Intercultural Management and Professional Language Communication and has participated in EU research programmes. Her numerous publication activities are closely connected to her professional as well as research interests. xiii

Marcelo Moreira Campos holds a Master´s Degree in Information Science in the field of knowledge management. His professional experience encompasses information and knowledge management in government organizations in Brasília, strategic management of information, and research project management. Since 2005, he is in charge of information management and organization at the R&D Department of the Brazilian Agricultural Research Corporation (Embrapa). Natalia Dnerpovskaya, PhD, Associate Professor and Head of Knowledge Management Department of Moscow State University of Economics, Statistics and Informatics (MESI). Professional activities include the academic knowledge management, online training courses design and E-learning development. José Esteves is an Associate professor of Information Systems at IE Businss School. José Holds a Ph.D. in Information systems, a Diploma in Business Administration, MSc. and engineer degrees in Information systems. He has been an Author of many published articles about ERP systems. Interests focus on the implementation and use of enterprise systems, ERP systems, and impact of information systems on organizations, benefits of information systems, knowledge management and its use at organizational level. Max Evans BSc, MI, PhD is an Adjunct at the University of Toronto (Faculty of Information and Institute of Communication, Culture and Information Technology (Digital Enterprise Management program)) where he teaches strategy, innovation, and information systems/technology. Max’s is also an Affiliated Researcher at the Knowledge Media Design Institute (KMDI) in the area of knowledge management. Helio Aisenberg Ferenhof, M. Eng., MBA, PMP PhD candidate in Production Engineering (UFSC). Has Master degree in Knowledge Management from UFSC (2011). MBA in E-Business from FGV / RJ (2001); Specialist in Ditatics for Higher Education from SENAC/SC (2012); Bachelor's degree in computer science from UNESA (1999); Johanna Frances has worked 10 years in the fields of computer science before going back to school in 2004. She did a Master 1 of History and Ethnology and a Master 2 of Sociology. Today she is a PhD in Knowledge Management in partnership with the PSA (French car manufacturer) and Telecom Ecole de Management (France). nd

Adela Anca Fucec Adela is a 2 year PhD Student at the Management Doctoral School of the Bucharest University of Economic Studies, Romania. The author’s main focus of research is the knowledge economy and its effects on micro and macroeconomic level, especially from the point of view of the quantitative and qualitative managerial efficiency. Tatiana A. Garanina Currently Tatiana A. Garanina is Senior Lecturer, Department of Finance and Accounting and Associate Director of Master in Management Programs at Graduate School of Management, St.Petersburg University. Tatiana got her Specialist Degree and Ph.D. from the same University in 2009. She also participated in Executive Programs at Harvard Business School (2011, 2012). Mahmood Ghaznavi Has 12 years of professional experience in the field of Banking and Information Technology (IT). Mahmood was involved in planning and managing IT initiatives/investments of a bank and played a key role in the development of National Credit Information System of Pakistan. Mahmood is currently pursuing his PhD degree in knowledge management (KM) in Massey University, New Zealand. Ales Gregar, Ph.D. is a Vice-Rector at Tomas Bata University in Zlín, Czech Republic. He teaches in the Faculty of Management and Economics for master and doctoral degree courses in Human Resource Management and Operating Systems. He has led many international research projects focused at strategic HRM for competitiveness, Knowledge management, and managing the Careers of elderly employees. Hussain Hamed PhD student in health care management. My research area is focused on improving performance and quality of care in acute hospitals. The focus of my PhD thesis is on the role of intangible resources (IR)in improving quality of care in hospitals. I am also interested in service improvements in health care settings. Ionut Viorel Herghiligiu is currently a PhD student in the last year at “Gheorghe Asachi” Technical University from Iasi, Romania and in the first year at University of Angers, ISTIA, and France. The title of Ionut PhD thesis is “Research on the Environmental Management System as a Complex Process at Organizations Level” Jukka Huhtamäki, M.Sc. (Hypermedia) is a researcher and a teacher at the Intelligent Information Systems Laboratory at Tampere University of Technology and a founding member of Stanford’s Innovation Ecosystems Network. His research is focused on developing methods and processes of data-driven visual analytics for insights on the structure and dynamics of business and innovation ecosystems.

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Chandana Jayawardena Doctoral researcher attached to the Department of Management and Marketing, Faculty of Management and Economics, Tomas Bata University in Zlin, Czech Republic. His research interests focus on Career Development, Managerial Competencies, and Employees Behaviour at work. He is an academic staff member of the University of Peradeniya, Sri Lanka Palmira Juceviciene Ph. D., Habil. Dr., full professor at Kaunas University of Technology, Lithuania. Research interests are individual and organizational learning, knowledge creation and management, learning organizations and regions, human resource development, higher education. Palmira has published more than 200 scholarly articles and 10 books, and is a consultant in individual and organizational learning, learning organizations and regions, and human resource development. Robertas Jucevicius is a Professor and Director of the Business Strategy Institute at Kaunas University of Technology, Lithuania. Robertas holds a PhD in Economics and Habilitated Doctor in Management, a visiting fellow at the University of Cambridge (UK), as well as Fulbright (USA) and Wallenberg (Sweden) fellow and the member of the Council for National Progress of Lithuania. Nowshade Kabir CEO of Knolee Group, a Canadian investment and consulting company focused on technology investment. Has M. Sc. in Computer Science, MBA and Ph. D. in Information Technology. Present interests are Big Data, Innovation, Knowledge Management, Semantic Technologies, Entrepreneurship and Strategic Management. Sajid Khan is doing PhD in Management at Massey University New Zealand. His doctoral research aims to identify the characteristics of mental models of flexible educators developing innovative approaches to instruction. Sajid has earned a Master degree in Human Resource Development and Post Graduate Diploma in Management from IMSciences, Pakistan and Massey University, New Zealand respectively. Nasrullah Khilji is an MBA graduate from Cardiff University of Wales, UK and currently pursuing his doctorate research at University of West London. He is actively involved in the technology driven strategies for innovative business processes at Cranfield University Innovation Centre. He also has senior level management experience in training and development. Irene Kitimbo is a doctoral candidate in the School of Information Studies at McGill University. She holds a Masters in Information Studies from McGill with an emphasis in Knowledge Management. Irene currently studies learning processes in project based organizations, such as knowledge sharing, lessons learned and the effects of institutional memory loss. Palmira López-Fresno is specialized in service quality, management and leadership abilities. Palmira is a Visiting professor at the University of East of Finland, and a President of the Service Quality Committee – Spanish Association for Quality (AEC) and Vice President of AEC. Palmira is also Author of several books on the topics of service quality and leadership abilities. Gabriela Citlalli López Torres Graduated from the Manchester Business School, in 2010, PhD in Operations Management. Her work experience includes working at the London Business School, as program developer of the Master in Business Administration (MBA). She taught at the University of Manchester. She has participated in ISO 9000 certifications at the Economical Ministry of Aguascalientes. Her industrial experience is in the automotive and electronic sector. She is researcher at the Universidad de Aguascalientes. Sarah McNabb is a Research and Dissemination Associate at Futures Group, where she supports corporate knowledge management initiatives and the USAID-funded Health Policy Project. She holds a BS in International Health from Georgetown University. Her research interests include knowledge management, health promotion, behavior change communications, health informatics, and data demand and use. Tarryn Mason is the General Manager of Progression, based in Johannesburg, South Africa, which offers end to end disability equity solutions. Tarryn has a finance degree and an MBA and has over 6 years of experience in disability solutions. She is also the project sponsor of The Core Programme – a strategic knowledge leadership intervention in Progression. Negar Monazam Tabrizi is a Doctoral candidate at the School of Environment and Development at the University of Manchester. Her research interests are knowledge management, information technology communications, and healthcare. Prior to entering academia she worked as an industrial engineer in the field of software engineering. Her academic background includes industrial engineering and management of information systems. Oliver Moravcik from 1972-1976 he was at Technische Hochschule Ilmenau/Germany, Dipl.-Ing. in Automation. 1978-1982 Technische Hochschule Ilmenau/Germany, Dr.-Ing. in Computer Science, 1990 Slovak University of Technology, assoc. Professor/ Applied Informatics and Automation, visiting profesor in Koethen and Darmstadt/Germany, 1998 Professor/Applied In-

formatics and Automation at Slovak University of Technology Bratislava, 2006 Dean of Faculty of Materials Science and Technology in Trnava, Slovak University of Technology Bratislava Vyda Mozuriuniene from Comfort Heat Ltd is the Managing Director, and has a Ph.D. in Management. Research interests – knowledge creation and management, process management, strategic management. Vyda is a Consultant in the areas of organization’s knowledge management, process management, and franchise. Dr. Mozuriuniene has published 5 scholarly articles. Li Ya Nan is a master student from University of Chinese Academy of Sciences (UCAS). Fattah Nazem is an Associate Professor. He has been vice-president of the research department for the last five years. His research interests are in the field of Higher Education Management. He has written 2 books and 97 articles. He is Chief Executive of the Quarterly Journal of Educational Science. Faezeh Norouzi has lived in Tehran, Iran for 10 years. Faezeh accomplished his master of Art in research training and has taught statistic and research approach within his study in master degree. Faezeh is now in charge of bank accountant in City bank-syyed khandan street, Tehran, Iran. Jamie O'Brien teaches at St. Norbert College in De Pere, Wisconsin, in the Business Administration Department. His areas of teaching include Management, Organizational Theory, Organizational Behavior and Strategy. He earned his Ph.D. from the University Of Limerick, Ireland, in May 2012. His research interests fall predominantly within the management discipline in the knowledge management field. Dan Paulin holds a PhD in Technology Management and Economics from Chalmers University of Technology in Gothenburg, Sweden. Currently he holds positions as Lecturer, Program Director for executive education programs, and Vice Head of Department. His research is focused on knowledge dissemination in multinational settings, with publications in scientific journals such as the EJKM. Michael Pitts is an Associate Professor of Strategic Management at Virginia Commonwealth University. He has also held a Fulbright Scholar position in Bratislava, Slovakia. He has used the ‘live-case’ to assist nearly 100 companies and he is honored to be twice selected as a School of Business “Distinguished Teacher”. Internationally, He has presented research and/or participated in grants in the European Union, Africa and the Middle East as well as North America. Alexandru-Ionut Pohontu PhD student within “Gheorghe Asachi” Technical University from Iasi, Romania focused on knowledge sharing, and organizational learning process. The title of my PhD thesis is „Promoting of synergistic processes to knowledge sharing within organizations.”. Agnieta Beatrijs Pretorius is the Academic Manager, ICT, at the eMalahleni campus of the Tshwane University of Technology, South Africa. Prior to this she was a software developer. Her current domains of research and teaching include knowledge management, assessment of intellectual capital, performance management, decision support systems, software engineering and technical programming. Juli Purwanti graduated in Mathematical Science, University of GadjahMada Yogyakarta, and getting Magister of Management, Institute of Technology Bandung. Is Leadership Facilitator in Telkom Corporate University. Juli has experience in designing Human capital Policy for 11 years. Juli is ery interested on Knowledge Management and Learning Organization practices. Hong Quach pursued her Doctor of Science in Engineering Management at the George Washington University. She has extensive professional experience in leadership and management, engineering, information technology, strategy planning, and change management. Her research interests include Engineering and Technology Management, Knowledge and Information Management. Gillian Ragsdell is a Senior Lecturer in Knowledge Management and Director of Research Degree Programme in the Department of Information Science at Loughborough University. Her interest in knowledge management practices has taken her into a wide variety of organisations; recent examples are from the voluntary sector and the energy industry. Yolanda Ramírez is an Assistant Professor of the Faculty of Economics and Business Administration at the University of Castilla-La Mancha, Spain. Her current research interests include intellectual capital, knowledge management, non-profit management and quality management. Her research work is focused on methods and techniques for building models of measuring and management intellectual capital in the universities. xvi

Vincent Ribière is a Co-Founder and the Managing Director of the Institute for Knowledge and Innovation (IKI-SEA) Southeast Asia hosted by Bangkok University. He is an Associate Professor at Bangkok University and the Program Director of the Ph.D. program in Knowledge and Innovation (KIM). Vincent consults, teaches, and conducts research in the areas of knowledge management, innovation management and creativity. Mochamad Fadillah Rizky earned a Bachelor’s Degree in Management of Telecommunication and Informatics Business (S.MB) at Sekolah Tinggi Manajemen Bisnis Telkom in 2007 and a Master‘s Degree in Business Administration (MBA) at James Cook University in 2009. He is currently working in PT Telekomunikasi Indonesia, Tbk. as an officer in Leadership and Global Talent Academy, Telkom Corporate University. Francis Rousseaux is a full professor in computer science at University, coordinator of several European R&D projects for Ircam-CNRS, a research laboratory dedicated to computer music. Engineer in informatics, he first worked within the software industry before becoming a researcher in artificial intelligence. Jean Petit is one of his students, working on cultural heritage problems. Mikhail Rozhkov PhD candidate of the Knowledge Management and Innovation Research Centre of the Department of Industrial and Systems Engineering of The Hong Kong Polytechnic University. Associate Head of the KM Research Institute of the Moscow State University of Economics, Statistics and Informatics (MESI), Moscow, Russia. Mikhail graduated from the Amur State University, Blagoveshchensk, Russia. Kalsom Salleh is a Senior lecturer at the Faculty of Accountancy, Universiti Teknologi MARA, Shah Alam, Malaysia. Research areas include Knowledge Management, Intellectual Capital, Accounting and Auditing. Published many research papers in international refereed journals, conference proceedings and book chapters as well as sitting on the editorial board and reviewing committee members of several journals and conference proceedings. Chanthawan Sathitsemakul is a Ph.D. candidate in Knowledge Management and Innovation Management and is working at Kasikornbank, the leading Thai financial institute. She is interested in how to sustain KM in the knowledge sensitive organization. She is currently doing her research on the topic “The influence of emotional intelligence on employees’ knowledge sharing attitude in organizations in Thailand”. Taina Savolainen is a Professorship of Management and Leadership at the University of Eastern Finland, Dpt. of Business. Prof. Savolainen is specialized in trust within organizations, leadership, and organizational change, and global competitiveness management. Her academic achievements have been recognized in Who’s Who in the World with over 100 international academic publications. Alexander Serenko is an Associate Professor of Management Information Systems in the Faculty of Business Administration at Lakehead University, Canada. Dr. Serenko holds a Ph.D. in Business Administration from McMaster University. Alexander has published over 60 articles in refereed journals, including MIS Quarterly, Information & Management, Communications of the ACM, and Journal of Knowledge Management. Manasi Shukla, obtained her MBA (FMS: from top five B-schools in India), PhD in Knowledge management services industries (Delhi University) has around seven years each of industry and academic experience. In total, she has around 9 publications and 17 conference acceptances. She is currently an Assistant Professor and KM Strategist at IKI-SEA, Bangkok University, Thailand. Łukasz Sienkiewicz holds a Ph.D. in human capital management from Warsaw School of Economics, where he is currently employed as an Associate Professor at the Department of Human Capital Development. He specialises in human capital management and labour market issues. Lukasz is an Expert in European Employment Observatory of the European Commission and national skills forecasting expert at CEDEFOP. Aelita Skaržauskienė was the couch in the Self-managing teams building project in European Parliament together with DEMOS Group Belgium (www.demosgroup.com). In her work dr. A. Skaržauskienė applies both knowledge of management and modern leadership-correlated disciplines such as Business dynamics, Systems thinking, Chaos and complexity theories. Adam Soder is an Applications Project Engineer at Sumitomo Drive Technologies in Chesapeake, Virginia; and is the lead managing engineer for the company’s “smart” gearbox technology. He received his Bachelors Degree in Mechanical Engineering Technology from Old Dominion University in Norfolk, Virginia in 2009; and will soon be pursuing graduate studies in engineering management.

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Melanie Sutton is the owner of i-innovate, a strategic intangible capital consulting company in Johannesburg, South Africa. With a Masters Degree in Philosophy - Knowledge Management, 13 years of hands-on practical experience as a knowledge management practitioner and an ICountant accreditation from Smarter Companies, Melanie focuses primarily on designing and developing intangible strategy programmes for organisations. Petr Svoboda received both B.Sc. and M.Sc. degrees in Management and Economics from the Faculty of Management, University of Economics in Prague, Czech Republic, in 2009 and 2011. He is currently studying for his Ph.D. degree at the same university. His research interests include marketing strategies and management. Elena Tkachenko Anatolyevna Doctor of Economics, the professor of the Department of the Enterprise Economics and Industrial Management (St. Petersburg State University Of Economics). Author of more than 120 scientific and methodical works, including 10 textbooks and 7 monographs. The sphere of scientific interests â&#x20AC;&#x201C;innovations, investments, management of the intellectual capital, Industrial development. Katarzyna TrawiĹ&#x201E;ska-Konador Studied at the University of Leuven in Belgium, the Freie UniversitĂ¤t in Berlin and the University of Vienna. Katarzyna accuired extensive hands-on professional experience in education working as director for studies at private continuing education institutions. Main fields of professional interest include vocational education and training, continuing, non-formal and informal education, and distance education. Nurhayat Varol is an Instructor in the field of Information Technology at Firat University/Turkey since 1992. Teaches IT courses based on student and project centered learning methods using distance education. Research interests are in multimedia, computer aided learning and computer aided design, e-pedagogy, distance education, knowledge management, and technical communication. Nurhayat has published more than 40 journal and conference publications. Serkan Varol He is currently pursuing Doctorate degree at Lamar University. He earned his Bachelor of Science in Industrial Engineering at West Virginia University and Master of Science in Engineering Management at Wilkes University. His research interests are in knowledge management and engineering management. Jana Volna is a Ph.D. student. She currently works on the research project VEGA 1/0920/11 called "Intellectual capital management as part of the strategic management of the company's value" with the duration of the project: January 2011 - December 2013. Karen Voolaid, graduated from Estonian Business School in 2001, defended her PhD in business administration at Tallinn University of Technology in 2013. She is working as Head of International programs and Director of Dean`s office at Tallinn School of Economics and Business Administration of TUT. Field of Research: organizational learning and development. Naphunsakorn Waiyawuththanapoom (Ronnie) is a researcher in IKI-SEA, with an extensive background and expertise in the energy economic, knowledge management and innovation management. He is currently a PhD candidate in the PhD program in Knowledge and Innovation management (PhD KIM) at Bangkok University, focused on the Open Innovation readiness framework. Paul Woolliscroft is a PhD student and researcher at the Slovak University of Technology, Faculty of Materials Science and Technology in Trnava. His PhD study area is Industrial Management and his current research interests focus upon knowledge management and the application of tacit knowledge. He holds a Masters Degree in Marketing from Staffordshire University, England.

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The Influence of Intellectual Capital on Firm Performance Among Slovak SMEs Anna Pilková, Jana Volná, Ján Papula and Marián Holienka Faculty of Management, Comenius University, Bratislava, Slovak Republic anna.pilkova@fm.uniba.sk jana.volna@fm.uniba.sk jan.papula@fm.uniba.sk marian.holienka@fm.uniba.sk Abstract: 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 capital in the new economy. Uniqueness, and hence the possibility of a longer validity of chosen competitive strategy lies within the internal environment of the organization, not external. The ability to identify opportunities does not make the competitive ability long‐term, or even sustainable. Sustainability is built and affected by the ability of a company to set up correctly its internal resources, not only tangible but intangible as well, with a focus on maximizing the creation of value added in the value chain. In relation with this view, the explanation why some companies have long been successful, while others fail, may just be answered through the analysis of their resources and capabilities. Issue of intellectual capital and its influence on firm performance is still under investigated among SMEs in Slovakia. Positive influence of intellectual capital on firm performance has been proved in numerous studies around the world, but the empirical evidence in case of Slovak SMEs has not yet been provided. In these studies, to measure the level of intellectual capital and its respective components in SMEs, value added intellectual coefficient (VAIC™), as developed by Ante Pulic, providing the information about the efficiency of tangible and intangible assets that can be used to generate value to a firm, is being applied. Keywords: intellectual capital, firm performance, VAIC™, SMEs, forward stepwise regression analysis

1. Introduction 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. Intellectual capital, which incorporates skills and knowledge at all levels of an organization, has become the most important economic resource that is replacing financial and physical capitals as the most important source in the new economy. The growing awareness and acceptance of the importance of intellectual capital as a source of competitive advantage has led to the need for an acceptable measurement model, given that traditional financial tools do not address the necessary concepts of intellectual capital (Volkov 2012). The value added intellectual coefficient (VAIC™), method developed by Ante Pulic (Pulic 2004), uses financial statements of a firm to calculate the efficiency coefficient on three types of capital – human capital, structural capital and capital employed. This measure, however being criticized by some authors, is the most frequent metric used to evaluate intellectual capital and its components using financial data. Due using information from accounting statements as a basis, VAIC™ is a suitable method when providing analyses of human capital efficiency, structural capital efficiency, capital employed efficiency and firm performance on a macro level due better comparability between companies, sectors or nations.

2. Intellectual capital and its influence on firm performance 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). 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

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Anna Pilková et al. the staff went home (Edvinsson 1997). The third category of intellectual capital resources comprises external structure, customer capital and market assets, which are basically about relations with customers (Bukh et al. 2001). Positive influence of intellectual capital on firm performance has been discussed and analyzed in numerous studies around the world (Chen et al. 2005, Mavridis and Kyrmizoglou 2005, Clarke et al. 2011, Komnenic and Pokrajčić 2012, Alipour 2012, Mondal and Ghosh 2012, Joshi et al. 2013). Uniqueness, and hence the possibility of a longer validity of chosen competitive strategy of a company lies within the internal environment of the organization, not external. The ability to identify opportunities does not make the competitive ability long‐ term, or even sustainable. Sustainability is built and affected by the ability of a company to set up correctly its internal resources, not only tangible but intangible as well, with a focus on maximizing the creation of value added in the value chain. In relation with this view, the explanation why some companies have long been successful, while others fail, may just be answered through the analysis of their resources and capabilities. Figure 1 shows the process of influence of intellectual capital components on firm performance through financial and non‐financial results as they are drafted within ARCS model for Intellectual Capital Repoting (Koch et al. 2000).

Figure 1: Process of influence of intellectual capital components on firm performance through financial and non‐financial results as drafted within ARCS model for intellectual capital Repoting (Koch et al. 2000)

3. Measurement of intellectual capital Because traditional financial and management accounting instruments are not able to capture all aspects of intellectual capital and report them to organizational managers and stakeholders, there is a high demand for an appropriate corporate reporting structure (Nazari and Herremans 2007). Over the past years, several methods have been developed to measure the intellectual capital of an organization. Karl Eric Sveiby presents an excellent summary of 42 existing measurement methods (Sveiby 2005). According to Sveiby, the suggested measuring methods for intellectual capital fall into four main categories: 1. Direct (DIC), which estimate the monetary value of intangible assets by identifying its various components. Once these components are identified, they can be directly evaluated, either individually or as an aggregated coefficient. 2. Scorecard (SC), through which various components of intangible assets or intellectual capital are identified and indicators and indices are generated and reported in scorecards or as graphs. SC methods are similar to DIC methods; expect that no estimation is made of the monetary value of the Intangible assets. A composite index may or may not be produced. 3. Market capitalization (MCM), which basically calculate the difference between a company's market capitalization and its stockholders' equity as the value of its intellectual capital or intangible assets. 4. Return on assets (ROA), where average pre‐tax earnings of a company for a period of time are divided by the average tangible assets of the company. The result is a company ROA that is then compared with its industry average. The difference is multiplied by the company's average tangible assets to calculate an average annual

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Anna Pilková et al. earning from the Intangibles. Dividing the above‐average earnings by the company's average cost of capital or an interest rate, one can derive an estimation of the value of its intangible assets or intellectual capital. ROA methods are based on the assumption that the existence of intellectual capital / intangible resources in an organization, which are generally not captured in the balance sheet of the company, affect operating results of an organization recorded in the profit and loss statement of that organization. First two categories, the direct and scorecard intellectual capital measurement methods might be very useful at a company level, as a basis for internal management information especially in the connection with business strategy management. Actually, these methods are operating with indicators and calculations at business level, what results in worse inter‐company comparability. On the other hand, market capitalization and ROA are methods, which should be generally preferred when providing analyses on a macro level due better comparability between companies, sectors or nations due using information from accounting statements as a basis.

4. Value added intellectual coefficient (VAIC™) From the mentioned 42 measurement methods, value added intellectual coefficient (VAIC™), a method developed by Ante Pulic (Pulic 2004), as the only one doesn’t quite fit any of the four listed categories, although it closest fits the ROA measurement methods (Sveiby 2005). According to Pulic, VAIC™ is an equation, which measures how much and how efficiently intellectual capital and capital employed create value (Pulic 2004). Ante Pulic (Pulic 2004) was one of the first scholars in the field of IC research to focus explicitly on the connection between IC and economic performance and the first to base his analyses solely on company balance sheet figures, i.e. financial indicators (Stahle et al. 2011). Until present times, VAIC™ has been frequently quoted in academic research papers through various analyses investigating the performance of individual companies, often on sectorial or national level (Mavridis and Kyrmizoglou 2005, Chen et al. 2005, Nazari and Herremans 2007, Kiong Ting and Lean 2009, Laing et al. 2010, Aras et al. 2011, Clarke et al. 2011, Pal and Soriya 2012, Pucar 2012, Alipour 2012, Mondal and Ghosh 2012, Komnenic and Pokrajcic 2012, Joshi et al. 2013). According to Pulic (Pulic 2004), VAIC™ calculations are based on intellectual capital efficiency (ICE) and capital TM employed efficiency (CEE), where value added intellectual coefficient (VAIC ) is the sum of these two coefficients. CEE can be calculated as VA/CE, where VA represents the value added and CE is the book value of the net assets of a company. ICE is obtained by adding up the human capital efficiency (HCE) and structural capital efficiency (SCE), where HCE is calculated as VA/HC and SCE is calculated as SC/VA. Human capital (HC) includes total salaries and wages for the company and structural capital is calculated as VA – HC. Value added for the company (VA) is the difference between the total sales (OUT) and total input or the total cost of bought‐in materials, components and services (IN). The VAIC™ equation is drafted in figure 2. As a TM performance measurement tool, greater value of VAIC indicates a higher efficiency level of the company TM (Joshi et al. 2013). If VAIC rises over time, then the efficiency level improves and a company creates more value and vice versa (Joshi et al. 2013).

Figure 2: Graphical illustration of VAIC™ equation

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5. Research methodology 5.1 Research goal The aim of this paper is to examine whether and to what extent the efficiency of intellectual capital and capital employed of a company as defined within VAIC™ model developed by Pulic (Pulic 2004) and its individual components affect the firm performance among SMEs in different industries in Slovakia. VAIC™ method uses financial statements of a firm to calculate the efficiency coefficient on three types of capital; human capital by the medium of human capital efficiency coefficient (HCE), structural capital by the medium of structural capital efficiency coefficient (SCE) and capital employed by the medium of capital employed efficiency coefficient (CEE). As a proxy for profitability, we have deployed return on assets of a company (ROA) due its general acceptance as a performance measure in entrepreneurship and strategy research. While investigating the effect of VAIC™ components on SME performance, our analysis also accounts for return on equity (ROE), leverage of the firm and firm size as control variables. Table 1: Industry distribution of research sample SK NACE Industry division Sample size 01 Crop and animal production, hunting and related service activities 337 10 Manufacture of food products 88 22 Manufacture of rubber and plastic products 79 23 Manufacture of other non‐metallic mineral products 45 25 Manufacture of fabricated metal products, exc. machinery and equipment 181 27 Manufacture of electrical equipment 60 28 Manufacture of machinery and equipment 92 35 Electricity, gas, steam and air conditioning supply 84 41 Construction of buildings 163 42 Civil engineering 69 45 Wholesale/retail trade and repair of motor vehicles and motorcycles 132 46 Wholesale trade, except of motor vehicles and motorcycles 753 47 Retail trade, except of motor vehicles and motorcycles 397 49 Land transport and transport via pipelines 139 62 Computer programming, consultancy and related activities 99

5.2 Sample We have based our analysis on 2011 financial statements of 2,718 Slovak SMEs operating in 15 different industries. To obtain our research sample, we have approached the commercial database of business information portal Universal Register Plus operated by CRIF ‐ Slovak Credit Bureau. The vendor obtained this database from the official Company register collection of documents and official Business bulletin. The original available dataset contained financial statements of 6,211 companies for year 2011. We have applied several criteria to obtain the final sample. Firstly, we have selected only small and medium‐sized enterprises according to the EU definition (EC 2005), using the annual turnover and/or annual balance sheet total criteria (due to missing information on staff headcount and autonomy of the enterprise these criteria were omitted). Secondly, we have removed companies with zero turnover, zero staff costs or negative equity value in the observed year from our selection. Finally, we have selected industries containing at least 10 companies in each SME size category (i.e. micro, small and medium), while assuring that this selection will reasonably cover the scope of existing industries. This procedure resulted in the final sample of 2,718 SMEs. Despite limited representativeness of this sample (due to character of the original data from commercial database) resulting from convenience sampling, its considerable scale enables us to generalize the findings to certain extent. The industry distribution of our sample is presented in table 1.

5.3 Analysis of the impact of VAIC™ and its components on firm performance To state the impact of each variable (value added intellectual coefficient (VAIC), human capital efficiency (HCE), structural capital efficiency (SCE), capital employed efficiency (CEE), return on equity (ROE), leverage and firm size) on return on assets (ROA), forward stepwise regression analysis has been applied on data. This method enables individual adding of independent variables into the model in every step of regression (according to setting F to enter/F to remove) till the moment when the best regression is achieved (Munková et al 2012). The model allows us to estimate the contribution of individual independent variables to the

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Anna Pilková et al. variability explanation of the dependent variable ROA. All variables have been divided into 3 groups: Dependent, independent and control. 5.3.1 Dependent variable As a dependent variable reflecting performance of a firm, we have used return on assets of a company financial indicator (ROA). This indicator is calculated as the ratio of operating income to book value of the total assets of the company and it shows how profitable a company is relative to its total assets. ROA is commonly used as a key performance indicator of profitability of companies in their annual reports and it has been widely used as a measure of financial performance in earlier research (Joshi et al. 2013). 5.3.2 Independent variables As independent variables, VAIC™ indicator as well as its components have been taken, specifically the human capital efficiency (HCE), structural capital efficiency (SCE) and capital employed efficiency (CEE) and their impact on ROA has been analyzed. VAIC™ is a coefficient, which measures how much and how efficiently intellectual capital and capital employed create value (Pulic 2004). As described earlier, it consists of three individual parts, the HCE, SCE and CEE. Human capital efficiency coefficient (HCE) is calculated by dividing a company’s value added by its human capital to indicate in practice the real productivity of the company’s personnel, i.e. how much value the company creates through one monetary unit invested in human resources (Stahle et al. 2011). There are three groups of possible results of HCE measure. If it is given the value more than “1”, this means that the created value added must have been greater than the human resources costs. If it is given the value less than “1”, the created value added is less than the human resources costs and if the measure gives exactly the value “1”, created value added accurately equals the costs of human resources. Structural capital efficiency (SCE) measures how much capital a company can create through one monetary unit invested in value added, i.e. it measures the productivity or efficiency of value added (Stahle et al. 2011). It is given by the formula SC/VA. Since SC equals VA – HC, exactly the same input data (HC and VA) enter into this measure as into human capital efficiency component (HCE). Capital employed efficiency coefficient (CEE) is created as value added divided by net assets of the company. This metric reflects how much value the company creates relative to its capital employed, expressed by net assets as a proxy. 5.3.3 Control variables To check the impact of other variables that may explain observed relationships with firm performance (ROA), three control variables have been included within the regression analysis: Return on equity (ROE), leverage and size of the firm. Return on equity (ROE) is calculated as the ratio of operating income of a business to its stockholders' equity. It is a measure of profitability of stockholders' investments. Leverage of the firm is calculated as the ratio of total debt to book value of the total assets of the company. A high proportion of debt may lead a firm to primarily focus on the needs of debt holders (Williams 2000). This is not consistent with the stakeholder view assumed by value added and VAIC™ (Clarke et al. 2011). Alternatively, firms that rely heavily on debt may lack the security required to attract investors, and will likely have higher interest payments, reflecting on the riskiness and returns of he firm (Clarke et al. 2011). The size of the firm is determined as a book value of total assets. Because it may have an impact on the dependent variable (ROA), it has been used as a control variable in the regression models. In our analysis we employed a natural logarithm of this figure.

6. Results 6.1 Descriptive statistics Main descriptive statistics such as mean, median, minimum, maximum, standard deviation, variance, kurtosis and skewness individually for human capital efficiency coefficient (HCE), structural capital efficiency coefficient (SCE), capital employed efficiency coefficient (CEE) and value added intellectual coefficient (VAIC) are presented in the annex of this paper.

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6.2 Regression analysis To state the impact of independent variables on firm performance (ROA), we have applied stepwise regression analysis on data with the purpose to estimate the contribution of each individual independent variable to the variability explanation of the dependent variable. We have developed two different types of models, each for all 15 industries. The first group of 15 models with value added intellectual coefficient (VAIC) as independent variable (table 2 and 3) and the second group with HCE, SCE and CEE as independent variables (table 4 and 5). Table 2: Overview of regression – modeling of the dependent variable ROA, models 1.1 – 1.8

Table 3: Overview of regression – modeling of the dependent variable ROA, models 1.9 – 1.15

Table 4: Overview of regression – modeling of the dependent variable ROA, models 2.1 – 2.8

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Anna Pilková et al. Table 5: Overview of regression – modeling of the dependent variable ROA, models 2.9 – 2.15

To explain the functionality of the model, we give an example with model 2.1. At the beginning the variables are not in a regression equation, no variable has been entered or removed. In model 1, which reflects the industry 01 ‐ Crop and animal production, hunting and related service activities, the progress has been as follows. In the first step, one variable in regression model ‐ the variable SCE has been entered. As the table of stepwise summary (table 4) shows, after entering one variable the determination index (R‐square) went up by 0.412 (R‐square (SCE)). In the second step, two variables are in regression equation, the variable CEE has been entered. The determination index (R‐square) went up by 0.124, and this model explains 54.6% variability of the variable ROA. In the third step, the variable ROE has been entered and the determination index (R‐square) went up by 0.016. In the fourth step, the variable leverage has been entered and the determination index (R‐ square) went up by 0.007, while this model explains 55.9% variability of the variable ROA (total R‐square of 0.559). According to table 2 and 3 in Model 1, VAIC™ coefficient is positive and significant at the 0.1% level for all industries but one: 35 Electricity, gas, steam and air conditioning supply. At this industry, VAIC doesn’t have sufficient explanatory power and only two control variables (ROE and leverage in which ROE has a dominant position) explain variability of ROA. In spite of fact, that VAIC coefficient has sufficient explanatory power in 14 industries, there are five industries in which ROE has greater explanatory power than VAIC. While according to Model 1 VAIC™ is positive and significant for all industries but one in Model 2 table 4 and 5 it is appearant that only in one industry (46: Wholesale trade, exept of motor vehicles and morcycles) all three VAIC components contribute to explanation of ROA variability. While capital employeed (CEE) contributes significantly to variability of ROA explanation in all 15 industries, human capital (HCE) contributes in 10 industries and structural capital (SCE) only in 7 industries. In addition CEE has greater explanatory power in 10 industries than either HCE or SCE. Based on these findings CEE is generally the more dominant component in VAIC™ when predicting ROA in companies across industries.

7. Discussion and conclusion This paper concerns highly topical issue of the competitiveness development and the support of the growth of small and medium‐sized enterprises (SMEs) as the driving force of economic development of Slovakia. To examine the success of this development expressed via performance results, there is the need to identify the enablers through the concept of intellectual capital. Many experts share opinion that intellectual capital at nowadays is the most important asset of the companies, industries or regions that significantly contributes to value creation. There is a general interest to know whether and at which extend intellectual capital contributes to this value creation. In spite of the fact that many research studies have been run so far the complex issue of measurement of the intellectual capital impact on value of companies, industries, states hasn’t been satisfactory solved, yet. The results of these

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Anna Pilková et al. studies are also various measurement tools that have been applied on different analyzed objects. One of such tools is VAIC™. This paper examines whether and to what extent the efficiency of intellectual capital and capital employed of a company as defined within VAIC™ model and its individual components affect the firm performance among SMEs in different industries in Slovakia. Based on analysis of 2,718 SMEs operating in 15 industries we found out positive significant relations between VAIC™ and ROA as a proxy of value creation in 14 industries. However, the industry with no significant relationship (35: Electricity, gas, steam and air conditioning supply) shows the highest mean value of VAIC™ (see figure 9 in Annex), which suggest that relatively high level of intellectual capital is present in companies irrespective their performance, while the performance is driven by another factors. These findings are in line with other studies which have been run in different countries. Despite the fact that VAIC™ has sufficient explanatory power in 14 Slovak industries we found out greater explanatory power of ROE in 5 of them. This finding may be explained by the traditional industrial concepts in the mind of the Slovak SME´s owners and managers. It suggests that high emphasis is still placed on quantity of production, revenues and profits in business success evaluation instead of knowledge content and value creation efficiency which is a core of VAIC™ measure (Pulic, 2000, 2004). This traditional concept seems to be most visible in already above mentioned industry 35: Electricity, gas, steam and air conditioning supply We have also studied impacts of three components of VAIC™ on ROA. We found out that Slovak companies in 10 industries (out of 15) would benefit if they further invest in human capital ‐ skills and knowledge of employees. However, similarly as in other studies carried out worldwide, we have concluded that physical and financial capital provides the strongest influence over the value creation process and has significant and positive influence on ROA in all 15 industries. This also confirms that intellectual capital both is not sole value creator and must be accompanied with optimal structure of physical and financial capital and CEE is still very dominant component in VAIC™. This may be again explained less emphasizing of the intellectual capital component in the process of value creation in Slovakia. Our research has also confirmed that structural capital efficiency (SCE) is less embedded component with dominant explanatory power in only two industries out of 15. Our research has a few limitations. First of all, it is based on one‐year data (2011), which means that no dynamics and time lag analysis could be run. We understand that in this type of investigation time lag is very important to study, too. We have not also studied relationships between components of IC that create so‐ called moderating effects. Finally, further more detailed industrial analysis to identify reasons of differentiation among industries as far as impact of individual components of intellectual capital is important, too. Nevertheless, our research is the first in to study IC from value creation perspectives in Slovakia and contributes to extent knowledge in this field in our country.

Acknowledgements This paper has been funded by project Vega 1/0920/11.

Annex 1 Table 6: Statistics summaries for human capital efficiency (HCE) coefficient

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Anna Pilková et al. Table 7: Statistics summaries for structural capital efficiency (SCE) coefficient

Figure 8: Statistics summaries for capital employed efficiency (CEE) coefficient

Figure 9: Statistics summaries for value added intellectual coefficient (VAIC) coefficient

References Alipour, M. (2012) “The effect of intellectual capital on firm performance: an investigation of Iran insurance companies”, Measuring Business Excellence, Vol. 16, No. 1, pp. 53‐66. Aras, G., Aybars, A. and Kutlu, O. (2011) “The interaction between corporate social responsibility and value added intellectual capital: empirical evidence from Turkey”, Social Responsibility Journal, Vol. 7, No. 4, pp. 622‐637. Bontis, N. (2002) World Congress on Intellectual Capital Reading. Butterworth‐Heinemann, Boston, 392 pp. Bukh, P.H., Larsen, H.T. and Mouritsen, J. (2001) “Constructing intellectual capital statements“, Scandinavian Journal of Management, Vol. 17, pp. 87‐108. Chen, M., Cheng, S. and Yuchang, H. (2005) “An empirical investigation of the relationship between Intellectual capital and firms’ market value and financial performance”, Journal of Intellectual Capital, Vol. 6, No. 2, pp. 159‐176. Clarke, M., Seng, D. and Whiting, R. (2011) “Intellectual capital and firm performance in Australia”, Journal of Intellectual Capital, Vol. 12, No. 4, pp. 505‐530. Edvinsson, L. (1997) “Developing intellectual capital at Skandia”, Long Range Planning, Vol. 30, No. 3, pp. 366‐73. Edvinsson, L. and Malone, M.S. (1997) Intellectual Capital. The proven way to establish your comopany's real value by measuring its hidden brainpower. Harper Business, London. European Commission (2005) The new SME definition. User guide and model declaration. Publications Office of the European Union, Luxembourg. Joshi, M., Cahill, D., Sidhu, J. and Kansal, M. (2013) “Intellectual Capital and Financial Performance: an Evaluation of the Australian Financial Sector“, Journal of Intellectual Capital, Vol. 14, No.2, pp. 264‐285. Kiong Ting, I. and Lean, H. (2009) “Intellectual capital performance of financial institutions in Malaysia”, Journal of Intellectual Capital, Vol. 10, No. 4, pp. 588‐599.

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Anna Pilková et al. Koch, G., Leitner, K.H. and Bornemann, M. (2000) “Measuring and reporting intangible assets and results in a European Contract Research Organization”, Paper prepared for the Joint German‐OECD Conference Benchmarking Industry‐ Science Relationships, Berlin, October 16‐17. Komnenic, B. and Pokrajčić, D. (2012) “Intellectual capital and corporate performance of MNCs in Serbia”, Journal of Intellectual Capital, Vol. 13, No. 1, pp. 106‐119. Laing, G., Dunn, J. and Hughes‐Lucas, S. (2010) “Applying the VAIC model to Australian hotels”, Journal of Intellectual Capital, Vol. 11, No. 3, pp. 269‐283. Mavridis, D. and Kyrmizoglou, P. (2005) “Intellectual Capital Performance Drivers in the Greek Banking Sector”, Management Research News, Vol. 28, No. 5, pp. 43‐62. MERITUM (2002) “MERITUM Guidelines for Managing and Reporting on Intangibles, Measuring Intangibles to Understand and Improve Innovation Management”, [online], InCaS. http://ec.europa.eu/research/social‐ sciences/projects/073_en.html. Mondal, A. and Ghosh, S. (2012) “Intellectual capital and financial performance of Indian banks”, Journal of Intellectual Capital, Vol. 13, No. 4, pp. 515‐530. Mouritsen, J., Bukh, P.N., Larsen, H.T. and Johansen, M.R. (2002) “Developing and managing knowledge through intellectual capital statements”, Journal of Intellectual Capital, Vol. 3, No. 1, pp. 10‐29. Munková, D., Stranovská, E. and Ďuračková, B. (2012) “Impact of Cognitive‐Individual Variables on Process of Foreign Language Learning”, Procedia ‐ Social and Behavioral Sciences 46, 5430‐5434. Nazari, J. and Herremans, I. (2007) “Extended VAIC model: measuring intellectual capital components”, Journal of Intellectual Capital, Vol. 8, No. 4, pp. 595‐609. Pablos, P.O.D. (2003) “Knowledge management projects: state of the art in the Spanish manufacturing industry International”, Journal of Manufacturing Technology and Management, Vol. 14, No. 4, pp. 297‐310. Pal, K. and Soriya, S. (2012) “IC performance of Indian pharmaceutical and textile industry”, Journal of Intellectual Capital, Vol. 13, No. 1, pp. 120‐137. Pucar, S. (2012) “The influence of intellectual capital on export performance”, Journal of Intellectual Capital, Vol. 13, No. 2, pp. 248‐261. Pulic, A. (2004) “Intellectual capital – does it create or destroy value?”, Measuring Business Excellence, Vol. 8, No. 1, pp. 62‐ 68. Stähle, P., Stähle, S. and Aho, S. (2011) “Value added intellectual coefficient (VAIC): a critical analysis”, Journal of Intellectual Capital, Vol. 12, No. 4, pp. 531‐551. Stewart, T. (1998) Intellectual Capital Nicholas Brealey Publishing, London. Sveiby, K. E. (1997) The New Organizational Wealth: Managing and Measuring Knowledge‐based Assets. Barrett‐Kohler, San Francisco. Sveiby, K.E. (2005) “Methods for measuring intangible assets”, [online]. www.sveiby.com/Portals/0/articles/IntangibleMethods.htm. Volkov, A. (2012) “Value Added Intellectual Co‐efficient (VAIC): A Selective Thematic‐Bibliography”, Journal New Business Ideas and Trends, Vol. 10, No. 1, pp. 14‐24. Williams, M. (2000) “The Association between Gender and Ethnic Diversity of Board Structure on the Intellectual Capital Performance of Publicly Listed Companies from an Emerging Economy: Evidence from South Africa”, [online]. www.vaic–on.net/download/Paper3.pdf.

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Indicators for Assessment of Innovation Related Intellectual Capital Agnieta Pretorius Department of Software Engineering, Faculty of Information and Communication Technology, Tshwane University of Technology, Pretoria, South Africa PretoriusAB1@tut.ac.za Abstract: Innovation and the promotion of innovation are increasingly reflected in the goals and objectives of organisations. To increase the likelihood and frequency of innovation, organisations strive to develop their innovative capacity. The ability or capacity to innovate is a determinant factor for survival and success in a free market economy. This research views innovative capacity as a subset of intellectual capital, referred to as innovation related intellectual capital. It draws upon literature on intellectual capital and on innovation to identify possible indicators for innovation related intellectual capital. These indicators span across the major categories of intellectual capital, including human capital, relational capital and structural capital. Indicators for innovation related intellectual capital are context dependent and therefore need to be customised for individual contexts, taking into account factors such as purpose of or motivation for assessment, level of assessment, goals and objectives of organisation, industry and line of business, business sector, size of organisation and resources the organisation is willing to commit. It is argued that the relevance, importance and significance of indicators proposed for measurement of innovation related intellectual capital need to be evaluated. This research subsequently explores possibilities for evaluating the suitability of these indicators for predicting innovation performance. This is done, for example, by analysing the extent and significance of the correlation of such indicators with innovation performance and subsequently the extent and correlation of innovation performance with organisational performance. Examples of indicators for assessment of innovation performance and organisational performance, retrieved from literature, were also provided. This research is of a qualitative, theoretical and explorative nature. The findings are expected to form the basis for future research towards determining appropriate indicators for assessment of innovation related intellectual capital in various contexts. Possibilities for such further research are provided. The resulting indicators are furthermore expected to be of value to practitioners and consultants by assisting in identification of development areas for promotion of innovation. Keywords: innovative capacity, innovation performance, intellectual capital, assessment, indicators, performance management

1. Introduction Innovation and the promotion of innovation increasingly occupy central stage in the goals and objectives of organisations. To increase the likelihood and frequency of innovation, organisations strive to develop their innovative capacity. The ability or capacity to innovate is acknowledged as a determinant factor in nurturing a firm’s competitive advantage (Marques & Ferreira 2009) and for survival and success in a free market economy (Doyle 1998; Quinn 2000; Tidd, Bessant, & Pavitt 2001; Wang & Ahmed 2004; Bessant & Tidd 2007). This research view innovative capacity as a subset of intellectual capital (IC), referred to as innovation related IC. This paper draws on existing literature on IC and on innovation to identify possible indicators (reflecting level of achievement) for assessment of innovation related IC and explores possibilities for evaluating the suitability of such indicators for predicting innovative performance. These indicators span across the major categories of IC, including human capital, relational capital and structural capital. This research is expected to form the basis for future research towards determining appropriate indicators for assessment of innovation related IC in various contexts. These indicators are also expected to assist in pin‐pointing development areas for promotion of innovation.

2. Terminology The following sub‐sections explain some of the terminology of this paper.

2.1 Intellectual capital Literature provides a variety of definitions and categorisations for IC, for example (as illustrated in Figure 1):

According to Brooking (1999), IC refers to the collective intangible assets that enable an organisation to function, including human centered assets, market assets, infrastructure assets and intellectual property (IP) assets.

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Similar to the components of IC identified by Brooking – without providing a separate category for intellectual IP assets – Sveiby (1997) refers to these components as individual competence, external structure and internal structure respectively.

Stewart observes that academics typically group IC into at least three categories, namely human capital, customer capital and structural capital (as cited in Smith & McKeen, 2003).

Customer capital is also referred to as relational capital, including not only relationships with customers, but also relationships with other stakeholders (e.g. De Pablos, 2004).

Edvinsson and Malone (1997) subdivide structural capital into organisational capital, process capital and innovation capital.

The Skandia Navigator is composed of five focus areas (Edvinsson & Malone 1997; Roos et al. 1997; Bontis 2001): financial, human, customer, process and renewal and development.

The balanced scorecard (Kaplan & Norton 2005) includes four perspectives, namely financial, learning and growth (also referred to as innovation and learning), customer and internal business processes. Brooking

Sveiby

Human Centred Assets

Individual Competence

Human Capital

Human Focus

Market Assets

External Structure

Customer (Relational) Capital

Customer Focus

Customer Perspective

Process Perspective

Internal Business Processes Perspective

Intellectual Capital (IC)

Stewart

Edvinsson and Malone

Organisational Capital Infrastructure Assets

Intellectual Property Assets

Internal Structure

Structural Capital

Process Capital

Innovation Capital

Skandia Navigator Financial Focus

Renewal and Development Focus

Balanced Scorecard Financial Perspective Learning and Growth Perspective

Source: extended from Pretorius and Coetzee (2005) Figure 1: Components of IC In this study our aim is not to derive a generally accepted definition for IC, but to accommodate the variety of definitions and categorisations.

2.2 Innovation Innovation involves creating/developing/conceiving and implementing something new, e.g. ideas, processes products or services (Thompson 1965; Van der Van 1986; Martins 2000; Sáenz & Aramburu 2011) and requires generation of new knowledge (Nonaka & Takeuchi 1995; Subramania & Youndt, Sáenz & Aramburu 2011). In addition, some authors include the notion of benefit or usefulness (Coetzee 2000). For organisations, useful innovation would be innovation of benefit to the organisation, e.g. supporting the goals, objectives and key success factors of the organisation.

2.3 Innovative capacity and innovation related intellectual capital As mentioned earlier, this research view innovative capacity as a subset of IC, referred to as innovation related IC. The phrase innovation related intellectual capital (also referred to as innovative capacity or innovative capabilities) is used to refer to the capacity of organisations to accomplish “useful” innovations. The extent to which organisations accomplish such useful innovations is referred to as innovation performance.

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2.4 Assessment Although the terms “measurement”, “(e)valuation” and “assessment” are often used interchangeably, authors such as Andriessen (2004), note a distinctive difference between measurement and (e)valuation. In this paper the word “assessment” includes measurement, (e)valuation and all other such notions for determining value.

2.5 Context Existing literature suggests that methods for assessment of IC to be used in a specific context need to be selected and/or customised based on factors or contextual dimensions such as:

Audience (Sveiby 2002);

Business sector (Viedma Marti 2001; Malhotra 2003);

Goals and objectives of organisation (Harrison & Sullivan 2000; Smith & McKeen 2003);

Industry or line of business (Van Buren 1999);

Level of assessment (Sánchez, Chaminade & Olea 2000; Smith & McKeen 2003);

Purpose of or motivation for assessment (Andriessen 2004; Housel & Bell 2001; Sveiby 2002);

Level of resources the organisation is willing to commit towards assessment of IC (Harrison & Sullivan 2000); and

Size of organisation (O'Sullivan 2005).

The importance of above factors in selecting an appropriate method for assessment of IC in a particular context was confirmed by a survey performed by Pretorius and Coetzee (2009). In this paper “context” is interpreted as a vector comprised of factors such as these listed above, them being viewed as variables to the process of selecting and customising an appropriate method for assessment of innovation‐related IC, given any particular context.

3. Methodology In this section we introduce the research question and objectives and the research design.

3.1 Research question and objectives We have taken the research question to be as formulated in Figure 2. Which indicators could be used to assess the innovation related intellectual capital of organisations? Figure 2: Research question Corresponding to this research question, we have taken the objectives of this research to:

Identify possible indicators that could be used to assess the innovation related IC of organisations.

Explore possibilities for evaluating the suitability of these indicators for predicting innovation performance.

3.2 Research design This explorative research employs a literature review to identify possible indicators for assessment of IC and in particular innovation related IC and to seek propositions related to the evaluation of the suitability of these indicators for predicting innovation performance. Literature is also scanned for suggestions on evaluating the suitability of these indicators for predicting innovation performance.

4. Indicators for assessment of IC and innovation related IC This section explores literature on IC and on innovation to identify possible indicators (or types of indicators) for assessment of innovation related IC. These indicators span across the major categories of IC, including

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Agnieta Pretorius human capital, relational capital and structural capital. Note that factors proposed in literature (not reflecting level of achievement) that could potentially be used to derive indicators (reflecting level of achievement) for assessment of innovation related IC are included in the discussion.

4.1 Indicators proposed by literature on IC A number of scorecard‐type methods for assessment of IC are proposed in IC literature. Scorecard‐type methods make use of indicators to assess organisational performance. “First generation practices” for assessment of IC provide information on single components or forms of IC, whereas “second generation” practices strive to combine individual indicators into a single index and to correlate fluctuations in IC with changes in the market (Roos, Roos, Dragonetti & Edvinsson 1997). Scorecard‐type methods for assessment of IC include the Intellectual Capital Index (IC Index), Skandia Navigator, Balanced Scorecard, Intellectual Capital Report for Universities (ICU Report), Regional Intellectual Capital Index (RICI), Intellectual asset‐based management (IAbM), National Intellectual Capital, Topplinjen/Business IQ, Public sector IC, Danish guidelines, IC‐dVAL, Intellectus model, IC Rating, Value Chain Scoreboard, Meritum guidelines, Intangible assets statement, Knowledge Audit Cycle, Value Creation Index (VCI), Holistic Accounts and Intangible Asset Monitor (Sveiby 2010). The Skandia navigator, as an example of a scorecard‐type method for assessment of IC, incorporates a large number of indicators (also referred to as metrics) to measure IC and its derivatives for the five focus areas (refer to Figure 1), namely customer, human, renewal and development and process capital (Edvinsson & Malone 1997, Bontis 2001; Housel & Bell 2001). A sample of these can be seen in table 1. Note that such indicators are typically derived from the key success factors of an organisation and would therefore differ in different contexts and for different organisations. Further research could be performed to determine which of the metrics/indicators proposed by the Skandia Navigator (and other scorecard‐type methods for assessment of IC) display significant correlations with the innovation performance and with organisational performance and in which contexts. Table 1: Skandia Navigator: A sample of measures of intellectual capital: Adapted from Edvinsson and Malone (1997) and Housel and Bell (2001) Skandia Navigator: A sample of measures of Intellectual Capital (and its derivatives) Focus Area Financial

Measure Total assets ($)

Revenues/total assets (%)

Lost business revenues compared to market average (%) Revenues from new customers/total revenues (%) Market value ($)

Profits/total assets (%)

Return on net asset value (%)

Revenues resulting from new business operations ($) Profits resulting from new business operations ($) Revenues/employee ($)

Return on net assets resulting from new business operations ($) Value added/employee ($)

Market share (%)

Field salespeople (#)

Annual sales/customer ($) Customers lost (#)

Average time from customer contact to sales response (#) Sales closed/sales contact (%)

Average duration of customer relationship (#)

Satisfied customer index (%)

Average customer size ($)

IT investment/salesperson ($)

Customer rating (%) Customer visits to the company (#)

IT investment/service and support employees ($) Support expense/customer ($)

Days visiting customers (#)

Service expense/customer/year ($)

Total assets/employee ($)

Customer

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Value added/IT employees ($)

Agnieta Pretorius Skandia Navigator: A sample of measures of Intellectual Capital (and its derivatives) Focus Area Human

Measure Leadership index (#) Motivation index (#) Empowerment index (#) Number of employees (#) Employee turnover (%)

Number of woman managers (#) Average age of employees (#) Share of employees less than 40 years old (%)

Competence development expense/employee ($)

Average years with company of full‐time temporary employees (#) Per capita annual cost of training and support programs for full‐time temporary employees ($) Company managers with advanced degrees: business (%), science and engineering (%), liberal arts (%) Average customer duration with company (months) (#)

Satisfied employee index (#)

Training investment/customer ($)

Share of development hours (%)

New market development investment ($)

Employee’s view (empowerment index) (#)

Industry development investment ($)

R & D expense/administrative expense (%)

Value of EDI system ($)

Training expense/administrative expense (%)

Capacity of EDI system (#)

Business development expense/administrative expense (%) Share of employees below age 40 (%) IT development expense/IT expense (%)

Ratio of new products (less than 2 years old) to full company catalogue (%) Ratio of new products (less than 2 years old) to product family (%) R & D invested in basic research (%)

IT expenses on training/IT expense (%0

R & D invested in product design (%)

R & D resources/total resources (%)

R & D invested in processes

Average customer age (#)

Average age of company patents (#)

Average customer education (#)

Patents pending (#)

Administrative expense/total revenues (%)

Administrative expense/gross premium (%)

Processing time, outpayments (#)

Change in IT inventory ($)

Contracts filed without error (#)

Corporate quality goal (#)

Function points/employee‐month (#)

Corporate performance/quality goal (%)

PCs/employee (#)

Discontinued IT inventory/IT inventory (%)

Laptops/employee (#)

Orphan IT inventory/IT inventory (%)

Administrative expense/employee ($)

IT capacity/employee (#)

IT expense/employee ($)

IT performance/employee (#)

Time in training (days/year) (#)

Number of women directors (#)

Renewal and Development

Process

Number of full‐time or permanent employees (#) Average age of full‐time or permanent employees (#) Average years with company of full‐time or permanent employees (#) Annual turnover of full‐time permanent employees (#) Per Capita annual cost of training, communication, and support programs for full‐ time permanent employees ($) Per capita annual cost of training, communication, and support programs ($) Number of full‐time temporary employees (#)

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4.2 Indicators proposed by recent literature on innovation This section presents a selection of recent literature on innovation proposing indicators for assessment of innovative capacity. The Emerald (emaraldinsight.com) database was scanned for journal papers where the title of the article contains the term “innovation”. 2037 records were retrieved. This paper reports on a content analysis performed on the most recent 100 of these articles (to keep this paper within the prescribed size, but still be able to provide a suitable level of detail). 41 of these articles proposed indicators that could be used to assess innovative capacity. From these 41 articles (refer to the contextual dimensions introduced in Section 5.1) those with the “purpose of or motivation for assessment” to assess or develop intellectual capital, a “level of assessment” of the organisation or business unit and where at least one of the following contextual factors are specified: “industry or line of business”, “business sector” or “size of organisation are shown in Table 2 below. Table 2: Indicators of innovative capacity proposed by recent literature on innovation

Business Sector

Size

Industry or line of business Ceramic tile

SME

Country of origin Italy Spain

Number of organisations studied 181

Financial

Taiwan

118

Health care

Taiwan

112

Higher Education

Australia

1 business unit of 1 organisation

Integrated staffing approach nurturing innovation and skills development

Higher Education

Knowledge management practices

Hi‐Tech

China

1 business unit of 1 organisation 159

Knowledge intensive business services

Spain

154

Pre‐dispositioning to new service co‐creation

Manufacturi ng

China

345

Manufacturi ng

Japan

(Toyota)

R&D partnerships (with private firms and government institutions) Structured processes, in particular relating to scanning, idea occurrence, strategy relating to formulation, resource procurement, implementation and value creation

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Indicators

Source

Organisational learning capabilities (leading to design management capability)

Fernández‐ Mesa, Alegre‐ Vidal, Chiva – Gómez & Gutiérrez‐ Gracia (2013) Tsou (2012)

Collaboration competency Partner match Knowledge integration mechanisms (KIMS) ICT Competencies

Paternalistic leadership

Sheng, Chang, Teo & Lin (2013) Sparks, O’Brien, Richardson, Wolski, Tadic & Morris (2013) He & Abdous (2013) Fu, Li & Si (2013) Santos‐ Vijande, González‐ Mieres, Ángel & López‐ Sánchez (2013) Azasegan, Napshin & Oke (2013) Ota, Hazama & Samson (2013)

Agnieta Pretorius

1 0

Industry or line of business Medum and Hi‐Tech

Business Sector

Size

Country of origin

> 50 empl oyees

Spain (75)

Number of organisations studied 144

1 1

R&D

Columbia (69) Japan

1 2

Services

India

1 3 1 4

Services

Taiwan

179

Services (Tourism)

Greece

Jordan

358

1 5

Public

125

1 6

SME

Russia

192

1 7

Famil y owne d busin ess SME

500

Pakistan

124

SME

Hungary

814

1 8

1 9

345

Indicators

Source

Knowledge sharing

Sáenz, Arambur & Bianco (2012)

Long term managerial influences Knowledge exploitation resources Customer interaction (in new service development) Customer Involvement

Zelaya‐Zamora & Senoo (2013)

Social capital (approximated by active and purposeful engagement in network alliances) Transformational leadership (individualised consideration, intellectual stimulation, idealised influence and inspirational motivation) Place of work Collaborative activities of clustering organisations Expectations concerning institutional conditions on the macro level Environment Innovation strategy Family involvement Owner’s background Learning Three dimensions of entrepreneurship, namely risk‐taking, pro‐activeness and autonomy Encouraging external environment (government and EU should support SMEs’ innovation) Supporting internal environment (SME’s: learning and learning culture; knowledge creation; building of a learning culture)

Alam (2013)

Cheng, Chen & Tsou (2012) Petrou & Daskalopoulou (2013)

Nusair, Ababneh & Bae (2012)

Bek, Bek, Sheresheva & Johnston (2013)

Laforet (2013)

Ndubisi & Iftikhar (2012)

Csath (2012)

Agnieta Pretorius

5. Linking innovation related IC with innovation performance and company performance Various indicators – as well as factors that could be used to derive possible indicators – potentially relevant to the assessment of innovation related IC (also referred to as innovative capacity or innovative capabilities) were introduced in Section 4. Subramania and Youndt (2005) point out that the intricacies of how organisational knowledge gets accumulated and utilised remain untied to the specific types of innovative capabilities that exist in organisations and that the majority of studies link these capacities only to broadly defined innovation outcomes (such as new product introductions, technology patents and sales generated from new products.). One approach for evaluating the suitability (relevance, importance and significance) of these indicators (of innovation related IC/innovative capacity/innovative capabilities), as proposed by Lazzarotti, Manzini and Pellegrini (2011), is to analyse the extent and significance of correlation with innovation performance and subsequently the extent and correlation of innovation performance with organisational performance. Lazzarotti, Manzini and Pellegrini (2011) utilise the following factors/indicators of “innovation performance”:

The company’s competence base was enlarged;

The average development costs of new products/processes were reduced;

The time to market of new products/processes was reduced;

The level of innovativeness of new products/processes was improved; and

Sales volume and market acceptance of new products was improved.

Similarly, Marques and Ferreira (2009) utilse the following factors/indicators (referred to as “sub‐variables”) for an organisation’s innovative performance:

Product innovation;

Process innovation;

R&D investment; and

The use of new distribution channels. In their study multiple regression techniques are used to establish which of these sub‐variables best explains organisation’s improved performance and, consequently, creation of competitive advantage.

Possible factors/indicators for the notion of organisational performance include turnover/sales, net profit/loss, success of new products, and perception of results (Marques and Ferreira, 2009).

6. Summary and conclusion Literature on IC and on innovation were explored to identify possible indicators for innovation related IC, also referred to as innovative capacity. Such indicators are context dependent and furthermore need to be customised for individual organisations, taking into account the goals, objectives and key success factors of the individual organisations. The relevance, importance and significance of indicators proposed for assessment of innovation related IC need to be evaluated. This could be done, for example, by analysing the extent and significance of the correlation of such indicators with innovation performance and subsequently the extent and correlation of the indicators of innovation performance with organisational performance. This research gives an indication of the extent to which existing literature could be useful in determining indicators for assessment of innovation related IC and is expected to form the basis for future research towards determining appropriate indicators for assessment of innovation related IC in various contexts. These indicators are also expected to assist in pin‐pointing development areas for promotion of innovation. Further research possibilities include:

A larger scale, more comprehensive review and analysis of literature proposing factors and/or indicators for assessment of IC, innovation related IC and innovative capacity.

A literature review focussing on a particular industry or line of business.

Evaluation of factors/indicators for assessment of IC, innovation related IC/innovative capacity (retrieved, derived or adapted from literature) for suitability for predicting innovation performance.

Prioritisation of indicators for assessment of innovation related IC/innovative capacity.

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Investigating the links between specific narrowly‐defined types of innovation related IC/innovative capacity and specific narrowly defined aspects of innovation performance and organisational performance.

The design, implementation and testing of a support system for assisting consultants, practitioners and researchers in selecting, customising and comparing methods and indicators for assessment of innovation related IC/innovative capacity.

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Voluntary Sector Organisations: Untapped Sources of Lessons for Knowledge Management Gillian Ragsdell Loughborough University, Leicestershire, UK g.ragsdell@lboro.ac.uk Abstract: Voluntary sector organisations are an important element of UK society and its economy. Since 1999 the number of charitable organisations in the UK has remained relatively stable at around 162 000; the annual gross income of these organisations has continually increased from £24 billion in 1999 to £56 billion in 2011 (based on Charity Commission figures). To continue with the statistical theme, it is estimated that around 25% of adults in England and Wales formally volunteer at least once a month (www.thirdsector.co.uk). With an emphasis on the development of the Big Society in the UK, and concepts of empowerment, localism and participation being fundamental to its success, there is much that can be learned from volunteer‐led organisations for the contemporary social agenda. Hence, one might expect a noticeable increase in the amount of research undertaken in the voluntary sector in coming years; the knowledge management (KM) research agenda is one that could be shaped and informed by this sector. Voluntary sector organisations are different from those in the private and public sectors where, to date, many of the KM studies have been undertaken. They have particular characteristics that can present extreme challenges for managing knowledge therein. For example, the transient nature of volunteer workers means that knowledge retention can be difficult; the lack of opportunity to plan strategically, due to financial insecurity, can prevent long term investment in KM initiatives that require technological support; and the lack of formal contracts for volunteers means that knowledge activities are not determined by job descriptions and formal rewards. The other side of the coin reveals a set of characteristics that can support effective KM practices. For example, volunteers operate within a particular ethos that can trigger the development of a wide range of types of trust that are not obvious in the workplace setting; additionally their behaviour with respect to knowledge is likely to be influenced by motivators that are not common in a place of employment. Given the current economic situation, more private and public sector organisations are struggling to retain their workforce and to secure budgets for new projects – their overarching organisational struggles are aligning more with longstanding ones of the voluntary sector. Since organisations in the voluntary sector have experience of overcoming such challenges it would seem reasonable to suggest that there is much to be learned from voluntary sector organisations per se but, in terms of this contribution, with respect to their KM practices. Keywords: knowledge management, voluntary sector, research agenda

1. Introduction Most knowledge management (KM) research has been undertaken in the public and private sectors. The plethora of literature includes early examples of private sector studies from Graham and Pizzo (1996) through to Gibbert et al (2011), and Syed‐Ikhsan and Rowland’s (2004) and Riege and Lindsay’s (2006) work in the public sector. Research in these two sectors has produced useful insights. However, there is a missing third context from which KM insights have not been generated. To date, KM research has not taken equal account of experiences in the voluntary sector (often termed the ‘third sector’ but this is not an expression that the author chooses to use). Knowledge management has been somewhat invisible in the voluntary sector; perhaps the motivation of private sector organisations to gain competitive advantage through KM and of public sector organisations to drive down costs by improving efficiency has raised the profile of KM therein. There is evidence of some interest in voluntary sector KM (e.g. Lettieri et al, 2004; Hurley and Green, 2005; Hume and Hume, 2008; Ragsdell, 2009; Ragsdell and Jepson, 2013; Ragsdell et al, 2013; Stadler et al, 2013) but it certainly does not mirror the massive interest shown in the other two sectors. So, in relative terms, KM research in the voluntary sector is an, as yet, unexplored and unexploited contribution, and one that, this paper argues, is invaluable to progress understanding about the management of knowledge. The dynamic set of relationships that voluntary sector organisations operate within, triggered by factors such as funding, staffing and governance, means that managing voluntary sector organisations is complex (Fotler, 1981). Their high level of complexity also means that voluntary sector organisations are rich contexts to explore and fertile arenas from which to generate organisational lessons; yet they seem to have been overlooked. As such, voluntary sector organisations remain untapped sources of lessons for KM. This paper intends to raise awareness of the contribution that the voluntary sector could make to inform the development of KM theories and practices, and to trigger debate about such a contribution. Thus, the central proposition is that many of the characteristics of voluntary sector organisations are extremes of those

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Gillian Ragsdell characteristics which have challenged and supported KM in the private and public sectors. As such, the ways in which the voluntary sector overcomes and exploits its attributes for managing knowledge, afford the opportunity to generate lessons for managing knowledge in less extreme scenarios such as those often seen in the private and public sector. Recent changes in the UK and international economic climate are stimulating changes in private and public sector organisations to bring challenges that are akin to those long‐experienced in the voluntary sector. This closer alignment with the voluntary sector supports the transferability of lessons and strengthens the proposition asserted herein. It is not anticipated that this proposition will be without its critics; they will, perhaps, argue that the different sectors reflect very different types of organisations and that they should be treated discretely. However, this is not a position that is firmly held in the literature on public and private sector organisations so far; it is quite the contrary in fact, with comparisons being made and lessons being transferred across sectors (McAdam and Reid, 2000; Bate and Robert, 2002, Bouthillier and Shearer, 2002). Reservations about researching in and with the voluntary sector per se may also be expressed; for example, the lack of a permanent workplace and the irregular hours of operation could prove obstacles to researcher. The same characteristics that influence KM activities are, ironically, likely to influence the feasibility of research activities but that is a topic that warrants its own discussion. So, this paper draws attention to four key features that generally set voluntary sector organisations apart from organisations in the other two sectors – putting to one side the differing organisational purposes, these distinguishing features are related to finances, work force, culture and infrastructure – and that might shape a KM research agenda in the untapped sector. Each feature is taken in turn (in no order of priority) and discussed with respect to how it impacts on KM practice in the voluntary sector; the linearity is for clarity only and interconnections between features are not being dismissed by this style of presentation. Although the author draws from the aforementioned limited literature on KM in the voluntary sector, there is some irony that academic studies from the private and public sectors represent the main supporting literature – a necessary approach given the position that is being put forward.

2. Finances The voluntary sector is notorious for operating in an environment of financial turbulence and uncertainty. It is therefore not surprising that the financial model for voluntary sector organisations is a major influence on their behaviour. New understanding of funding is continuously sought as the financial environment changes (Gronbjerg, 1991; Clifford et al 2010; Phillips and Hebb, 2010). Nonetheless, with three potential income streams (grants, commercial activities and private donations (Teruyo, 2010)) each suffering from increasing competition, activities to generate funds remain an essential element of the pattern of work.

2.1 Financial Insecurity and KM strategy The lack of stability in finances can mean that long term planning is not an option for voluntary sector organisations; this precludes the ability to adopt a strategic approach to KM. So, while a string of academic work focusses on the development and implementation of a KM strategy (e.g. Earl, 2001; Choi and Lee, 2002; Haggie and Kingston, 2003; Raub and von Wittich, 2004; Choi et al, 2008) and renowned KM contributors assert that “Knowledge management is a strategic activity. It starts with strategy and ends with strategy” Wenger (2004, p3), the question arises as to how the voluntary sector manages its knowledge without an explicit strategic stance. Good practices of KM have been seen in not‐for–profit event management (Ragsdell et al, 2013; Ragsdell and Jepson, 2013); these have emerged from a piecemeal approach and so one might claim that there can be ‘successes’ in the absence a holistic approach. Indeed, while the ‘bottom up’ approach might not be one that is promoted in the literature, it might suit the ethos of the voluntary sector and accommodate the absence of financial stability quite well. In time, the individual KM initiatives might morph into a strategy; perhaps the voluntary sector starts at a different point on Wenger’s (2004) ‘doughnut’ of KM. No doubt the lack of a strategic outlook from the outset brings limitations too; the balance between the permanent benefits of a ‘top down’ strategic approach and the ‘quick wins’ of a ‘bottom up’ piecemeal approach to KM might be the first item for a new research agenda in KM.

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2.2 Financial restrictions and technology Discussion of the impact of a restricted and unpredictable income to the voluntary sector is continued as the level of emphasis on technology in KM activities comes into focus. If the drivers for KM are plotted, it will be seen that, as the ‘hard’ paradigm dominated the early years and ‘solutions’ were sought to KM ‘problems’, information technology (IT) was often resorted to (Borghoff and Pareschi, 1997; Marwick, 2001; Bose and Sugumaran, 2003;). More recently a ‘softer’ paradigm has been encouraged and attention has, increasingly, been paid to cultural, social and human factors to support effective KM with, for example, Soliman and Spooner (2000) and Smith and McLaughlin (2004) suggesting a more people‐centric approach to KM. So, while IT in KM activities might be viewed with a more humanistic lens, it still plays an important role and ‘new’ technologies such as social media have entered the frame to actively influence KM behaviour (Wasko and Faraj, 2005). Yet, the latest technology may be out of reach of voluntary sector organisations’ budgets and may not ‘fit’ with their way of working. There have been few studies into the role of IT, in general, in the voluntary sector but those that have been undertaken (e.g. Boyle et al, 1993) indicate that the IT needs of this sector are quite different from those of commercial organisations. This was certainly seen in the case of knowledge sharing (Ragsdell, 2009) where “the spotlight was not on technology”. Instead there was a greater emphasis on structural, procedural and cultural factors to support the sharing of knowledge. As hardware and software becomes more affordable, there is an argument that the voluntary sector will become more akin to the private and public sectors in its use of technology to support KM; in turn the voluntary sector might need to learn from experiences of its counterparts in the other two sectors. However, there is a counter argument suggesting that, as the complexities of the ‘softer’ KM movement come under closer scrutiny in a future research agenda and as IT budgets are cut in other sectors, the voluntary sector will be in a better position to answer questions related to the issues that fall under this heading.

3. Workforce A distinct feature of the voluntary sector is the heterogeneous profile of its ‘workforce’; the make‐up of the pool of volunteers sets voluntary sector organisations apart from the private and public sectors. A range of characteristics is reflected in the people who give of their time and skills to contribute to the aims of voluntary sector organisations. Coupled with the ephemeral quality of the volunteer group, these characteristics trigger another facet which might inform KM research and practice.

3.1 Heterogeneity of volunteers The heterogeneity of the volunteer community is recognised by researchers such as Reed and Selbee (2000; 2002); this includes the heterogeneous nature of the volunteer group with respect to their motivations for being involved, their assorted demographics and the variety of abilities that they bring to the sector. In the case study festivals that were explored by Ragsdell et al (2013), the overarching motivations of volunteers were very similar – ‘to do the job well’ – and this inspired a positive attitude in their knowledge sharing behaviour. This was in spite of the vast differences in age and in abilities of the volunteers. The scenario that has been described in the context of volunteer‐led festivals may be atypical and, indeed, the unequivocal interest in volunteers’ motivations continues as shown in the continuing chronology of example studies: Clary et al, 1992; Clary et al, 1998; Serafino, 2001; Okun and Shultz, 2003; Dolnicar and Randle, 2007; Millette and Gagne, 2008; Caldarella et al, 2010. Nonetheless, the absence of influences such as a political agenda, the lack of competition triggered by the pursuit of promotion and salary increases, and the pride taken in their contribution seemed to generate a set of motivations amongst the volunteers that are not as obvious in the private and public sectors. Explorations into (re)kindling this type of motivator could form another item on the KM research agenda.

3.2 Transient nature of volunteering The ephemeral nature of the volunteer group is symptomatic of the volunteering process often being additional to volunteers’ employment and supplementary to other commitments. Sometimes volunteers engage and disengage with organisations in an arbitrary manner that creates challenges for managing knowledge. The transience of volunteers spawns particular problems for knowledge capture and retention. With downsizing regularly featuring as a way for private and public sector organisations to weather the

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Gillian Ragsdell economic storms, the transient nature of their workforce is akin to the movements experienced in the voluntary sector. So, while research has focussed on knowledge capture amongst retirees and the ‘grey workforce’, (e.g. Liebowitz, 2003; Lampl et al, 2004; Joe and Yoong, 2006; Slagter, 2007), the voluntary sector has not yet been recognised as a place in which this aspect might be further investigated.

4. Culture As stated earlier, there has been an increasing emphasis on KM’s ‘softer’ dimensions and, in line with this, there have been regular academic contributions based on cultural aspects (Sackmann, 1992; Banks, 1999; De Long and Fahey, 2000; Oliver and Kandadi, 2006). The features mentioned in earlier sections contribute to a particular organisational culture in which volunteers operate. They are both influenced by this culture and influence it; it would seem to be an essential ingredient to support effective KM. In the study of festivals led by volunteers (Ragsdell et al, 2013), the dominant culture was based on a genuine willingness and enthusiasm to share knowledge. Volunteers took great pride in undertaking their allocated responsibilities and sharing their experience; there was no question of seeking rewards for doing so (Bock and Kim, 2002; Bartol and Srivastava, 2000). Sometimes contracted employment can bring competition and pressure to meet individual targets; the high levels of trust exhibited in the festivals and the relaxed environment in which festival experience was shared are unlikely to be engendered in those conditions. So, it is put forward that the process of emulating the festival environment highlighted in Ragsdell et al (2013), and its transfer into private and public sector organisations, might be investigated so as to promote a knowledge culture (Oliver and Kandadi, 2006).

5. Infrastructure The set‐up of some voluntary sector organisations can lend themselves to provoking additional learning about managing knowledge in contemporary organisational arrangements. For instance, it is not uncommon for some organisations in this sector to operate with no physical head office or local permanent base. Similarly they may well operate with significant levels of autonomy, and with little or no centralised administrative or technological support. And there is unlikely to be the equivalent of a Chief Knowledge Officer (Earl and Scott, 1999). This was the case for the participants in the study conducted by Ragsdell et al (2013). In addition, the time that they could dedicate to volunteering was restricted due to work commitments causing irregular attendance at festival planning meetings and, in turn, making it difficult to synchronise communications. The above factors (and more) meant that the volunteers were not working in a traditional team setting (Webster and Wong, 2008) and managing knowledge in an orthodox structure. Rather, the volunteers were managing the KM challenges that working in a virtual team and in a global team can bring. This feature of the voluntary sector organisation then brings the final item to a KM research agenda.

6. Summary This paper advocates for more KM research to be undertaken in the voluntary sector so as to address the current imbalance that the massive academic interest in KM in the private and public sectors has created. Four key features that have, in the past, distinguished voluntary sector organisations from private and public sector organisations, were taken in turn to discuss the challenges and opportunities they present for KM in the voluntary sector (albeit with the acknowledgement that there are exceptions to this generalisation). It is recognised, however, that, although voluntary sector organisations have operated at the extremes of these characteristics, the current economic climate is moving some private and public sector organisations to align more with the voluntary sector operating context. Thus, it is put forward that it is becoming more important for researchers to study the voluntary sector and generate lessons from it. KM researchers are no exception and this paper has achieved its aim if the proposed research agenda developed herein triggers a debate about the potential contributions from this arena.

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Proposal of Indicators for Reporting on Intellectual Capital in Universities Yolanda Ramírez, Ángel Tejada and Agustín Baidez University of Castilla‐La Mancha, Albacete, Spain Yolanda.Ramirez@uclm.es Angel.Tejada@uclm.es Agustin.Baidez@uclm.es Abstract: There is a growing interest in applying an intellectual capital approach in universities, since knowledge is the main output and input of these institutions. Universities produce knowledge, either through technical and scientific research (the results of investigation, publications, etc.) or through teaching (students trained and productive relationships with their stakeholders). Their most valuable resources also include their teachers, researchers, administration and service staff, university governors and students, with all their organisational relationships and routines. Furthermore, necessities like the increasing stakeholder demand for greater transparency, the increasing competition between universities and firms, and greater autonomy, push universities towards the adoption of new reporting systems which should necessarily incorporate intangibles. This paper has two purposes: to determine the extent to which university stakeholders are interested in having information relating to the intellectual capital; and to propose a battery of indicators for measuring and reporting intellectual capital in Spanish universities. In this study we developed a questionnaire which was sent to members of the Social Councils of Spanish public universities in order to identify which intangible elements university stakeholders demand most. Our study’s results served as basis to develop a battery of indicators which allows these intangible elements to be measured. The results of our empirical study show that the respondents consider it essential that universities provide information on intellectual capital. These results allow us to recommend extending the limits of universities’ financial statements so as to include the information on different intangible elements demanded by the different stakeholders. Furthermore, our proposal is an attempt at the standardization of indicators for measuring and reporting the universities’ intellectual capital. Our proposal of indicators aims to provide guidelines to help universities on the path to presenting information which is useful to their stakeholders, contributing to a greater transparency, accountability and comparability in the higher education sector. Keywords: intellectual capital, stakeholders, proposal, indicators, higher education institutions

1. Introduction There is a growing interest in applying an intellectual capital approach in universities, since knowledge is the main output and input of these institutions (Leitner and Warden, 2004; Sánchez et al., 2009; Brătianu, 2009; Rafiee et al., 2010). Universities produce knowledge, either through technical and scientific research (the results of investigation, publications, etc.) or through teaching (students trained and productive relationships with their stakeholders). Their most valuable resources include their teachers, researchers, administration and service staff, university governors and students, with all their organisational relationships and routines (Warden, 2003; Leitner, 2004; Ramírez et al., 2007). The higher education institutions are, therefore, an ideal framework for the application of the ideas related to intellectual capital theory. Furthermore, necessities like the increasing stakeholder demand for greater transparency, the increasing competition between universities and firms, and greater autonomy, push universities towards the adoption of new reporting systems which should necessarily incorporate intangibles (Sánchez et al., 2009). This paper focuses on the importance of reporting on intellectual capital for Spanish universities and the information needs of university stakeholders. Numerous papers and books have come to the conclusion that our traditional accounting systems do not suffice for today’s organisations, whose value creation often depends more on intellectual capital type resources rather than monetary or physical resources (Burgman et al., 2007). However, the information provided by public universities focuses on ensuring financial control of the organisation without paying attention to the needs of other groups of interest. In this sense, Gray (2006) considers that the information supplied in traditional financial reports is not enough. He highlights the need to establish more extensive communication and accounting mechanisms which take into account the needs of the different groups of interest. Coy et al. (2001) recommend extending the limits of US universities’ annual accounts and defend a new paradigm for the annual accounts which provides more wide‐ranging information on teaching and

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Yolanda Ramírez, Ángel Tejada and Agustín Baidez research. They favour the inclusion of effort indicators and achievements, with more attention being paid to the social responsibility of institutions of higher education. Consequently, the current socio‐economic climate creates the need for universities’ financial statements provide all the relevant information on their activities and the key factors of their success –their intangible resources‐. In this study we will show the opinion which exists among the university stakeholders regarding the need to complete the content of the current university financial statements by providing non‐financial information on intellectual capital. Based on this empirical study, we define a battery of indicators for reporting on intellectual capital in universities. The paper is structured as follows: in section 2, we explore the concept of intellectual capital in higher education institutions. In section 3, we define the scope of the empirical study conducted and the methodology used. Then, we present the results obtained. According to this, our proposal of indicators for reporting on intellectual capital in universities is presented. Final conclusions are drawn in Section 4.

2. Intellectual capital in higher education institutions Specifically, when referred to a university, the term intellectual capital is used to cover all the institution’s non‐ tangible or non‐physical assets, including processes, capacity for innovation, patents, the tacit knowledge of its members and their abilities, talents and skills, the recognition of society, its network of collaborators and contacts, etc. The components of a university’s intellectual capital have been categorized in diverse ways, although undoubtedly, the tripartite classification is the most widely accepted in specialized literature (Leitner, 2004; Ramírez et al., 2007; Cañibano and Sánchez, 2008; Sánchez et al., 2009; Bezhani, 2010; Bodnár et al., 2010). Intellectual capital is represented as being formed by the following three basic and closely interrelated components:

Human Capital: it is the sum of the explicit and tacit knowledge of the university staff (teachers, researchers, managers, administration and service staff), acquired through formal and non‐formal education and refresher processes included in their activities.

Structural Capital: it is the explicit knowledge relating to the internal processes of dissemination, communication and management of the scientific and technical knowledge at the university.

Relational Capital: this refers to the extensive collection of economic, political and institutional relations developed and upheld between the university and its non‐academic partners: enterprises, non‐profit organisations, local government and society in general. It also includes the perception that others have of the university: its image, appeal, reliability, etc.

Current accounting regulations restrict the recognition of intangibles. Only acquired intangible assets may be reflected in an organisation’s balance sheet (Ramírez et al., 2007). For this reason, there are numerous international regulatory bodies, agencies and academic institutions that aware of the difficulty of incorporating intellectual capital into the balance, tend to recommend the development and presentation of the so‐called Intellectual Capital Reports. Intellectual capital reports contain a set of indicators that contribute to improving the quality of accounting information in organisations. In this line, at Spanish level, the Commission of Accounting Experts of Ministry of Economy (ICAC, 2002) recommends the voluntary drafting and publication of a report on intellectual capital by following the guidelines of the Meritum Project (Cañibano et al., 2002), consisting of three parts: a vision of the company, a summary of intangible resources and activities and a system of indicators. Taking these considerations into account, we believe that complementary non‐financial information is the most appropriate form to supply information on universities’ non‐tangible elements, so as to avoid the inclusion of accounting criteria which could endanger the quality and reliability of the financial information. In our opinion, an improvement in university accounting systems would be achieved by the drafting and presentation of a new report complementary to the current financial statements –the Intellectual Capital Report‐. A set of indicators would show the information most demanded by different stakeholders regarding the institution’s intangible resources.

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3. Empirical study The generalised concern regarding the need to guarantee the information transparency of universities led us to consider the need to include information on intellectual capital in universities’ annual reports. To this end the decision was taken to seek out the opinion of the university stakeholders regarding the importance they give to completing the information from university financial statements with information relating to these institutions’ intellectual capital.

3.1 Research objectives The two fundamental objectives of the empirical study are:

Objective I: To determine the extent to which different university stakeholders are demanding information relating to the intellectual capital of Spanish public universities in order to make the right decisions, identifying which intangible resources are the most relevant for publication.

Objective II: To propose a battery of indicators for reporting on intellectual capital in Spanish universities.

3.2 Methodology and data collection In order to achieve the previously mentioned objectives, in mid‐May 2010 an online questionnaire requesting the opinion of the members of the Social Councils was sent to all Spanish public universities. The questionnaire consists of 5‐point Likert scale questions. 3.2.1 Defining the population and selecting the sample Two important factors were used to justify the population to be studied: (1) members of the Social Councils of Spanish public universities were considered to provide a good sample of the feelings of university stakeholders, as they represent the various social groups with links to the universities: university governors, students, teaching and research staff, administration and services staff, union organizations, business organizations, and public administrations (2) these members are familiar with the accounting information published by the universities since they are responsible for approving the universities’ annual accounts. The population to be studied was therefore composed of the 1.904 members of the Social Councils of Spanish public universities. Replies were received from 247 members, 22.57% of the total. The size of the sample was considered sufficient, since in a binomial population the estimation error would be 5.37% for a reliability level of 95%. 3.2.2 Information collection and treatment The information was collected via an online survey. An email was sent to the members of the Spanish university Social Councils requesting the members to take part in our research. The questionnaire consists of closed dichotomous questions combined with Likert scales, designed to learn the opinion of university stakeholders on the importance of Spanish public universities publishing information on their intellectual capital. A list of intangible elements relating to human capital, structural capital, and relational capital is included so as to ascertain to what degree it is relevant to publish this information. Specifically, we proposed 32 intangible elements according to the characteristics of the higher education institutions, in order to establish their relevance for disclosing. Twelve were related to human capital (concerning the abilities and skills of the people belonging to the institutions), fourteen were related to structural capital (referring to how the institution is structured and how it works), and sixteen were related to relational capital (the institution’s relations with students and the outside world). A descriptive analysis of the replies was conducted according to the characteristics of each of the questions.

3.3 Analysis of the results of the empirical study There now follows a consideration of the principal results obtained through the empirical study for each of the objectives previously established.

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Yolanda Ramírez, Ángel Tejada and Agustín Baidez 3.3.1 Objective 1: The importance given by university stakeholders to the presentation of information on intellectual capital In order to identify the intangible elements about which university stakeholders consider it relevant or very relevant to publish information, we set as a requirement that these elements had to reach a mean value or a median equal or higher than 4 points in combination with a minimum 25 of 4 points and a minimum 75 percentile of 5 points. In short, the intention is that most of the distribution of values is concentrated in high scores close to 5 points. It was also considered that in order to classify any of the intangible items as essential to publish, apart from meeting the previous requirements, they must achieve a mean value of over above 4.5. Table 1 shows the frequencies obtained in the study (mean, median, standard deviation, and percentile 25 and 75) to the different intangible elements (grouped in three categories of intellectual capital). Table 1: Intangible elements used in the study

Intangible elements

HC1

Typology university staff (historical data on increase and decrease of staffing numbers, staff age structures, type of contract, etc.) Academic and professional qualifications of teaching and research staff (% of teachers, % of civil servants, etc.) Mobility of teachers and researchers (% of teachers with fellowships, etc.) Scientific productivity (books, articles published, etc.) Professional qualifications of administration and service staff Mobility of graduate students Efficiency of human capital Teaching capacities and competences (didactic capacity, teaching innovation, teaching quality, languages, etc.) Research capacities and competences (research quality, participation in national and international projects, % of doctors, six year terms, etc.) Capacity for teamwork Leadership capacity Training activities Facilities and material resources supporting pedagogical qualification and innovation Facilities and material resources supporting research and development The institution’s assessment and qualification processes Organisational structure Teaching management and organisation (academic networks, periodical exchange with foreign teachers, teaching incentives, etc.) Research management and organisation (internal communication of results, efficient management of research projects, research incentives, theses read, etc.) Organisation of scientific, cultural and social events Productivity of the administration, academic and support services Organisation culture and values Effort in innovation and improvement (expenditure on innovation, staffing level, etc.)

HC2

HC3 HC4 HC5 HC6 HC7 HC8

HC9

HC10 HC11 HC12 SC1 SC2 SC3 SC4 SC5

SC6

SC7 SC8 SC9 SC10

Mean Median 3.66

Standard deviation 0.433

4.60

0.321

4.54

0.552

4.48

0.365

3.66

0.672

4.37 4.44 4.60

4 5 5

0.327 0.413 0.438

4 4 4

5 5 5

4.59

0.285

4.08 3.99 4.44 4.12

4 4 5 4

0.366 0.452 0.369 0.344

4 3 4 4

5 5 5 5

4.47

0.343

4.31

0.383

4.06 4.63

4 5

0.602 0.402

3 4

5 5

4.40

0.329

4.46

0.406

4.05

0.449

4.12 4.38

4 4

0.437 0.352

3 4

5 5

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Percentile 25 3

Percentile 75 4

Yolanda Ramírez, Ángel Tejada and Agustín Baidez Intangible elements Management quality Information system (document processes, databases, ITC use, etc.) Technological capacity (total expenditure on technology, availability and use of computer programmes, intranet/internet use, etc.)

4.54 4.48

5 4

Standard deviation 0.394 0.363

4.49

0.394

SC14

Intellectual property (patents, licenses, etc.)

4.58

0.358

RC1

Efficiency of graduate teaching (average duration of studies, drop‐out rate, graduation rate, etc.)

4.57

0.295

RC2

Student satisfaction

4.66

0.377

RC3

Graduate employability

4.79

0.252

RC4

Relations with students (capacity of response to students’ needs, permanent relations with graduates, etc.) Relations with the business world (spin‐offs, R&D contacts and projects, etc.)

4.29

0.359

4.79

0.271

SC11 SC12 SC13

RC5

Mean Median

Percentile 25 4 4

Percentile 75 5 5

RC6

Relations with society in general (institutional representation in external organisations, collaboration in national and international projects, etc.)

4.47

0.354

RC7

Application and dissemination of research (dissemination of results, social appropriateness of research)

4.43

0. 374

RC8

Results with the media

4.01

0.547

RC9

University’s image

4.65

0.313

RC10

Collaborations and contacts with public and private organisations

4.40

0.348

RC11

Collaboration with other universities

4.56

0.284

RC12

Strategic links

4.42

0.335

RC13

Relations with quality institutions

4.44

0.341

RC14

The regional, national and international reputation of the university

4.47

0.405

RC15

Social and cultural commitment

4.40

0.398

RC16

Environmental responsibility

4.49

0.434

Source: Compiled by the authors It must be observed that, in general, a high mean value was awarded to publishing information on intangible items relating to human, structural and relational capital, which shows a strong emphasis on the need for universities to publish information on their intellectual capital. Specifically, the analysis of the data obtained from the various statistics (mean, median, standard deviation, 25 and 75 percentiles) led to classifying the following intangible elements as essential to publish:

Human capital: academic and professional qualifications of the teaching and research staff, mobility of teachers and researchers, teaching capacities and competences and research capacities and competences.

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Structural capital: management quality, teaching management and organization, effort in innovation and improvement, and intellectual property.

Relational capital: graduate employability, efficiency of graduate teaching, relations with the business world, student satisfaction, the university’s image and collaboration with other universities.

3.3.2 Objective II: Proposal of a battery of indicators related to intellectual capital in Spanish universities. We have developed a proposal of indicators for reporting on intellectual capital based on the results of our empirical study. We also reviewed the principal literature on intellectual capital reports drawn up at different institutions of higher education and research centers (Leitner, 2004; Fazlagic, 2005; Sánchez et al., 2006, 2009; Ramírez et al., 2007; Cañibano and Sánchez, 2008; Schaffhauser, 2009; Bezhani, 2010; Bodnár et al., 2010; Silvestri y Veltri, 2011; etc.), and also we took into account various studies which bring together tables of indicators designed by different universities (Malyshko, 2008; Sánchez and Rivera, 2009; Jones et al., 2009; Nava and Mercado, 2011). Using this information and the results obtained from our empirical study we are now able to identify the key aspects that need to be included in a presentation of intellectual capital information by Spanish universities in order to satisfy the needs of stakeholders. Table 2 shows our proposal of battery of basic or general indicators which will align all the intangible elements which it is “essential” to make public. Table 2: Proposed indicators for reporting intellectual capital in universities Intangible elements Academic and professional qualifications of staff Teaching capacities and competences

Mobility of teachers and researchers Research capacities and competences

Teaching management and organisation

Management quality Effort in innovation Intellectual property

Graduate employability Efficiency of graduate teaching

Student satisfaction

Relations with the business world

Collaboration with other universities University’s Image

Indicators HUMAN CAPITAL % of doctors among teaching and research staff % of qualified teachers Total teaching and research staff / students Number of participants in training programmes Number of hours dedicated to teacher training % of teachers with fellowships at other universities Rate of participation in research projects Proportion of six‐year research periods Production of doctoral thesis Number of scientific publications STRUCTURAL CAPITAL % of classes with less than 50 students Rate first cycle credits in English Library places Quality certificates awarded R&D expenditure Number of R&D projects under development Generation of patents Scientific production RELATIONAL CAPITAL Employment rate Time until first employment Drop‐out rate Efficiency rate Graduation rate Performance rate Graduate satisfaction with studies (surveys) % of pre‐enrolled students in first option in relation to total number of places on offer Rate of in‐company work experience Evaluation of university training by employers Number of collaboration agreements on projects and activities with enterprises % of teachers received from other universities Society’s opinion of the university Doctorate programmes with official mention of quality Rate of students from foreign universities on postgraduate programmes

Source: own information

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Yolanda Ramírez, Ángel Tejada and Agustín Baidez The indicators are broken down into the respective elements of intellectual capital and comparisons will be made with previous periods and provisional information. The values of the indicators can be calculated and presented for different successive periods, which permits a time‐based comparative analysis. DESCRIPTIONS OF SELECTED INDICATORS OF INTELLECTUAL CAPITAL Below we show a few descriptive sheets of the proposed indicators for each category of intellectual capital. HUMAN CAPITAL INDICATORS: Proportion of doctors DEFINITION: Percentage relationship between the number of doctors and the total number of teaching and research staff at the university (TRS) CALCULATION: Total no. of TRS and doctors at the university ‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐ x 100 Total No. of TRS at the university Proportion of qualified teaching staff DEFINITION: Percentage relationship between the number of qualified teachers and the total number of teaching and research staff (TRS) at the university CALCULATION: No. of qualified teachers ‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐ x 100 Total no. of TRS at the university Teaching and research staff‐student ratio DEFINITION: Relationship between the total number of teaching and research staff (TRS) at the university and the total number of students. CALCULATION: No. of TRS at the university (FTE) ‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐ Total No. of students (FTE) Participants in training programmes DEFINITION: Number of participants in training programmes either in or outside the university CALCULATION: Number of teaching and research staff participating in training programmes

Hours dedicated to teacher training DEFINITION: Number of hours of teaching and research staff dedicate to teacher training activities CALCULATION: No. of hours TRS dedicate to training ‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐ x 100 Total no. of TRS daily working hours Percentage of teachers who hold/have held fellowships at other universities DEFINITION: Percentage relationship between the number of teachers who hold/have held fellowships at other universities (national or international) and the total number of teaching and research staff (TRS) at the university CALCULATION: No. of TRS who hold/have held fellowships at other universities ‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐ x 100 Total no. of TRS at the university Participation rate in research projects DEFINITION: Percentage relationship between the total number of teachers who participate in competitive scientific programmes (European Framework programmes, National or Regional Plan programmes with project assessment, etc.) and the total number of teachers. CALCULATION: Total no. of teachers who participate in research projects

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‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐ x 100 Total no. of TRS at the university

Proportion of six‐year research periods DEFINITION: Percentage relationship between the number of six‐year research periods awarded to teaching and research staff (TRS) and the total possible number of six‐year research periods CALCULATION: Total no. of six‐year research periods awarded ‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐x 100 Total no. of possible six‐year periods

Production of doctoral thesis DEFINITION: Number of doctoral theses defended in the last year in relation to the total number of teaching and research staff (TRS) on doctorate programmes CALCULATION: No. of thesis defended in the last year ‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐ x 100 Total no. of TRS on doctorate programmes

Number of scientific publications DEFINITION: Number of scientific publications in relation to number of teaching and research staff (TRS) at the university CALCULATION: Total no. of scientific publications ‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐ Total no. of TRS at the university

STRUCTURAL CAPITAL INDICATORS: Percentage of classes with fewer than 50 students DEFINITION: Percentage relationship between the total number of classes with 50 or fewer students and the total number of classes. CALCULATION: Total no. of classes with 50 or fewer students ‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐ x 100 Total no. of classes

Number Rate first cycle credits in English DEFINITION: Percentage relationship between the number of credits available in English and the total number of credits of the study plan CALCULATION: No. of credits available in English ‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐ x 100 Total no. of credits of the study plan

Places in the library DEFINITION: Relationship between the number of students enrolled and the number of reading places in the library. CALCULATION: Total no. of students enrolled ‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐ x 100 Total no. of library places

CALCULATION:

Quality certificates awarded DEFINITION: Quality certificates awarded for good management systems. Quality seals awarded.

CALCULATION:

R&D expenditure DEFINITION: Expenditure on research and development. Expenditure on R&D (in euros)

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Yolanda Ramírez, Ángel Tejada and Agustín Baidez R&D projects DEFINITION: Number of on‐going R&D projects Number of on‐going R&D projects at the university

CALCULATION:

Production of patents DEFINITION: Number of patents registered by the university in the last year Number of patents generated by the university to date

Scientific production DEFINITION: Relationship between the number of scientific documents registered in the Science Citation Index and the Social Science Citation Index in relation to the total number of teaching and research staff (TRS) at the university CALCULATION: No. of scientific publications registered in the SSCI ‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐ x 100 Total no. of TRS at the university

RELATIONAL CAPITAL INDICATORS: Employment rate DEFINITION: Percentage relationship between the number of graduates in year “x‐3” who are in a job that matches their education in year “x” and the total number of students in work in year “x” who graduated in year “x‐3” CALCULATION: No. of graduates in year “x‐3” who are working in year “x” in a job that matches their education ‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐ x 100 Total no. of graduates in year “x‐3” Time until first employment DEFINITION: Amount of time passed (in months) between graduating and finding the first job CALCULATION: ∑ months passed between graduating and finding the first job for all graduates ‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐ x 100 Total number of graduates Drop‐out rate DEFINITION: Percentage relationship between the total number of a new entry of students who should have finished the previous year of studies and have not enrolled in the final two years CALCULATION: No. of students not enrolled in the two final years “x” y “x‐1)” ‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐ x 100 No. of new entry students in the year “x‐n+1” Efficiency rate DEFINITION: Percentage relationship between the theoretical number of credits of the study plan for which the group of graduates from a certain year should have enrolled and the total number of credits for which they have actually had to enroll. CALCULATION: No. of theoretical credits of the study plan x no. of graduates ‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐ x 100 Total number of credits for which the graduates actually enrolled Graduation rate DEFINITION: Percentage of students who graduate in the time foreseen in the study plan(d) or one academic year later (d+1) in relation to their entry group CALCULATION: Graduates in “d” or in “d+1” of those enrolled in “c” ‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐ x 100 Total number of students enrolled in one academic year “c” Performance rate

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Yolanda Ramírez, Ángel Tejada and Agustín Baidez DEFINITION: Percentage relationship between the total number of credits passed (excluding adapted, transferred, recognised credits, etc.) by students and the number of credits for which they have enrolled CALCULATION: No. of credits passed by students ‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐ x 100 Total no. of credits for which they have enrolled

Graduate satisfaction with their studies DEFINITION: The mean score obtained from all the graduates surveyed when answering the following three questions: A. How do you evaluate the training received at the university? B. How appropriate is the training received to the actual requirements of your job? C. Would you study the same degree course again? At the same university? CALCULATION: ∑ scores obtained for each question ‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐ x 100 Total number of answers obtained

Percentage of pre‐enrolled students in first option DEFINITION: Relationship between the total number of students pre‐enrolled in first option and the total number of places offered by the university. CALCULATION: Total no. of students pre‐enrolled in first option in each branch ‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐ x 100 Total no. of laces offered by the university

Rate of in‐company work experience DEFINITION: Percentage relationship between the number of students on voluntary work experience in companies (with a duration of at least three months) and the total number of students enrolled CALCULATION: No. of students on work experience in companies ‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐ x 100 Total number of students enrolled

Evaluation of university training by employers DEFINITION: Mean score obtained from all the employers surveyed when answering the following three questions: A. How do you evaluate the theoretical training acquired by graduates? B. How do you evaluate the practical training acquired by graduates? C. How do you evaluate the usefulness of the competences acquired at university for the work position? CALCULATION: ∑ scores obtained for each question ‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐ x 100 Total number of answers obtained

Number of collaboration agreements on projects and activities with enterprises DEFINITION: Number of co‐operation projects signed between the university and enterprises CALCULATION: Number of co‐operation agreements with enterprises

% of teachers received from other universities DEFINITION: Percentage of teachers received from other universities in relation to the total number of TRS at our university CALCULATION: No. of teachers received from other universities ‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐ x 100 Total no. of TRS at the university

Society’s opinion of the university DEFINITION: Proportion of positive assessments received, the number of answers which give a very good or quite good assessment of the public image of the university CALCULATION: Total number of positive assessments (very good or quite good) ‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐ x 100

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Yolanda Ramírez, Ángel Tejada and Agustín Baidez Total number of answers obtained Doctorate programmes with official mention of quality DEFINITION: Number of doctorate programmes which have received the Mention of Quality awarded by the Ministry of Education and Science CALCULATION: Number of doctorate programmes with a Mention of Quality Rate of students from foreign universities on postgraduate programmes DEFINITION: Relationship between the number of students from foreign universities enrolled on post‐graduate courses and the total number of enrolments on these courses (official masters courses, doctorate programmes, university’s own courses) CALCULATION: No. of students from foreign universities on post‐graduate courses ‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐ ‐‐‐‐‐‐‐‐‐‐‐‐ x 100 Total no. of students on post‐graduate courses

4. Conclusion This paper presents a proposal of indicators for reporting on intellectual capital in Spanish universities. This involved identifying intangible elements university stakeholders demand most, which served as a basis for developing our proposal. In our opinion, universities will have to pay greater attention to their different stakeholders and their respective information interests when designing their communication strategy. The empirical study conducted for this work is a first step towards highlighting the importance given by different Spanish public universities to the need to carry out a proactive publication of information on intellectual capital. Specifically, it is considered essential the disclosure of the following intangible elements: academic and professional qualifications of the teaching and research staff, mobility of teachers and researchers, teaching capacities and competences, and research capacities and competences (Human Capital); effort in innovation and improvement, teaching management and organization, intellectual property, and quality management (Capital Structure); as well as the graduate employability, relations with the business world, efficiency of graduate teaching, student satisfaction, the university’s image and collaboration with other universities (Relational Capital). Based on these results, we develop our proposal of indicators for reporting on intellectual capital in universities. We believe that complementary non‐financial information is the most appropriate form to supply information on universities’ intangible elements, so as to avoid the inclusion of accounting criteria which could endanger the quality and reliability of the financial information. In our opinion, an improvement in university accounting systems would be achieved by the drafting and presentation of a new report complementary to the current financial statements –the Intellectual Capital Report‐. A set of indicators would show the information most demanded by different stakeholders regarding the institution’s intangible elements. It would be a healthy exercise in transparency for these institutions to facilitate access for their users to a variety of information which is relevant to their decision making.

References Bezhani, I. (2010) “Intellectual capital reporting at UK universities”, Journal of Intellectual Capital, Vol. 11, No. 2, pp. 179‐ 207 Bodnár, V., Harangozó, T., Tirnitz, T., Révész, E. and Kováts, G. (2010) “Managing intellectual capital in Hungarian Universities – the case of Corvinus University of Budapest”, Paper read at 2nd European Conference on Intellectual Capital, Lisbon, Portugal. Brătianu, C. (2009) “The intellectual capital of universities”, Annals of the University of Ljubljana, 30 June‐1 July, Ljubljana.

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Yolanda Ramírez, Ángel Tejada and Agustín Baidez Burgman, R., Roos, G., Boldt, L. and Pike, S. (2007) “Information needs of internal and external stakeholders and how to respond: Reporting on operations and intellectual capital”, International Journal of Accounting, Auditing and Performance Evaluation, Vol. 4, No. 4‐5, pp. 529‐546 Cañibano, L. and Sánchez, P. (2008) “Intellectual Capital Management and Reporting in Universities and Research Institutions”, Estudios de Economía Aplicada, Vol. 26, No. 2, pp. 7‐26. Cañibano, L., Sánchez, P., García‐Ayuso, M. and Chaminade, C. (Ed.) (2002) Directrices para la Gestión y Difusión de Información sobre Intangibles. Informe de Capital Intelectual. Proyecto Meritum, Vodafone Fundación, Madrid. Coy, D., Tower, G. and Dixon, K. (2001) “Public accountability: A new paradigm for college and university annual reports”, Critical Perspective on Accounting, Vol. 12, pp. 1‐31. Gray, R.H. (2006) “Social, environmental and sustainability reporting and organizational value creation? Whose value? Whose creation?”, Accounting, Auditing and Accountability Journal, Vol. 19, No. 6, pp. 793‐819. Instituto de Contabilidad y Auditoría de Cuentas (ICAC) (2002) Informe sobre la situación actual de la contabilidad en España y líneas básicas para abordar su reforma (Libro Blanco para la reforma de la contabilidad en España), ICAC, Madrid. Jones, N., Meadow, C. and Sicilia, M.A. (2009) “Measuring intellectual capital in higher education”, Journal of Information & Knowledge Management, Vol. 8, No. 2, pp. 113‐136 Leitner, K.H. (2004) “Intellectual Capital reporting for universities: conceptual background and application for Austrian Universities”, Research Evaluation, Vol. 13, No. 2, pp. 129‐140 Leitner, K.H. and Warden, C. (2004) “Managing and reporting knowledge‐based resources and processes in research organisations: specifics, lesson and perspectives”, Management Accounting Research, Vol. 15, pp. 33‐51. Malyshko, A.V. (2008) “About European format of indices system for measurement of intellectual capital value of a regional science center”, Actual Problems of Economics, No. 11, pp. 162‐172 Nava, R.M. and Mercado, P. (2011) “An analysis of the trajectory of intelectual capital in a Mexican public university”, Revista electrónica de investigación educativa, Vol. 13, No. 2, pp. 166‐187 Rafiee, M., Mosavi, M. and Amirzadeh, R. (2010) “Formulating and elaborating a model for the recognition of intellectual capital in Iranian universities”, World Applied Sciences Journal, Vol. 10, No. 1, pp. 23‐28. Ramírez, Y., Lorduy, C. and Rojas, J.A. (2007) “Intellectual capital management in Spanish Universities”, Journal of Intellectual Capital, Vol. 8, No. 4, pp. 732‐748. Sánchez, J.M. and Rivera, S.C. (2009) “A model for measuring research capacity using an intellectual capital‐based approach in a colombian higher education instittutions”, Innovar, Especial en Educación, pp. 179‐197. Sánchez, P., Elena, S. and Castrillo, R. (2009):”Intellectual capital dynamics in universities: a reporting model”, Journal of Intellectual Capital, Vol. 10, No. 2, pp. 307‐324 Schaffhauser, M. (2009): “Intellectual capital reporting for Austrian Universities – a trilling work in progress”, Paper read at EIASM Workshop on the Process of Reform the University, May, Siena, Italy. Silvestri, A. and Veltri, S. (2011) “The intellectual capital report within universities: comparing experiences”, Economic Science Series, Vol. 20, No. 2, pp. 618‐624. Warden, C. (2003): “Managing and Reporting Intellectual Capital: New Strategic Challenges for HEROs”, IP Helpdesk Bulletin, Vol. 8

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10 Years of IC and KM Research – a Content and Citation Analysis Vincent Ribiere and Christian Walter The Institute for Knowledge and Innovation Southeast Asia, Bangkok University, Bangkok, Thailand vince@vincentribiere.com christian.w@bu.ac.th Abstract: Celebrating the first decade anniversary of the International Conference on Intellectual Capital and Knowledge Management Conference (ICICKM), this paper presents a meta‐analysis of the citation, keywords and contents of all articles published in the proceedings of ICICKM conferences over the last decade. The study covers all nine proceedings with its 465 articles. The main knowledge management and Intellectual Capital themes, concepts, authors who published over the past 10 years are presented and discussed. Keywords: content analysis, keyword analysis, citation analysis, knowledge management, ICICKM

1. Introduction We conducted an analysis of the content, authors, keywords and citation of the 465 papers published in the nine proceedings over the past 10 years of the ICICKM conference. We will focus our analysis on concepts, themes and keywords associated with the papers published over the past 10 years, we map the locations of authors and analyze the number of cited references as well as their origin (book or article). It has been shown (e.g. Bentley (2008) and Wang, Wang, Zhang, Cai, and Sun (2012)) that it is possible to track the evolution of science by looking at keyword frequencies and doing a similar analysis focusing on the keywords and the words in article titles. Content and keyword analyses explore the “what”, a citation analysis explores the “who”. More specifically, a citation analysis of the ICICKM articles that focuses on which used references are cited most often, can be seen as an indicator for the impact of prior publications. This paper focuses on the field of Knowledge Management and Intellectual Capital by analyzing the first decade of articles published in the ICICKM proceedings. In addition to a frequency analysis we utilize the software Leximancer, a text mining software, in order to perform a frequency and semantic analysis of concepts.

2. Research methodology This study took into account all the 465 articles (4,200 pages) published in the ICICKM proceedings since 2004. We requested the proceedings for all nine ICICKM iterations. Every file contains a list of authors and their affiliation, and all articles published in each conference. Every article features a title, the list of authors, up to six keywords, the content body of each articles, and the references. The first author, the authors affiliation (location) at the time of submission, keywords, and the reference list were extracted into excel files. Subsequently the information was checked for duplicates, typographical errors and inconsistencies between American and British English. The keywords associated with each paper were self‐defined by their authors. A total of 2425 keywords (including duplicates), 1466 keywords (with no duplicates) were extracted and sorted by level of occurrence in st an Excel spreadsheet. Similar steps were taken for citations and 1 author location information. An Adobe Acrobat Pdf version of all the nine proceedings were imported into Leximancer Version 4 in order to run the various text analyzes. The Pdfs only included the actual articles and no index pages, no references, no author information or any other supplement information.

3. Data analysis and results Table 1 shows the ranking of references by how often they are cited in ICICKM articles. Not surprisingly, The Knowledge Creating Company, Working Knowledge, and The Tacit Dimension are among the top three most cited articles. In a previous study conducted by Walter and Ribière (2013), the same results were discovered.

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Vincent Ribiere and Christian Walter Nonaka’s and Takeuchi’s Knowledge Creating Company is the best known academic publication in the Knowledge Management literature and the SECI Model and the Concept of Ba are still among the most discussed topics in Knowledge Management. This is reconfirmed by the fact that Nonaka is present with three publications in the Top 20 ranking. The Top 20 ranking includes 29 articles, as references with the same amount of cites were all placed on the same rank. When checking for the article origins we found that 17 references are from book publications and 12 articles are from journal publications. Working Knowledge by Davenport and Prusak is another classic in the KM literature and provides a concise overview and introduction into the topic of knowledge management. The top three is completed with Stewart’s (1997) Intellectual Capital. The ranking of the top 20 most cited references is dominated by Intellectual Capital, which is not surprising given the conference title. Intellectual capital can be seen as part of the more general topic of knowledge management, it makes therefore sense that intellectual capital literature is dominantly cited, as the conference particularly focuses on issues around this area. Nine out of the 20 top referenced articles include the term ”intellectual capital” in their title. Several authors appear multiple times in the ranking. Nonaka, as already mentioned, is three times listed. The same numbers of mentions get Leif Edvinson for his work on intellectual capital and Etienne Wenger for his work on communities on practice. Michael Polanyi is mentioned twice in the ranking, for his foundational elaborations on tacit and personal knowledge. Table 1: Most cited references in ICICKM proceedings Rank

Author(s)

Year

Title

Times Cited 132

Nonaka, I., Takeuchi, H.,

(1995)

Davenport, T., Prusak, L.

(1998)

3 4

60 53

Stewart, T. Sveiby, K.

(1997) (1997)

Edvinsson, L. and Malon, M.

(1997)

Senge, P.

(1990)

7 8

32 31

Polanyi, M. Grant, R.

(1966) (1996)

(1990)

Cohen, W. M. and D. Levinthal Alavi and Leidner

10 11

29 28

Nonaka, I. Nahapiet, J. & Ghoshal, S.

(1991) (1998)

(1998)

Roos, G., Roos, J., Dragonetti, N. & Edvinsson, L. Nonaka, I.

The Knowledge‐Creating Company: How Japanese Companies Create the Dynamics of Innovation Working knowledge: How organizations manage what they know. Intellectual capital. The new organizational wealth: managing and measuring knowledge‐based assets. Intellectual Capital – Realizing your Enterprise’s true Value by Finding its Hidden Brainpower. The Fifth Discipline: The Art and Practice of the Learning Organization The Tacit Dimension Towards a knowledge‐based theory of the firm Absorptive Capacity. A new perspective on learning and innovation. Review: Knowledge Management and Knowledge Management Systems: Conceptual Foundations and Research Issues The knowledge creating company Social Capital, Intellectual Capital, and the Organizational Advantage Intellectual capital: navigating in the new business landscape

(1994)

Szulanski, G.

(1996)

Kogut, B and Zander, V

(1992)

Wenger, E.

(1998)

(2001)

Type

Book Book Book Book

Book Book Book Article Article

Article Article Article

Book

368

A Dynamic Theory of Organizational Knowledge Creation Exploring internal stickiness: impediments to the transfer of best practice within the firm Knowledge of the firm combinative capabilities and the replication of technology Communities of Practice, Learning, Meaning

Article

Article Book

Vincent Ribiere and Christian Walter Rank

Times Cited

Author(s)

Year

Brooking, A.

(1996)

16 17

22 21

Yin, R.K. Barney, J.B.

(1989) (1991)

17 18

21 20

Edvinsson, L. Nonaka and Konno

(1997) (1998)

Lave J. & Wenger, E.

(1991)

20 20

18 28

(2003) (1996)

Andriessen, D. Kaplan, R. S. and Norton, D. P. Lev, B.

20 20

18 18

(1974) (2002)

Polanyi, M. Wenger, E., McDermott, R and Snyder, W. Zack, M.

and Identity Intellectual capital: Core assets for the third millennium enterprise Case Study Research, Design and Methods Firm resources and sustained competitive advantage Developing Intellectual Capital at Skandia The concept of “Ba” building a foundation for knowledge creation Situated Learning. Legitimate peripheral participation. Making Sense of Intellectual Capital The Balanced Scored Card. Translating Strategy into Action Intangibles management, measurement, and reporting Personal Knowledge Cultivating Communities of Practice

(1999)

Developing a Knowledge Strategy

(2001)

Title

Type

Book Book Article Article Article Book Book Book Book Book Book Article

In addition to the citation count we also analyzed the Citation Memory (Figure 1) of all ICICKM proceedings. This had previously been done in the field of KM by Lambe (2011) and Walter and Ribière (2013). The Citation Memory is obtained by calculating the median of the difference between publications from a certain year and the publication year of the article they cite. The average citation memory for ICICKM is 7 years which is on one level with the citation memory of KMRP and above the findings by Lambe (2011). When combined with the findings of the Citation analysis we can see that, while the past is referenced rather well the more current publications seem to be less relevant. In the top 20 ranking only four articles were written in the 2000’s, namely Alavi and Leidner’s (2001) review of the field of knowledge management, Andriessen’s (2003) take on intellectual capital and Wenger, McDermott’s and Snyder’s (2002) collaborative work on communities of practice. We are aware that it takes several years for a publication to get traction within an academic field; however, one would expect that publications with wider relevance have been published in the recent past.

Figure 1: Citation memory Table 2 presents the ranking of the top 17 keywords (composed of 44 keywords) listed. We only selected keywords that were mentioned at least in 5 publications (occurrence count) over the past ten years. Table 2: Ranking of top Keywords (self‐reported by author) Rank 1 2 3

4 5

Keyword Knowledge Management Intellectual Capital

Count

Rank 15

160

Knowledge Sharing

Organizational Learning

Innovation

15 15

369

Keyword

Count

Intangibles

Leadership

Intellectual Capital Management

Trust

Competitive Advantage

Vincent Ribiere and Christian Walter Rank 6 7 8 9 10 10 10 11 12 13 13 13

13 13 14 14 14

Keyword

Count

Knowledge

Knowledge Transfer Communities Of Practice

Learning Tacit Knowledge

14 14

Ontologies

Intangible Assets

Absorptive Capacity Higher Education ICT

20 17

SME

Human Capital

Learning Organization Social Capital Malaysia Knowledge Management Systems Case Study

Rank 16

16 17 17 17 17 17

11 9 9

17 17 17

9 9

17 17

8 8 8

Keyword Balanced Scorecard Competitive Advantage

Count

Value Creation

Management

Culture Framework Innovation Management Performance Measurement

5 5

Technology

Open Innovation Quality

5 5

Decision Making

Enabling Context Knowledge Modeling

6 6

5 5

Social Networks

Value Networks

Not surprisingly, Knowledge Management and Intellectual Capital are ranked on top, as both terms give the conference its name. Knowledge sharing (KS) completes the top three. Understanding why and how people share or do not share knowledge is key to the field of KM, even though it represents only one limited aspect of the overall KM field. Correspondingly, knowledge transfer is within the top 10, as they are often interchangeably used in the literature even though they represent two different concepts. The former doesn’t imply transfer since a level of absorptive capacity and a cognitive process need to be applied for the latter to happen. Organizational learning, learning and human capital are other keywords very central to the field of knowledge management and their presence acknowledge the need for individuals in organizations and th organizations build upon available knowledge. Innovation is ranked on the 5 place. Innovation management and knowledge management have recently been more closely linked to each other and innovation is a fundamental topic in intellectual capital research. Communities of practice are ranked 8th in the keyword list. The first knowledge management “tool” in the keyword list is Communities of Practice, highlighting not only Etienne Wenger’s contributions are also three times listed in the most cited references. Communities of practice are at the intersection of the concepts of sharing and learning and can be regarded as an organizational approach or tool to enhance both. Interesting to further note is that while tacit knowledge is ranked 10th, explicit knowledge does not seem to have grabbed the attention of authors. This finding might be somewhat surprising as explicit knowledge could be seen as a means of protecting intellectual capital, for example in forms of patents.

Text analysis The text analysis was conducted using the text mining tool Leximancer. Leximancer has been used in more than 870 academic publications (Leximancer, 2013) .Leximancer is a text analytics tools (machine learning) that can be used to analyze the content of a corpus of documents and to visually represent the extracted information. Leximancer produces a concept maps that displays the main concepts of the documents analyzed as well as their relationship. The content analysis performed by Leximancer is both a conceptual (frequency of concepts) and a relational analysis (semantic analysis). The concepts are clustered into higher‐level themes.

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Vincent Ribiere and Christian Walter Error! Reference source not found. presents the top 48 concepts extracted (total 78 concepts identified) by Leximancer as well as their occurrence (count) in the text of the 465 papers analyzed. We displayed 48 concepts that were extracted with a relevance percentage level higher or equal than 10%. Table 3: Top 48 concepts (extracted by the text analytics tool) Rank

Concept

Count

Rank

Concept

Count

Knowledge

18,269

Social

2,568

Organization

7,470

Assets

2,525

Process

6,928

Community

2,468

Management

6,467

Analysis

2464

Intellectual

5,629

Innovation

2,324

Information

5,104

Results

2,285

Capital

4,724

Resources

2,277

Research

4,634

Services

2,257

4,595

Human

2,242

Development

4,371

People

2,195

Context

2,192

Learning

4,142

System

4,108

Approach

2,151

Company

4,080

Competitive

2,107

Study

4,055

Employees

2,069

Value

3,936

Case

2,033

Model

3,910

Products

1,976

Business

3,828

Culture

1,975

Work

3,157

Transfer

1,954

Strategic

3,102

Members

1,933

Data

2,824

Technology

1,890

Performance

2,803

Communication

1,822

Practice

2,776

Key

1,816

23 24

Group Level

2,715 2,583

47 48

Students Project

1,803 1,764

We will not describe in details each of the concepts extracted by Leximancer, but as expected we can notice that some concepts were also part of the keyword analysis previously described; Case/Case study, Community/Community of Practice, Competitive/Competitive advantage, Culture, Innovation, Intellectual/Intellectual capital, Knowledge, Knowledge Management, Learning, Management, Performance/Performance management, Technology, Value/Value creation. Nevertheless a some important keywords extracted by Leximancer were not mentioned by authors, like: Communication, Context, Groups, people, process, Leximancer provides a Concept Cloud (Figure 2) representation of the concepts. This visualization technique allows seeing a global representation of the concepts and of their relationships. Concepts that are strongly semantically related will be displayed close to each other’s on the map. The Concept Cloud is heat‐mapped, in that hot colors (red, orange) denote the most relevant concepts, and cool colors (blue, green), denote the least relevant. We can already notice that the main concepts (in red are centered around the concept of Knowledge, and around the concepts of Intellectual capital (in orange), as one will have expected in the conference proceedings of the ICICKM! Concepts in between these 2 groups of concepts are linked on the map, but we can notice that they are not touching each other’s. A mediating set of concepts around the theme of strategy, competition and resources indirectly link them. This can be easily interpreted since IC and KM are two independent strategic approaches which overlap and complement each other’s. By clustering concepts that are semantically linked with each other’s, Leximancer creates themes. Based on the analysis of the 465 papers, Leximancer created 7 themes (composed of 78 concepts). By default Leximancer names each theme by the first concept that constitutes it. By analyzing the concepts constituting each theme and by looking at the

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Vincent Ribiere and Christian Walter thesaurus built around them, we renamed the themes accordingly to what aspect of KM we believed was represented. Table 3 displays the different theme names and their associated concepts. On the conceptual map representation (Figure 3) the themes are represented by grouping the clusters of concepts, and are shown as colored circles on the map. The concept heat map (Figure 2) and the themed concept map (Figure 3) show that the foci of publication presented at the ICICKM conference are in the fields of Knowledge Management, Intellectual Capital, Research, Business Strategy, Implementation Frameworks, Social Learning, and Groups. The KM theme (100% relevance) is more represented than IC (68% relevance). If we look at the concepts composing the first main theme, knowledge management, it is composed of the following concepts; Knowledge, Organization, Management, Process, Information, System, Culture, Technology, Transfer, Concepts. These concepts constitute the main KM pillars and the main centers of KM research interests. Knowledge transfer remains a challenge despite numerous publications on the topic. The second main theme of intellectual capital is represented by the following key concepts; Intellectual, Capital, Value, Company , Performance, Assets, Human, Market, Firms, Industry, Economic, Financial, Growth and Global. Once again we can see the core aspects of IC based on financially valuing the intellectual asset of a company, to better understand what makes its market value. The third theme is research; Research, Study, Model, Level, Data, Analysis, Results, Case, Relationship, Education, Significant, Countries, and Variables. The key terms associated with conducting research are presented. We can notice the concepts of education and countries, probably illustrating that a lot of the research presented were conducted in an education setting and/or conducted in a global context.

Figure 2: Concept cloud (heated map) The fourth theme is about business strategy; Business, Strategic, Resources, Innovation, Services, Competitive, Products, Employees, Key and Production. As previously explained, IC and KM are two types of strategies that an organization might want to follow in order to become more competitive, innovative in their products and services. They both heavily rely on properly managing and motivating employees who possess the knowledge.

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Vincent Ribiere and Christian Walter The fifth theme is about implementation frameworks; Development, Social, Approach, Quality, Framework, Structure, Public, Training, Networks, Theory, Authors, World and Local. The implementation of IC and KM is often implemented in a framework/structure, for example a Quality framework like the European Quality Award or Malcolm Baldrige Quality Award. We can notice that the term framework here is considered in both contexts, research (conceptual framework) and practice (structure/context), this is illustrated by its location between the KM and the Research themes on the concept cloud map.

Figure 3: Concept map showing themes The sixth theme is about learning; Learning, Work, Practice, Community, Context, People, Communication , Experience and Tools. Knowledge is created through learning. Learning often takes place in a social context, where people meet, communicate and experience things. When successfully implemented, Communities of Practices (CoP) can become a strong vehicle for sharing knowledge and for learning. The last and seventh theme is about groups; Groups, Members, Projects, Examples, Students, Team, Trust, and Software. Employees rarely work alone, they work in groups, teams, projects. Group knowledge is different from individual knowledge which has been source of great research interest. Intra and extra team knowledge sharing approaches have been studied. Nowadays, due to globalization, we see more and more teams composed of members located in geographically dispersed locations. In such situation, collaborative software must be used to get organized but also to socialize and build trust among the team members. Trust remains one of the key social lubricants for knowledge sharing. A lot of researches use teams of students to study team dynamics, learning and knowledge sharing behaviors. This is reflected by the location of this theme on the map, be located between the groups and research themes.

4. International distribution of contributors In addition to the content related analysis we conducted an analysis of the origin of 1st author’s who published at the ICICKM conference. We took all publications into account. Most authors came from the United Kingdom, South Africa and the USA. ICICKM was hosted in South Africa, USA, Thailand, Canada (twice), Columbia, Hong Kong, the United Arab Emirates and Chile. All of the hosting countries are among the top participating authors, with Chile and UAE falling a bit behind. Interestingly Malaysia is ranked very much on top of the ranking. It is relevant to note that the keyword “Malaysia” is also among the most used keywords. This shows a strong commitment of the South‐East Asian country to knowledge management.

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Vincent Ribiere and Christian Walter Table 3: Numbers of first authors attendees by countries Country

No. of 1st Author Attendees

Country

No. of 1st Author Attendees

Country

No. of 1st Author Attendees

Estonia

Netherlands

South Africa*

Turkey

Scotland

USA*

Romania

Denmark

France

Chile*

Japan

Malaysia

Greece

Mexico

Thailand*

Nigeria

Sweden

Italy New Zealand

Peru

Finland

Spain

Switzerland

Australia

Lithuania

Brazil

India

Vietnam Kingdom of Saudi Arabia

Canada*

UAE*

Northern Cyprus

Colombia*

Austria

Argentina

Iran

Russia

Bangladesh

Germany

Belgium

Ecuador

Latvia

Hong Kong*

Indonesia Czech Republic

Norway

Lebanon

Portugal

Taiwan

South Korea

China

Egypt

* hosted an ICICKM conference, Chile is missing in this list, two conferences were organized in Canada Table 4: Themes extracted by Leximancer Theme Name

Knowledge Managemen t

Intellectual Capital

Research

Business Strategy

Implementatio n Framework

Social Learning

Groups

Relevanc e

100%

68%

54%

44%

41%

38%

22%

Concepts

Knowledge

Intellectual

Research

Business

Development

Learning

Groups

Organization

Capital

Study

Strategic

Social

Work

Member s

Managemen t

Value

Model

Resources

Approach

Practice

Projects

Process

Company

Level

Innovation

Quality

Community

Examples

Information

Performanc e

Data

Services

Framework

Context

Students

System

Assets

Analysis

Competitiv e

Structure

People

Team

Human

Results

Products

Public

Communicatio n

Trust

Culture

Market

Case

Employees

Training

Experience

Software

Technology

Firms

Relationshi p

Key

Networks

Tools

Transfer

Industry

Education

Production

Theory

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Vincent Ribiere and Christian Walter Theme Name

Knowledge Managemen t

Intellectual Capital

Research

Concepts

Economic

Significant

Authors

Financial

Countries

World

Growth

Variables

Local

Business Strategy

Implementatio n Framework

Social Learning

Groups

Global

5. Conclusion This research used multiple approaches to assess the content of the 465 articles published over the past ten years in the proceedings of the ICICKM conference. The first approach focusing on a reference analysis revealed that the most cited article by ICICKM publications was “The Knowledge‐Creating Company: How Japanese Companies Create the Dynamics of Innovation” by Nonaka and Takeuchi (1995). The first top reference in IC was on the 3rd position with the “Intellectual capital” book by Stewart (1997). Both books were among the first ones to be published in the field, which may explain their popularity and impact. The citations mainly come from books from the mid‐1990s and the most cited articles are only two that are from the 2000s. The second approach used, looked at the occurrence and ranking of keywords defined by their authors. Among the top 10 keywords were; Knowledge, Organization, Process, Management, Intellectual, Information, Capital, Research, KM and Development. The third approach used a text‐mining tool to semantically analyze the content of the articles and to extract their main themes and concepts. Seven main themes emerged; Knowledge Management, Intellectual Capital, Research, Business Strategy, Implementation Frameworks, Social Learning, and Groups. These themes are not surprising since they represent the main pillars of KM and IC with a strong emphasis on people, groups and social aspects that are at the heart of the successful implementation of KM and IC. The good news is that technology aspects were addressed but relatively less than people aspects, which reflects that the researcher community realizes that technology is a tool, the enabler of KM and IC, but that it should not be the center of the research in the fields of KM and IC. KM is not something you buy but something you do! th Happy 10 anniversary and long live the ICICKM conference!

References Alavi, M., & Leidner, D. E. (2001). Review: Knowledge Management and Knowledge Management Systems: Conceptual Foundations and Research Issues. (W. H. Starbuck & S. Holloway, Eds.) MIS Quarterly, 25(1), 107–136. doi:10.2307/3250961 Andriessen, D. (2003). Making Sense of Intellectual Capital (p. 456). Oxford, UK: Butterworth Heinemann. Barney, J. (1991). Firm Resources and Sustained Competitive Advantage. (Anonymous, Ed.)Journal of Management, 17(1), 99–120. doi:10.1177/014920639101700108 Brooking, A. (1996). Intellectual capital: Core assets for the third millennium enterprise (p. 224). Cohen, W. M., & Levinthal, D. A. (1990). Absorptive Capacity: A New Perspective on Learning and Innovation. (W. H. Starbuck & P. S. Whalen, Eds.)Administrative Science Quarterly, 35(1), 128–152. doi:10.2307/2393553 Davenport, T., & Prusak, L. (1998). Working Knowledge. Boston, MA: Harvard Business School Press. Edvinsson, L. (1997). Developing Intellectual Capital at Skandia Understanding Knowledge Management. Long Range Planning, 30(3), 366–373. Retrieved from http://www.sciencedirect.com/science/article/pii/S002463019790248X Edvinsson, L., & Malon, M. S. (1997). Intellectual Capital – Realizing your Enterprise’s true Value by Finding its Hidden Brainpower (p. 240). Grant, R. M. (1996). Towards a Knowledge‐Based Theory of the Firm. Strategic Management Journal, 17(Winter Special Issue), 109–122. Retrieved from http://www.jstor.org/stable/2486994 Kaplan, R. S., & Norton, D. P. (1996). The Balanced Scored Card. Translating Strategy into Action (p. 336). Harvard, MA: Harvard Business Review Press. Kogut, B., & Zander, U. (1992). Knowledge of the firm, combinative capabilities, and replication of technologies. Organization Science, 3(3), 383–397. Lambe, P., (2011) "The unacknowledged parentage of knowledge management", Journal of Knowledge Management, Vol. 15 Iss: 2, pp.175 ‐ 197

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Vincent Ribiere and Christian Walter Lave, J., & Wenger, E. (1991). Situated learning: Legitimate peripheral participation. (R. Pea & J. S. Brown, Eds.)Learning in doing (Vol. 95, p. 138). Cambridge, MA: Cambridge University Press. doi:10.2307/2804509 Lev, B. (2001). Intangibles management, measurement, and reporting (p. 228). Washington DC: Brookings Institution Press. Leximancer. (2013), from http://www.leximancer.com Nahapiet, J., & Ghoshal, S. (1998). Social Capital, Intellectual Capital, and the Organizational Advantage. The Academy of Management Review, 23(2), 242. doi:10.2307/259373 Nonaka, I. (1991). The knowledge creating company. Harvard Business Review, (Nov‐Dec), 96–104. Nonaka, I. (1994). A Dynamic Theory of Organizational Knowledge Creation. (I Nonaka & H. Takeuchi, Eds.)Organization Science, 5(1), 14–37. doi:10.1287/orsc.5.1.14 Nonaka, Ikujiro, & Konno, N. (1998). The Concept of “Ba”: Building a Foundation for Knowledge Creation. California Management Review, 40(3), 40–54. Nonaka, Ikujiro, & Takeuchi, H. (1995). The Knowledge Creating Company. New York, New York, USA: Oxford University Press. Polanyi, M. (1966). The Tacit Dimension. The British Journal of Aesthetics (Vol. 19, p. 108). Chicago, IL: University of Chicago Press. Retrieved from http://books.google.com/books?hl=en&amp;lr=&amp;id=zfsb‐ eZHPy0C&amp;oi=fnd&amp;pg=PR6&amp;dq=The+Tacit+Dimension&amp;ots=HblBREDteH&amp;sig=GrJkF0B3‐ QC5Ikmbs0k18WO4gsE Polanyi, M. (1974). Personal Knowledge: Towards a Post‐Critical Philosophy (p. 428). Chicago, IL: University Of Chicago Press. Roos, J., Roos, G., Dragonetti, N. C., & Edvinsson, L. (1998). Intellectual Capital: Navigating in the New Business Landscape (p. 208). Senge, P. (1990). The Fifth Discipline: The Art and Practice of the Learning Organization (p. 464). Stewart, T. A. (1997). Intellectual Capital: The New Wealth of Organizations. Performance Improvement (Vol. 37, p. 320). New York, New York, USA: Doubleday. Retrieved from http://www.loc.gov/catdir/description/random046/96047491.html Sveiby, K. E. (1997). The new organizational wealth: managing and measuring knowledge‐based assets. (p. 275). Szulanski, G. (1996). Exploring internal stickiness: impediments to the transfer of best practice within the firm. Strategic Management Journal, 17(Winter Special Issue), 27–43. Walter, C. & Ribiere, V. (2013). A citation and co‐citation analysis of 10 years of KM theory and practices. Knowledge Management Research & Practice, 11, 221–229. doi:10.1057/kmrp.2013.25 Wenger, E. (1998). Communities of Practice: Learning, Meaning, and Identity. (R. Pea, J. S. Brown, & J. Hawkins, Eds.)Learning in doing (Vol. 15, p. 318). Cambridge, MA: Cambridge University Press. doi:10.2277/0521663636 Wenger, E., McDermott, R., & Snyder, W. M. (2002). Cultivating Communities of Practice (p. 284). Harvard, MA: Harvard Business Review Press. Yin, R. (1989). Case Study Research: Design and Methods. Beverly Hills, CA: SAGE Publications Ltd. Zack, M. H. (1999). Developing a Knowledge Strategy. California Management Review, 41(3), 125–145.

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Towards an Anthropological‐Based Knowledge Management Francis Rousseaux1 and Jean Petit2 1 Group Signal, Image and Knowledge, University of Reims Champagne‐Ardenne, Reims, France 2 Division Aerospace and Defense, Capgemini, Paris, France francis.rousseaux@ircam.fr jean.petit@etudiant.univ‐reims.fr Abstract: “To make the knowledge useful […], the KM manager must create a single shared understanding among people of what the knowledge means to the organization within the context of its business domain and how it is intended to be used.”(Malafsky and Newman) However people possess their own knowledge, meaning that useful knowledge from an individual or a collective will be useless for another. We defend the idea that knowledge isn’t determined by a context of use but by people who own it. So, to enable knowledge management, we have to first classify knowledge according to people. This paper proposes a way to organize knowledge based on an ontological classification of people. An ontological representation comes from Philippe Descola’s book "Beyond nature and culture" (2005), which explains that humans use their experience to organize the world, following a logical process in two parts, namely Identification and Relations leading to the modeling of their Ontology – Ontology with a capital “O” will be used in the context of the specification by an indi‐ vidual of what exist in the world and their relationships. That’s the classification of these Ontologies into ontologies – “a formal, explicit specification of a shared conceptualization” (Studer, Benjamins, Fensel, 1998) – which leads us to an onto‐ logical classification. By linking an ontology to the knowledge that comes from its people, we will prove that both are re‐ lated. In fact these ontologies determine knowledge and thus ontologies classification organize knowledge. While investi‐ gating the relation between ontologies and knowledge, we observe that using inadequate knowledge in a multi‐ontological context can trigger crisis due to the information interpretation, strengthening the need to manage it. In an attempt to expand the scale of knowledge use which is determined by people ontologies – echoing an ontological capital of people –, we shall discuss about merging informations to create an heterotopic phenomenon by using several knowledges resulting of a consultation process based on mutual knowledge. Keywords: anthropological knowledge management, ontological classification, ontological capital, multi‐ontological situa‐ tion, information heterotopy

1. Introduction It’s a known practice in knowledge management to use contexts or domains to organize knowledge but in fact it’s not the best practice. The main purpose of this paper is to demonstrate that a knowledge classification emerging from people Ontologies surpass the traditional organization based on the context in which the knowledge is used. This ontological classification stems from Philippe Descola’s two steps process which allow us to model people Ontology through Identification and Relations. On one hand Identification organizes exist‐ ing being in collectives using intrinsic ontological properties, on the other hand Relations establishes their relationship through extrinsic characteristics. By aggregating similar Ontologies in a shared ontology, we are able to propose a multi‐scalar ontological classification. To link these ontologies with knowledge, we use the anthropologist definition of knowledge which suggests that Ontologies are primary knowledge. To validate this proposition, we first demonstrate that the sustainable development ontology is a basis to knowledge creation in call for proposals and projects on the domain of the risks. Then we show that the sustainable development ontology shapes knowledge in any context of use by analyzing the land planning of a territorial project called Taonaba. While investigating this territorial project, it seems that an inadequate use of knowledge was related to the creation of a crisis between two communities. Indeed, the situation of Taonaba reveals a multi‐ ontological context – the sustainable development group and the Maroons – with a one sided use of knowl‐ edge on its land planning. Considering knowledge as information that an ontological framework successfully interprets, as a result it is impossible for the Maroons to understand the sustainable development believers’ use of knowledge. A late consultation process which failed to integrate properly the Maroon expresses the need of mutual recognition to enable a real participation of each stakeholder. Eventually, the heterotopic properties of information make possible the co‐utilization of knowledge which could solve multi‐ontological problematic.

2. Ontological classification The ontological classification is based on the hypothesis that we can organize knowledge through people’s Ontology. We use Philippe Descola’s two steps process composed of Identification and Relations – a capital I

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Francis Rousseaux and Jean Petit and R for Identification and Relations will be used to avoid confusion while we talk about these processes – to model Ontologies. People use the Identification to aggregate existing being in collectives and use the Relations to establish their relationships. If the resulting model for each individual is unique, shared models emerge at the scale of the collective, enabling a scalable classification.

2.1 Identification The first step of Philippe Descola’s process, Identification, is based on the projection of human ontological characteristics: the interiority and the physicality, to all existing being. Interiority is what we commonly call “spirit, soul or consciousness” (Descola, 2005) and physicality is referring to “outer shape, substance, physiol‐ ogic processes, perceptive and sensory‐motor” (Descola, 2005) of the existing being. Assigning attributes to each of these criteria will allow human to do a dual dichotomy interiority/physicality and difference/similarity, determining their relative places on an ontological matrix. At the end of the Identification process we obtain collectives, using the continuity and discontinuity of the compared qualities.

2.2 Relations The second step in Descola’s method is made of Relations. Beside the intrinsic properties of the existing beings extrinsic relations are added. They fall into two groups divided in atomic relations:

Some potentially reversible between two equivalent beings (gift, exchange and predation) situated at the “same ontological level” (Descola, 2005);

Some univocal based on connexity (genetic, temporal or spatial) between non‐equivalent beings (produc‐ tion, transmission and protection) “linking several ontological levels” (Descola, 2005).

2.3 Ontology modeling To illustrate the whole process, we are going to present a fictive example. Imagine a human A and three other existing being B, C and D. A starts the Identification by projecting his interiority and physicality over B, C and D. Then he assigns attributes to the physicality and interiority of himself, B, C and D: A(short, consciousness), B(short, spirit) and C(tall, unconsciousness), D(short, spirit). Next is the creation of the ontological matrix. We observe on the Table 1 that the given example creates three different collectives A, B and D – called E – and C. A and E have a physicality continuity, but have an interiority discontinuity. C has a physicality and interi‐ ority discontinuities with either A or E. Table 1: Ontological matrix of the illustration

Consciousness

Spirit

Unconsciousness

Short

B, D

Tall

Then, our human A explains the Relations between the collectives. A has a protectionist relationship with E and considers C as a production of E. Figure 1 is the partial Ontology of an individual. As it is, this ontological model is a formal, explicit specification of one’s conceptualization of the world. Therefore, are there similar Ontologies that we can gather in one ontology to enable a classification process?

Figure 1: Human A’s ontology, resulting of the Identification and Relations processes

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2.4 Classification To Philippe Descola, the continuities and/or discontinuities between collectives are driven by specifics Identifi‐ cation's modes: the animism, the naturalism, the totemism and the analogism. “Whether most existing being are deemed to have a similar interiority while distinguished by their bodies, and it’s the animism […]; Whether humans alone have the privilege of interiority while relating to the continuum of non‐humans by their physical characteristics, and it’s the natu‐ ralism[…]; Whether humans and non‐humans share, whitin a same class, physical and moral properties from a prototype […] and it’s the totemism […]; Whether all elements of the world dif‐ fer from each other on the ontological level […] and it’s the analogism.” (Descola, 2006) Table 2: Modes of identification denoted by Philippe Descola Same physicalities

Different physicalities

Same interiorities

Totemism Aboriginal from Australia.

Animism Amazonia, North America, Siberia, Melanesia and South‐east Asia.

Different interiorities

Naturalism Europe since Classical Age.

Analogism China, Europe since Renaissance, West Africa and Mesoamerica.

This classification presented by Philippe Descola shows Identification trends, but do not enable the organiza‐ tion of one’s conceptualization of the world in a shared conceptualization. In fact, Philippe Descola writes in his book that Ontologies are always hybrid to some extent. To create ontologies we have to study the common experience of the world shared by some people. It’s the aggregation of the similarities between several onto‐ logical models that can be express with a statistical operation which will represent this common conceptualiza‐ tion of the world.

Figure 2: The creation process of “ontologies” We can organize ontologies with a top‐down scalable classification where the biggest collective with the more abstract ontology are at the top and the individuals Ontologies are at the bottom. To summarize, Identification and Relations allow us to model Ontologies. Collective experiences as ontologies can be used to organize and scale Ontologies. Being able to classify people in ontologies, we have to now ana‐ lyze the relation between ontologies and knowledge. If we can prove that both are linked, then we will be able to associate useful knowledge with people.

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Figure 3: Ontologies classification

3. Ontologies and knowledge Anthropologists define knowledge as being first what we know about ourselves and about our environment, meaning that the ontologies we just modeled are representing primary knowledge. One of our objectives here is to demonstrate that this original knowledge emerging from people conceptualization of the world is present in any context of use and thus people’s ontology classification come first in a knowledge management process. If our proposition is true, an ontology of a given collective should shape further knowledge built by this collec‐ tive. To validate this hypothesis, we will study the relation between the ontology of sustainable development believers and its associated knowledge.

3.1 The ontology of the sustainable development believers Sustainable development believers adhere to the naturalism Identification mode, dividing existing being be‐ tween humans – society – and non‐humans – environment – based on an interiority discontinuity and a physi‐ cality continuity. In addition, the sustainable development conceptualization of the world prescribes the hu‐ man’s protection on the environment as an economic factor, establishing a hierarchical relationship between the two of them.

Figure 4: Simple representation of the ontology of sustainable development believers The sustainable development ontology – abbreviated SDO in this paper – is easily recognizable. It presents a characteristic duality, society on the one hand and environment on the other hand, connected by humans’ protection of non‐humans. Considering that this ontological model is the basis of further knowledge, we should be able to discover this remarkable setup in any context of use.

3.2 The sustainable development ontology footprint on knowledge To prove our theory, we will first explore a call for proposals and a call for projects in the field of risk, as we think they are the best suited to show that knowledge is built on the SDO properties. Then, we will demon‐ strate the multi‐contextual use of the ontology as primary knowledge by analyzing a territorial project. Once both will be proved, it will confirm the relevance of an ontological classification of knowledge. 3.2.1 Sustainable development ontology as a basis to knowledge creation The call for proposals that we study is a French one, titled "Risks, Decision, Territories" (available at http://www.developpement‐durable.gouv.fr/IMG/pdf/RDT_APR2013‐2.pdf). The thematic was "The territories resilience in the face of the risks". Most of the categories fit explicitly the sustainable development ontology:

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Axis 1 – “Emerging risks”

“3.1.1 Risks associated with technological innovation” Æ humans collective;

“3.1.2 Risks associated with climate and societal change” Æ humans and non‐humans collectives;

“3.1.3 Interaction between natural risks and technological risks” Æ humans and non‐humans collectives and their relationship;

“3.1.4 Risks and economy” Æ relationship between collectives in the sustainable development;

Axis 2 – “New approaches”

“3.2.3 The risk management division, the social inequalities” Æ humans collective;

“Communication, NTIC and territories” Æ humans collective;

“Social risks” Æ humans collective;

Axis 3 – “Factors of improvement of territories resilience in the face of the natural and technological risks” Æ humans and non‐humans collectives.

Figure 5: Sustainable development ontology and the conception of the risk We observe on the figure 7 that the arising knowledge from this call for proposals is indeed structured by the sustainable development ontology. To confirm our observation, we analyze a call for projects by the French National Research Center (CNRS). In a context close to the previous one, the title "Socio‐economic space of the environmental risk" (available at http://www.cnrs.fr/mi/IMG/pdf/peps_esereaap2013vf.pdf) reflects every parts of the sustainable development ontology. As a matter of fact, we have the separation of humans – socio –, non‐humans – environmental – and the hierarchical relation – economic. We won’t go further with our demonstration because it seems clear enough that the SDO is a support to create new knowledge in this call for projects. Our analysis took place in the specific field of the risks to validate that knowledge emerges from an ontology. As primary knowledge, we need to highlight the multi‐contextual use of an ontology. To reach this goal, we will study the consequence of the SDO on land planning knowledge. 3.2.2 A multi‐contextual use of the sustainable development ontology We will now introduce from prior works a territorial project called Taonaba, located in the French department of Guadeloupe in Overseas France in the town of Les Abymes. The main idea of the project is to create a Mangrove House, close to a coastal wetlands characteristic by its ecological diversity of national and international importance. The place is part of the former nature reserve of the Grand Cul‐de‐Sac, nowadays included in the National Park of Guadeloupe and is classified as a worldwide biosphere reserve by the Ramsar convention (an international treaty for the conservation and sustainable use of wetlands). In addition, an interesting agricultural area is adjacent (remains of the sugar plantation of Belle‐ Plaine).

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Figure 6: Les Abymes location on the Guadeloupe territory. Original picture after a Google Map satellite view The Territory approach of Taonaba is directly inspired by the creative process of "administrative" Countries LOADDT Pasqua 1995 then Voynet 1999, "the law of June 25th of 1999, guidance for planning and sustainable development of the territory." (http://www.legifrance.gouv.fr, the French government entity responsible for publishing legal texts online). The desire to preserve, develop and enhance the richness has led in a sustainable development logic:

An ecotourism development: to be the motor of the Abymes tourism through the local development of its richness;

An ecological preservation: through education to safeguard the environment and ecosystems knowledge (agricultural areas, coastal wetlands);

Local development (social well‐being) by promoting the development of employment‐generating activities for local actors, and creating a green space at the gates of the city.

Concretely, the city Les Abymes will develop a museum centre, as well as discovery paths by foot and boat. The figure 7 points the duality humans – museum center – and non‐humans – nature reserve –. The protec‐ tionist relationship of Taonaba could be express by showing the orientation of the interaction between the two collectives – tourists at the museum going for a walk in the nature reserve. The SDO isn’t used solely in the context of the risks, but also in the land planning and thus used in any context. Therefore, knowledge can be organized using an ontological classification. It’s good to notice that this classification reveals an ontological capital of people. We believe that the importance of this ontological classification exceeds the unique purpose of proposing useful knowledge to the right people. In fact, while investigating Taonaba territorial project, it seems that knowledge use was related to the creation of a crisis.

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Figure 7: Taonaba project. Original picture after a Google Map satellite view

4. Towards an ontological knowledge management Taonaba is divided in two main ontologies: the Maroons one and the sustainable development one. This multi‐ ontological situation which is quite common will reveal itself more complex than expected. In fact a one sided use of knowledge will trigger a crisis and the consultation process will fail to reach its objectives. We think that these consecutive problems could have been avoided with an ontological knowledge management.

4.1 A multi‐ontological situation The description that we did of Taonaba comes from various press papers of the city Les Abymes to the general public. Yet the city Les Abymes isn’t the only actor to work on this project. The semi‐public company in charge of land use planning in Guadeloupe (SEMAG) jointly participates. The SEMAG and the city Les Abymes share the same ontology, that’s what confirms the page “Who are we?” available on their website. However, one actor doesn’t participate in the realization of the project but is directly involved. Indeed the Maroons, slave descendants who took refuge in the mangrove, live in the middle of the land planning of Taonaba. From someone that belongs to the SDO, it seems unbelievable that people could live in a nature reserve, but the Maroons believe that this place is their rightful home. Thus, we presume that the land planning of the Maroon community reflects another ontology.

Figure 8: The Maroon location Our attention will therefore be focused on this particular situation between two ontologies and their relations in the context of the land planning of Taonaba.

4.2 A one sided use of knowledge In France, the Voynet law has two major consequences on the territorial approach. In fact, it ensures the co‐ herence of the French territory through sustainable development and also includes participatory consultation as part of the process. Initiated by the city Les Abymes and the SEMAG, the consultation process concerns the

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Francis Rousseaux and Jean Petit humans collective in accordance with the SDO. It’s worth noting that different ontologies may have asked for others collectives to participate, because people would have considered themselves as equal or inferior being. Thus, we expect that all the human actors take place in the consultation process. However we observe that “the project is initially developed without taking into account the population living on the Belle‐Plaine area” (Lahaye, 2008) and alone (???) are included in the process “1) the city of Les Abymes, 2) the semi‐public com‐ pany in charge of land use planning in Guadeloupe 3) the architect and contractor of the project” (Lahaye, 2008). If we refer to the figure 8, we see that the Maroons are in the area of non‐humans and the city Les Abymes, the SEMAG and the architect are in the humans area. Consistent with the characteristics of the SDO, it makes sense that the Maroons weren’t associated with the consultation process, because no human should be in the area reserved to the non‐humans. The exclusion of the Maroons is unintentional but real. Without being able to find an agreement through consultation, start the construction of Taonaba project based on a one sided use of knowledge.

4.3 Taonaba crisis With the Maroons out of the participatory consultation process, the land planning was led principally by the city Les Abymes and the SEMAG. If the actions of the sustainable development believers are justified by their own ontology, they weren’t perceived as such by the Maroons. Indeed, the “dwellers feel displaced and dis‐ possessed of a space they have appropriated for a long time” (Layahe, 2008). This resentment expresses the gap between the meaning of the knowledge used for the sustainable development holders and the Maroons. Tsuchiya explain that “Although terms “datum”, “information”, and “knowledge” are often used interchangea‐ bly, there exists a clear distinction among them. When datum is sense‐given through interpretative framework, it becomes information, and when information is sense‐read through interpretative framework, it becomes knowledge.” (Tsuchiya, 1993). To us, datums are sense‐given through Ontologies which act as an interpreta‐ tion framework and thus become information. When knowledge is used, it’s expressed as information and to understand it as knowledge it needs to be interpreted by the same ontology which created it. In our case there, it means that the Maroons ontology will to sense‐read informations produced by the sustainable devel‐ opment knowledge.

Figure 9: Information interpretation depends on ontologies. Consequences on the multi‐ontological situation of Taonaba Unable to read the sense of the incoming information the same way the sustainable development believers do, the Maroons began “vandalism (destruction of equipment, recurring thefts)" (Lahaye, 2008) in response to the incomprehensible action of the city Les Abymes and SEMAG, considered as an aggression. This crisis re‐ veals that knowledge used has various consequences on people depending on the ontologies which interpret the created information. This scenario confirms the need of an ontological knowledge management and ori‐ ents our reflection on a multiple use of knowledge which might satisfied several ontologies. Therefore, to start a co‐utilization of knowledge, it is necessary to include the Maroons in the consultation process.

4.4 Participatory consultation and knowledge use After the Maroons event, the city Les Abymes and the SEMAG which lead the Taonaba project became aware of this population and therefore logically integrated them in the consultation process. This turnaround marks

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Francis Rousseaux and Jean Petit the beginning of possible agreements to a joint creation of Taonaba land planning. However, as Nathalie La‐ haye noticed, the participation of the Maroons in the consultation process didn’t change the ongoing project. “The organization of meetings, topics discussed (the development of the canal which correspond to the third phase of work under the SEMAG supervision) and the timing (pre‐election period) suggests that participation is a way to interpret the interests of the planner as a general will, based on a local consensus to legitimize its actions.” (Lahaye Nathalie 2008). Thus this kind of consultation where there isn’t a mutual equality in the le‐ gitimacy to use knowledge can’t result in a hypothetical solution on a basis of its co‐utilization.

4.5 Mutual kowledge To enable a multi‐ontological use of knowledge, each ontology has to be considered as equals. If we consider Alain Le Pichon’s analysis, it is possible through mutual knowledge, “the art of discovering and producing a concise network of 'relations of relations’” (Le Pichon, 2004). This, in theory, allows the creation of relations between ontologies because they are sets of relations. “Mutual knowledge is built step after step and develops through mutual acceptance and recognition of the other's models” (Le Pichon, 2004). Le Pichon illustrates this acceptance and recognition with the visual play of anamorphosis, in which, by adjusting the arrangement of "these mirrors of hard, distorting glass, which is the way a given [ontology] looks onto another, [we get] the common field of mutual knowledge"(Le Pichon, 2004). Once different ontologies reached a mutual acceptance, they can go through the consultation process to use jointly their knowledge, since none of them could be said superior to another. By using several knowledges, informations will be associated to the context of use and should be interpreted as knowledge by those who participated on it. In theory the co‐utilization of knowledge could lead to informations merging. Applied to Taonaba, it means that the land planning would possess the information needed to generate knowledge for both the Maroons and the sustainable development group. To understand the feasibility of our process, we have to find a context where different meanings can be extracted from it.

4.6 Heterotopy and information interpretation Michel Foucault’s works on heterotopies stages this plural sense given process in a single context. “There are also, probably in every culture, every civilization, real places, actual places, places that are shaped in the very fabric of society, and which are kinds of counter‐sites, kinds of utopias actually achieved where the real sites, all the other real sites that can be found within a culture are simultaneously represented, questioned and inverted. Places of this kind are not part of any place, even though it may be possible to indicate their location. The places, because they are intrinsically different from the sites they reflect and speak about, I shall call them, by way of contrast with utopies, heterotopies.” (Foucault, 1984). Paraphrasing Michel Foucault, we could say that information functions as a heterotopy, since it has to pass through the virtual point of the ontology for the emerging knowledge to be real. Heterotopies confirm that’s it possible to put together several informations in a single context to recreate different knowledges. Therefore, in a multi‐ontological situation, a solution through a consultation process with mutual recognition can emerge from the co‐utilization of knowledge thanks to information heterotopy. This paper demonstrated that an inadequate use of knowledge can trigger a crisis in multi‐ontological con‐ texts. Most of companies have to deal with this kind of situation with their employees and customers. It’s especially true with the globalization. This ontological classification is needed to manage knowledge ade‐ quately according to people, giving the possibility to propose the right information to the right person who will interpret it as useful knowledge and therefore it should improve companies’ performances. Further researchs on informations merging, its interpretation by different ontologies and the emerging knowledges will be done to fully understand the process.

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Figure 10: Informations heterotopia through consultation with mutual recognition and multi‐ontological knowledge use

References: Descola, P. (2005), “Par‐delà nature et culture“, Editions Gallimard. Descola, P. (2006), “Universalisme absolu, universalisme particulier, universalisme relatif”, exposed at the UNESCO the 16 june. Lahaye, N. (2008), "Evaluation of participation and influence graph for a participatory governance in ecotourism. Le cas du projet écotouristique Taonaba en Guadeloupe", XLVth Conference of the association of regional science of french language, territories and public territorial action: new ressources for the regional development, [online], http://asrdlf2008.uqar.ca/Papiers%20en%20ligne/LAHAYE‐evaluation.pdf Le Pichon, A. (2004) “Stratégies transculturelles pour un monde multipolaire”, [online], http://www.tribunes.com/tribune/alliage/55‐56/LePichon.html. Malafsky, G. P. and Newman, B., “Organizing Knowledge with Ontologies and Taxonomies”, [online], http://www.techi2.com/download/Malafsky%20KM%20taxonomy_ontology.pdf Tsuchiya, S. (1993), “Improving Knowledge Creation Ability through Organizational Learning” ISMICK'93 Proceedings, Inter‐ national Symposium on the Management of Industrial and Corporate Knowledge, UTC, Compiègne, October 27‐28.

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Intelligence in the Oil Patch: Knowledge Management and Competitive Intelligence Insights Helen Rothberg1 and Scott Erickson2 1 Marist College, Poughkeepsie, USA 2 Ithaca College, Ithaca, USA hnrothberg@aol.com gerickson@ithaca.edu Abstract: The fields of knowledge management and competitive intelligence have been joined in the literature for over a decade, as scholars recognized the emphasis in each field on developing knowledge, albeit of different types. While knowledge management is often limited to the human, structural, and relational capital of the firm, competitive intelligence is more outward looking, building a broadly sourced knowledge base concerning competitors. In fact, practitioners are one step ahead of academia in this application as many organizations have a connection between their knowledge management and competitive intelligence functions. In extensive depth interviews to ascertain the state of intelligence work of all types in contemporary industry, we found such an inclination to be prominent in a number of specific industries. One of these was oil and gas. While exploration, recovery, refining, transportation, and retail are all separate aspects of this broad field, it is collectively of interest, in large part because of this extensive scope. In this paper, we compare and contrast knowledge management and competitive intelligence practice in oil‐based industries. In doing so, we draw upon an extensive database including financial returns of thousands of companies in a broad range of industries over a five‐year period. Looking specifically at industries related to oil and gas, we review data concerning the level and importance of knowledge assets in each industry. Included in the database is additional information on competitive intelligence activity in each industry. We add these figures to the analysis, allowing us to assess the relative competitive intelligence threat levels. Finally, we discuss the results from the depth interviews we conducted with practitioners in these industries, sharing their perspective on the nature of knowledge management, competitive intelligence, and the interplay between them in this complex industry. Keywords: knowledge management, competitive intelligence, strategy, Tobin’s q

1. Background Knowledge management (KM), intellectual capital, and competitive intelligence (CI) are all fields that grew up together over the past twenty‐five years. A full review of all three disciplines in a short paper is almost impossible, so this literature summary will focus on some major concepts in KM and CI, similarities and differences, and how the fields interact. KM and its related field, intellectual capital, evolved out of an interest in determining how and why firms are more competitive in the marketplace. As an extension to the resource‐based theory of the firm (Wernerfelt 1984), scholars suggested that one unique, sustainable resource of the firm might be knowledge (Teece 1998; Grant 1996) and, indeed, this might be the only unique source of competitive advantage in the modern economy. Interest grew in what an organization’s people might know and how that could be assessed, managed, and employed to best effect. Intellectual capital theory and practice made up one side of this effort, specifically directed at defining and measuring the knowledge assets of the organization. These assets went beyond traditional intellectual property (patents, copyrights, etc.), including less well‐defined, softer knowledge. Firms like Skandia (Edvinsson & Malone 1997), the general business press (Stewart 1997), and scholars (Bontis 1999) all worked at developing definitions and metrics. These and related efforts resulted in the familiar categories and concepts of human capital, structural capital, and relational capital we know today. While intellectual capital is chiefly concerned with the stock of knowledge assets, knowledge management has more to do with effectively managing and growing them (Zack 1999; Grant 1996). KM typically focuses on the nature of knowledge assets, organizational differences, and systems to best handle these differences while gaining participation from individuals throughout the firm. Regarding knowledge, the distinction between tacit and explicit knowledge (Polanyi 1967) is a critical one, particularly in terms of how to best develop those knowledge assets (Nonaka & Takeuchi 1996). Depending on the extent of tacitness or explicitness, different approaches and techniques have been developed to aid person‐to‐person sharing or use of more digital

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Helen Rothberg and Scott Erickson approaches (Choi & Lee 2003; Schulz & Jobe 2001; Boisot 1995). Additional knowledge characteristics that may matter include complexity and specificity/stickiness (McEvily & Chakravarthy 2002; Zander & Kogut 1995; Kogut & Zander 1992). Organizational variables include aspects such as the absorptive capacity (Cohen & Levinthal 1990) and the social capital (Nahapiet & Ghoshal 1998) of an individual firm. Depending on the circumstances of a given organization, particular approaches to KM can be chosen, including communities of practice, mentoring, and knowledge markets (Brown & Duguid 1991; Matson, Patiath & Shavers 2003; Thomas, Kellogg & Erickson 2001). Each also poses its own issues with workability, including how variables such as motivation and trust can influence participation. It’s really a matter of choosing the right approach for the circumstances of the firm and can be a complex decision. Competitive intelligence practice and study also grew during the past quarter century. The legal and ethical side of economic espionage, CI is the practice of collecting data and information about a competitor and/or its activities, processing and analyzing it for competitive insights, and acting on the results. As is the case with KM, CI also developed around practice (Gilad & Herring 1996; Fuld 1994) as it was being noticed by the academic community (Prescott & Miller 2001). Scholarship on CI, much like KM, has focused on sources of information/knowledge (McGonagle & Vella 2002) and techniques for using it (Fleisher & Bensoussan 2002). The nature of the knowledge and organizational characteristics have been less of a concern but one could certainly see the field moving in that direction for future research. Of more interest to researchers are characteristics of CI teams or operations. Maturity appears to matter, with CI groups expanding their human intelligence networks and adding to their own analytical capabilities (Wright, Picton & Callow 2002; Raouch & Santi 2001). One place where CI has already arrived is in deeper analysis of knowledge assets. Looking for actionable intelligence, CI teams are charged with understanding a competitor, its current strategies, and possible future strategies and actions (Gilad 2003; Bernhardt 1993). KM is likely headed in this direction, especially as the advent of big data and business intelligence work widens the view of valuable intangibles to include analysable data and information. Even so, KM has yet to reach the same analytical level as CI. So KM and CI have a number of similarities in terms of the identification and gathering of valuable intangible assets and the use of specific tools and techniques to manage them (Rothberg & Erickson 2005; 2002). One other important interaction is in the likely increased CI vulnerability that comes from expanded KM efforts. One of the key aims of KM is to make more of a firm’s knowledge stock available for use by many more employees. The result is more access points inside and outside the core company for competitors’ CI operations, with access to a greater proportion of the firm’s knowledge or information, in hard‐to‐monitor digital form. Greater dispersal of valuable proprietary assets can make them more at risk (Liebeskind 1996). As a consequence, there is a balance to be struck between development of knowledge and its protection, a balance that has only recently begun receiving scholarly attention (Liebowitz 2006; Rothberg & Erickson 2005) This paper reports on a study to examine more closely the relationship and interplay between KM and CI in practice. Looking at both objective results from a substantial database of financial returns and competitive intelligence activity and more subjective responses from practitioner interviews, we can more deeply study how KM and CI are managed in these closely related industries.

2. Methodology and results In looking to analyze knowledge development and protection across a number of firms and industries, one needs a tool capable of a certain amount of scope. Past work (Erickson & Rothberg 2012; Rothberg & Erickson 2005) has established that differences exist on the national, industry, and firm level that impact knowledge, so using industry and firm as the level of analysis, paired with an appropriate metric, can yield the kind of information we’re seeking. Indeed, by following the Strategic Protection Factor framework, we can organize industries (and firms) by whether KM is important or not to industry success and whether CI is prevalent or not.

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Helen Rothberg and Scott Erickson Measuring the knowledge development in a firm can be done in any number of ways (Sveiby 2010). Micro approaches tend to add up knowledge asset components in the firm to get a sense of the total intellectual capital. Given the difficulties in accessing such data, however, these approaches are usually limited to analysis of a single firm or a small group of related firms. This is how they have been used in practice (Lev & Radhakrishnan 2003; Marr & Schiuma 2001). By taking a more macro approach and using more available financial data, many more firms can be analyzed at one time, allowing comparisons across industries (Tan, Plowman & Hancock 2007; Firer & Williams 2003). Such metrics are more appropriate in this case. In particular, we apply a variation on Tobin’s q (Tobin & Brainard 1977) that has been used previously in such applications (Erickson & Rothberg 2012; 2009). Tobin’s q assesses the level of intangible assets of the firm, largely overlapping with the concept of the intellectual capital. The original value proposed by Tobin was market capitalization versus replacement value of assets. Replacement value of assets can be difficult to obtain, however, so a common variation is market cap to book value. For our purposes, we use market cap to asset value—the difference being that book value subtracts out liabilities giving debt levels an impact on the measure (this makes a difference for industries like financial services with huge levels of borrowed capital). We just want to know the productivity of the firm given a certain tangible asset level, borrowed or owned, so we tend to prefer the market cap to assets approach. But both are included here for context. We also employ the metric as a ratio, eliminating firm size as a potential source of bias. Financial data were obtained from the I/B/E/S service, including all firms trading on North American exchanges with at least $1 billion in annual revenue. Data from 2005‐2009 were included with over 7000 observations from over 2000 firms. The overall average for the market cap to asset ratio for the database was 1.02. The overall average for the alternate market cap to book value metric was 2.68. Firms were grouped by industry (according to Standard Industrial Classification number) and industries with at least twenty observations were included in the analysis. For this paper, we looked at industries involved in oil and gas exploration, drilling, refining, and transmission/delivery. Competitive intelligence metrics were taken from a benchmarking database constructed by Fuld & Company, a major CI consultancy. These data were collected over a similar five‐year period (2005‐2009) and include over 1,000 worldwide respondents. We used a specific question on the maturity/professionalism of the CI function in the respondent’s organization as our indicator. CI professionals included in the results rated their group’s proficiency along a four‐point scale, with 4 designating a highly developed capability and 1 suggesting a more ad hoc function. Again, we could group these by industry, using the same SIC codes and including specific organizations in the same place/industry in the data set. Depth interviews were conducted with practitioner contacts, participants solicited from training programs conducted by the authors, and other outreach efforts. No particular targeting by industry was done, participants were selected who participate in KM or CI (often both) at a senior level and with some substantive experience in at least one of the fields. Semi‐structured interviews were conducted focusing on KM practice in their organization, CI practice, and any relationship between the two. Significant probing was done to provide additional depth. Results are presented in Table 1. These include both Tobin’s q metrics to assess the level of knowledge development required to compete in the industry (Cap/Assets, Cap/Book), our KM measures. Competitive intelligence activity is shown by the number of firms at each level of proficiency. A single firm at level 2 was the midpoint for the overall database, below that suggested low CI activity while multiple firms at 2 or at least one firm reporting 3 or above shows high CI activity. We also included the SPF (strategic protection factor) from Rothberg & Erickson (2005) illustrating the competitive conditions facing each firm in terms of the combination of KM and CI results. We’ll talk more about these shortly.

3. Discussion There are a number of interesting things in the data, especially when paired with some of the insights from the interviews. But, as a first pass, let’s focus on the nature of the data. The cap/asset ratio shows a range of knowledge development in the industries, some well above the overall average (drilling and refining), some

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Helen Rothberg and Scott Erickson below (exploration, transmission/distribution). Cap/book has a similar range of results but all the numbers are below the average of the full database. So this is an application where the choice of KM metric does matter and there is obviously something in the data leading to the differing results—probably the drilling and refining industries have substantially higher levels of debt, especially when compared to the transmission/distribution industries. As debt is not an important part of what we are trying to analyze, we believe the cap/asset result is the more reliable and preferred option. Table 1: Oil & Gas Industries, knowledge management and competitive intelligence status Cap/Book

Cap/Assets

Cap/Book

1311 Crude Petroleum and Natural Gas Exploration

0.85

1.85

1381 Drilling Oil/Gas Wells

1.37

291 Petroleum Refining

4922 Natural Gas Transmission

2.25

1.93

2.42

0.58

4923 Natural Gas Transmission & Distribution

0.80

4924 Natural Gas Distribution

0.58

1.82 2.25 1.92

Competitive Intelligence (# firms, # respondents) 4 (0) 3 (0) 2 (4) 1 (0) 4 (1,6) 3 (0) 2 (0) 1 (0) 4 (0) 3 (1) 2 (1,2) 1 (0) 4 (0) 3 (0) 2 (1) 1 (0) 4 (0) 3 (0) 2 (0) 1 (0) 4 (0) 3 (0) 2 (0) 1 (0)

SPF Category SPF 30 Low KM High CI SPF 45 High KM High CI SPF 45 High KM High CI SPF 5 Low KM Low CI SPF 5 Low KM Low CI SPF 5 Low KM Low CI

The CI metric shows the number of firms and individuals within the firms reporting on their level of proficiency. Exploration, drilling, and refining all have higher than average levels of CI activity. In exploration, multiple firms (four) are all operating at a fairly organized level. Drilling only shows a single firm reporting a CI operation but it is at the highest level and included six team members independently responding to the benchmarking study, something very unusual in the dataset. Even if only a single firm, when you have someone in the industry operating at that level, it has major implications for the vulnerability of information and knowledge for everybody (as well as for the need for protection and counterintelligence). In refining, there are again multiple firms active in CI, at the second and third levels, and one of those firms again has more than one respondent. Once more, this is in the upper half of CI activity for the full database. Such metrics, on their face, may not seem to illustrate substantial CI operations, but the people reporting in the survey are competitive intelligence professionals, generally managers of the group. So a single responder can be indicative of a much bigger operation, particularly when reporting above the lowest level of proficiency. The three transmission/distribution industries show less activity. Only one individual reports an active operation in the three industries, and that is at the next to lowest proficiency level. Taken individually, all three industries are below the overall average for CI activity. In terms of the Strategic Protection Factors represented here, the main point is that there are different industry conditions. In some industries, there is clear evidence of substantial development of intangibles or knowledge assets. Firms would probably need to aggressively grow their knowledge in such industries in order to keep up with competitors. In other industries, there is no indication of significant knowledge development and so no such mandate for investment in knowledge by member firms. Similarly, there are industries with heavy CI activity, industries with no CI activity, and a range of other results in between. What the SPF categorization does is indicate these conditions—where KM investment is important (or not) and where CI activity and/or protection is needed (or not). Such evidence leads to a logical conclusion that KM decisions

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Helen Rothberg and Scott Erickson may be more strategic than we often think (many KM scholars would recommend ever more investment into development) and that a better understanding of these types of conditions could lead to better spending decisions on both KM systems and CI offense and defense. Depth interviews included conversations with four practicing managers working for oil and gas companies, chiefly in competitive intelligence. As might be expected, size of CI operations varied dramatically across such a small sample, from virtual teams formed for specific purposes to core groups of 30‐40 to loose networks of up to 100 contributors. Budgets also varied dramatically, when individuals were willing to report them, from a couple hundred thousand dollars to $75 million at one large integrated multinational. Key commonalities across the interviews included the distributed nature of many of the operations, being both task specific (information on joint ventures, mergers and acquisitions, market conditions, market entry, competitor strategies) or group specific (country, function, etc.). At the same time, there was a recognition that CI functions tend to become more centralized as they matured and senior management become more convinced of their value. All of the respondents noted the often close relationship between CI and KM, even if all efforts to integrate the two functions weren’t successful. Both functions, often managed though the same office, seek to gather information and knowledge from throughout the firm and its larger network. Respondents noted a desire to incorporate the KM network into CI information‐gathering and also using techniques such as communities of practice to good effect in both areas. There were comments about protecting knowledge, noting that these industries tend to be leaky. As a result, although knowledge might be gathered from throughout the firm and its extended network, it was not necessarily shared back out through the whole structure. Key knowledge was kept internal. What can we conclude from the data and the interviews about these industries and their wider implications? Knowledge has different levels of importance at various points along the value chain, both in the firm and across the industry‐wide chain. Firms that have a mandate to aggressively develop knowledge will often have key knowledge present in several places along the value chain, not just in a single spot (e.g. operations). That is seen clearly in this example. The industry value chain includes exploration through drilling, refining, and eventually transmission/delivery. One could include retail as well, though separating out gas stations in the data is difficult. Across this chain, the really valuable knowledge, at least from a knowledge development perspective, is in drilling and refining. This was reiterated in the interviews where those were the functions often mentioned by the respondents as being the key areas of attention for their offices. Not all knowledge is equally valuable or manageable, and this is true across industries and even across an individual firm. Firms integrated across several distinct functions or industries, as a number of the major oil and gas majors are, should expect to face different conditions in these different arenas. Again, the value of the knowledge may differ. The range of competitive intelligence operations and activities may differ. The interplay between KM and CI may differ. Once more, there is strong evidence that a strategic approach is best, examining the knowledge development and protection conditions as they apply in each setting. Part of that task would be understanding the type of knowledge involved. In some parts of the oil industry, the valuable knowledge is more tacit, potentially more valuable but also harder to manage effectively. Tacit knowledge is also harder for competitors to take by standard CI techniques. Similarly, if knowledge is more sticky or specific, it can have implications for sharing or competitive infiltration as could the complexity of the knowledge. The maturity of the industry comes into play and how much new, proprietary knowledge is being developed that is not easily available to everyone in the field. So drilling or refining, where tacit know‐how can be extremely important but also quite personal, complex, and perhaps sticky poses a very different knowledge development/protection scenario than does something like transmission, where much knowledge is explicit, well‐known throughout the industry, and complex but manageable using readily available logistics programs. The bottom line is, again, conditions differ, and strategists would be well‐advised to understand the full conditions surrounding their knowledge development and knowledge protection decisions.

4. Conclusions This paper has looked at knowledge practices in various oil and gas industries, specifically addressing the question of whether decision‐makers should take more strategic decisions regarding knowledge development

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Helen Rothberg and Scott Erickson and protection. While the natural inclination of most of us working in the fields of KM and CI is that more is always better, both theory and practice suggest that sometimes a more measured approach may be better. Knowledge has different levels of value in different industries included under the oil and gas designation. Development is critical in order to compete in industries such as drilling and refining while it may be less a priority in areas like exploration and transmission/distribution. Similarly, competitive intelligence can be a major threat, or not. CI activity levels are high in exploration, drilling, and refining but almost non‐existent in transmission/distribution. These industries also demonstrate an increasing integration in the KM and CI operations, according to respondent reports. As a result, what we see in these industries helps to make the case for the more strategic approach to knowledge development and protection. Evaluating circumstances can help in determining when making larger investments in KM will pay off. Similarly, such strategic planning can better focus investments in CI offense and defense. Taking such a wider view can help increase the odds that KM and CI initiatives will actually pay off, providing greater opportunities for the disciplines to make a true contribution to modern business success.

Acknowledgements The authors gratefully acknowledge the generosity of Fuld & Company in providing some of the data used in this study.

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Helen Rothberg and Scott Erickson McEvily, S. & Chakravarthy, B. (2002) “The Persistence of Knowledge‐Based Advantage: An Empirical Test for Product Performance and Technological Knowledge”, Strategic Management Journal, 23(4), 285‐305. Nahapiet, J. & Ghoshal, S. (1998) “Social Capital, Intellectual Capital, and the Organizational Advantage”, Academy of Management Review, 23(2), 242‐266. McGonagle, J. & Vella, C. (2002). Bottom Line Competitive Intelligence, Quorum Books, Inc., Westport, CT. Nonaka, I. & Takeuchi, H. (1995) The Knowledge‐Creating Company: How Japanese Companies Create the Dynamics of Innovation, Oxford University Press, New York. Polanyi, M. (1967) The Tacit Dimension, Doubleday, New York. Prescott, J.E. & Miller, S.H. (2001) Proven Strategies in Competitive Intelligence: Lessons from the Trenches. New York: John Wiley and Sons. Raouch, D. & Santi, P. (2001) “Competitive Intelligence Adds Value: Five Intelligence Attitudes. European Management Journal, 19(5), 552‐559. Rothberg, H.N. & Erickson, G.S. (2005) From Knowledge to Intelligence: Creating Competitive Advantage in the Next Economy, Elsevier Butterworth‐Heinemann, Woburn, MA. Rothberg, H.N. & Erickson, G.S. (2002) “Competitive Capital: A Fourth Pillar of Intellectual Capital?”, in Bontis, N., ed. World Congress on Intellectual Capital Readings, Elsevier Butterworth‐Heinemann, Woburn, MA. Schulz, M. & Jobe, L.A. (2001) “Codification and Tacitness as Knowledge Management Strategies: An Empirical Exploration”, Journal of High Technology Management Research, 12, 139‐165. Stewart, T.A. (1997) Intellectual Capital: The New Wealth of Organizations, Doubleday, New York. Sveiby, K‐E (2010) “Methods for Measuring Intangible Assets”, http://www.sveiby.com/articles/IntangibleMethods.htm, accessed 4/4/2012. Tan H.P., Plowman, D. & Hancock, P. (2007) “Intellectual Capital and the Financial Returns of Companies”. Journal of Intellectual Capital. 9(1), 76‐95. Teece, D.J. (1998) “Capturing Value from Knowledge Assets: The New Economy, Markets for Know‐How, and Intangible Assets”, California Management Review, 40(3), 55‐79. Thomas, J.C., Kellogg, W.A. & Erickson, T. (2001) “The Knowledge Management Puzzle: Human and Social Factors in Knowledge Management”, IBM Systems Journal, 40(4), 863‐884. Tobin, J. & Brainard, W. (1977) “Asset Markets and the Cost of Capital”, in Nelson, R. & Balassa, B., eds. Economic Progress, Private Values, and Public Policy: Essays in Honor of William Fellner, North Holland, Amsterdam. Wernerfelt, B. (1984) “The Resource‐based View of the Firm”, Strategic Management Journal, 5(2), 171‐180. Wright,S., Picton, D. & Callow, J. (2002) “Competitive Intelligence in UK Firms, A Typology”, Marketing Intelligence and Planning, 20(6), 349‐360. Zack, M.H. (1999) “Developing a Knowledge Strategy” California Management Review. 41(3), 125‐145. Zander, U. & Kogut, B. (1995) “Knowledge and the Speed of Transfer and Imitation of Organizational Capabilities: An Empirical Test”, Organization Science, 6(1), 76‐92.

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To Study the Relationship Between Knowledge Utilization and Learning Capability in a Team Manasi Shukla Institute of Knowledge & Innovation, South East Asia Bangkok University Thailand shukla.manasi@gmail.com manasi.s@bu.ac.th Abstract: In this empirical paper, we study the relationship between knowledge utilization and learning capability in a team environment. The knowledge utilization concept has received scant attention so far. While knowledge transfer rests solely on the knowledge flows, the novel construct of knowledge utilization involves the knowledge linkages. These linkages provide a channel through which information may flow from one sub‐system to the other in an organization. But, the effectiveness of these linkages in terms of insights gained and actions taken incorporating the gained know‐how is what typifies the knowledge utilization in a team. The knowledge utilization capability builds upon certain intra‐organizational mechanisms and processes (Backer, 1991). The high quality knowledge utilization may be considered strategic asset for learning capability in a team and vice versa. Hamel (1991), for instance suggests that conditions within the teams and the relationship itself provide the platform for higher learning. The Learning Capability is classified in five dimensions. Firstly, the learning intent entails the team’s goal directed behavior with regard to learning (MacInnis, Mooreman, & Jaworski, 1991). Secondly, transparency concerns the opportunity to learn. Thirdly, the receptivity connotes the team’s capacity or potential to learn. Fourthly, dissemination of knowledge means that the knowledge is passed around the team to the relevant managers in relevant functional areas. Fifthly, shared interpretation of knowledge inputs means that there is consensus about the meaning of the knowledge. These components of learning capability thus form a team’s learning platform. It is hypothesized that the knowledge utilization levels increase when learning capability is present. Likewise, the intra‐team learning is enabled strategically. Such teams would perform better by accomplishing their management defined goals if the above discussed learning activities and their supporting learning platform are present simultaneously. Keywords: knowledge utilization, learning capability, learning intent, receptivity, transparency, shared interpretation

1. Introduction As the industrial economy fades, a new economy arises out of the interlocking and dynamic forces of expanding globalization, accelerating technology, and shifting demographics. In this new environment, performance improvement through deliberate, systemic, and results‐oriented knowledge utilization provides a “meta‐strategy” to leverage organizational knowledge oriented capabilities (Sung & Gibson, 2005; Abraham & Knight, 2001). Knowledge is information whose validity has been established through tests of proof, has emerged as a strategically significant resource of the firm. While many businesses feel that they are suffering due to lack of knowledge, in the majority of cases it is probably more likely that it s due to lack of utilization of the available knowledge. This may be particularly true for large companies with different divisions and business units scattered throughout the globe. Thus, in this paper we strive to study the impact of a modern day learning organization on knowledge utilization capability

2. Knowledge utilization capability To help the business transformations today a dynamic capability is proposed which is called the knowledge utilization capability. This capability results in new knowledge or modifications in the existing system of knowledge via the interactions among the various sub‐systems or parts of the knowledge flow system. The concept of knowledge flow system involves all resources involved in development and application of meaningful knowledge (Duncan, 1972). This flow system in itself enables knowledge transfer that has been a frequently researched topic. On the contrary, the knowledge utilization concept has received scant attention so far. While knowledge transfer rest solely on the knowledge flows, the novel construct of knowledge utilization involves the knowledge linkages. These linkages provide a channel through which information may flow from one sub‐system to the other in an organization. Havelock (1969) claims that such a linkage with information flow exists when two sub‐systems within an organization are connected by a regularized pattern of information flow in the overall picture of the organizational system. The examples of such a knowledge linkage are the vendor’s public relations materials shared with the clients and the client driven training exercises for new and existing employees in a team. These knowledge linkages enable knowledge flows between client and vendor resulting in the knowledge transfer. But, the effectiveness of these linkages in

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Manasi Shukla terms of insights gained and actions taken incorporating the gained know‐how is what typifies the knowledge utilization capability of team. The realm of knowledge utilization capability via the theory of autopoiesis (Maturana & Varela, 1980, 1987) explains KUC. This theory originated in the field of neurobiology for understanding cell reproduction. In cell reproduction, not only the cells are reproducing themselves, but they are also reproducing their own capacity to reproduce. For an organism, this implies that most important process to maintain over time is the autopoietic process whereby knowledge is created and recreated. Through its application in the social sciences, autopoiesis theory also emerges as the new science of knowledge of a social system (Luhmann, 1986). The above view has found support in both the literature of strategic management and organizational theory. According to this view, knowledge utilization capability results in organizational knowledge which is highly dynamic, fragile, and developed through a self‐referential, simultaneously open and closed autopoietic process for knowledge creation and recreation using the knowledge linkages and connections. This means that unless there are these knowledge connections made available, the knowledge at one point in time does not connect with the new knowledge at a later point in time. (Von Krogh, Roos & Slocum, 1994). Thus, there are two conditions that need to be satisfied for this. The first one is to do with the availability of relationships, which enable immediate knowledge connections. The examples of these are the informal employee networks and reporting structures that are promoted by a relationship governance mechanism in a team. The second precursor to this is a self‐description of the team itself in terms of its strategic directions. The examples of these are the shared business ideas and management principles in a relationally governed team. The knowledge utilization capability builds upon certain inter‐organizational mechanisms and processes. Backer’s (1991) synthesis of the knowledge utilization summarizes six critical strategies of interpersonal contact, planning and conceptual foresight, external consultation, user‐oriented translation of knowledge, individual or organizational championship of knowledge and individual ownership or involvement for knowledge utilization within a firm. Such interpersonal linkages imply the characteristics of learning organizations. The learning capability of an organization thrives on such learning mechanisms like linkages and hence is explained in the following section.

3. Learning capability Organizational learning has its multi‐disciplinary origins in both marketing and strategy (Levinthal & March, 1993; Sinkula, 1994; Slater & Narver, 1995). The high quality inter‐firm relationships may be considered strategic assets for learning (Achrol, 1991; Day, 1994; Johnson, 1999; Larson, 1992). Hamel (1991), for instance suggests that conditions within the partner firms and the relationship itself provide the platform for higher learning. Slater and Narver (1995) draw on Fiol and Lyles (1985) conceptualization of learning to propose a process model of inter‐organizational learning that involves the presence of important activities of the partner firms. This is governed by the third prerequisite for the existence of a strategic capability which is the general managerial technique. It consists of the necessary organizational expertise in general management and a responsive administrative arrangement to coordinate human effort towards team learning. The key inter‐organizational learning activities are dissemination and a shared interpretation of information. Dissemination of knowledge means that the knowledge is passed around the team to the relevant managers in relevant functional areas. Shared interpretation of knowledge inputs means that there is consensus about the meaning of the knowledge. The above learning activities of dissemination and shared interpretation of knowledge do not happen automatically (Badaracco, 1991; Hamel, 1991; Slater & Narver, 1995) rather they depend upon the factors in the team culture (entrepreneurship, market orientation), and climate (organic structure, facilitative leadership, decentralized strategic planning). With the same underlying reasoning, Hamel (1991) suggested that learning between firms in strategic alliances depends on factors that can be considered as components of the climate or culture in an intra‐team relationship. These components are the learning intent, receptivity and transparency which provide the team’s learning platform. Firstly, the learning intent entails the team’s goal directed behavior with regard to learning (MacInnis, Mooreman, & Jaworski, 1991). It is the organizational counterpart of motivation. Stated in simple terms it is the team’s desire to learn. Secondly, transparency concerns the opportunity to learn. Between firms in strategic alliance, Hamel (1991) suggested that transparency involves the penetrability of the partner firms. Simply stated it reflects the openness of the team to learning. Thirdly the receptivity connotes the team’s

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Manasi Shukla capacity or potential to learn. Hamel (1991) defines receptivity as the team’s ability to actually absorb knowledge (Cohen & Levinthal, 1990). It is generally analogous to an ability to learn in a team. Thus, learning intent, transparency and receptivity together are synonymous to the motivation, opportunity and ability (MOA) model (MacInnis, Mooreman, & Jaworski, 1991). For instance, they found that the brand information processing levels increase when MOA is present. Likewise, the inter‐firm partnership is enabled strategically when the firm team has strong learning intent (motivation), is highly receptive (ability), and has high levels of transparency (opportunity). Thus, the teams would perform well by accomplishing their partner defined goals if the above discussed learning activities and their supporting learning platform are present simultaneously.

4. Hypothesis Teams can significantly influence inter‐firm learning as is the case observed in most of the teams. For such relationally governed exchanges in these alliances, the enforcement of obligations, promises and expectations occurs through social processes that promote norms of flexibility, solidarity and information exchange (Poppo & Zenger, 2002). Thus, by bringing together the firms with unique skills and capabilities, alliances can foster a unique learning atmosphere. The foremost requirement for inter‐firm learning is that this targeted alliance know‐how may be accessible to the strategic partners (Inkpen, 1998). Increasing levels of trust in the relational governance mechanism yield to an atmosphere of creative learning. This phenomenon contributes to the free exchange of information between committed exchange partners since the decision‐makers do not feel that they have to protect themselves from the others’ opportunistic behavior. This restricts competitive pressures of survival and leads to more interorganizational learning (Powell, Koput, & Smith‐Doerr, 1996). Several authors (Arad, Hanson, & Schneider, 1997; Lock & Kirkpatrick, 1995; Samaha, 1996) indicate that an organizational learning orientation encourages knowledge utilization. This may be fostered by encouraging the personnel in a team to interact with each other and keeping their skills and knowledge up to date. Increasing innovation is manifested by rapid change. This implies quicker knowledge obsolescence and entails constant internal adaptation including new strategies, structures, processes and tools and most importantly an undying need for people and organizations to learn quickly (Prusak, 1997). While relational governance in a team may or may not have a significant relationship with architectural innovation, it is proposed that this relationship will be stronger under increased levels of learning capability of the team. Hypothesis: Learning capability in a team would increase the likelihood of knowledge utilization capability in a team.

5. Methodology The methodology adapted is a focus group discussion within a team environment in this paper. A team gives inputs on the variables of study in this study viz. the learning capability and its dimensions and the knowledge utilization capability. This is just exploratory analysis of data to confirm the hypothesis is this study. A large scale survey using the instrument in this study as per the Table 1 shall be carried out for validating the hypothesis and the validity of five dimensions of learning capability. Table 1: The Instrument for knowledge utilization capability and the learning capability dimensions Knowledge Utilization Capability

Shaperman & Backer, 1995; Kogut & Zander, 1992

Learning Capability

(a). Learning Intent

Hamel, 1991; MacInnis Mooreman, & Jaworski, 2003; Johnson & Sohi, 2003

(b). Transparency

Hamel, 1991; Johnson & Sohi, 2003

The knowledge utilization is extremely high. (ku1) We utilize most of the available vendor and client knowledge resources. (ku2) We possess the knowledge utilization capability. (ku3) Our intent is to learn all we can about how to be an effective team member. (LC) We aim to know and understand as much as we can about our partner’s existing business practices. (lc11) There is a lot of incentive for learning ways to improvise on the client’s business practices. (lc12) We view close relationships with our alliance partners for the purpose of rapport building in our team. (lc21) We take advantage of every opportunity to understand our partner’s technical and business priorities. (lc22) We have sufficient awareness of the strategic targets and goals of

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Manasi Shukla Knowledge Utilization Capability

Shaperman & Backer, 1995; Kogut & Zander, 1992

(c). Receptivity

Hamel, 1991; Johnson & Sohi, 2003

(d). Dissemination of Information

Slater & Narver, 1995; Johnson & Sohi, 2003

(e). Shared Interpretation

Slater & Narver, 1995; Johnson & Sohi, 2003

The knowledge utilization is extremely high. (ku1) We utilize most of the available vendor and client knowledge resources. (ku2) We possess the knowledge utilization capability. (ku3) our team. (lc23) We have developed systems that facilitate learning from our relationships with partners (lc31). We have a strong capacity for learning how to be more responsive in our partner relationships. (lc32) We are highly receptive to learning about the technical, business and industry related knowledge of our team members. (lc33) We are open to incorporating business process improvements that lead to greater efficiency in the team. (lc34) Information related to the team performance is disseminated regularly. (lc41) We discuss any new developments in our partner relationship.(lc42) We openly discuss operational or management issues with others. (lc43) If something important happens with a partner everyone involved is quickly informed. (lc44) If a program is successful, we try to understand what made it work well. (lc51) We quickly try to identify our mistakes so that they are not repeated. (lc52) We constantly assess and analyze the effects of our decisions so that we know what adjustments to make.(lc53) If a mistake has been made, we retrace our steps and actions to understand what has happened. (lc55)

6. Focus group data analysis and exploratory findings For assessing the validity of the above mentioned questionnaire, the focus group interviews were carried out and data was analyzed using SPSS. Firstly to confirm the Hypothesis 1, the learning capability was regressed with average measure of knowledge utilization capability to yield a Beta of 0.465 as per Table 2. Since the Beta coefficient is approximately 0.5, we can say it confirms the hypothesis that knowledge utilization capability is impacted by Learning capability in a team Table 2: Regression analysis coefficients Unstandardized Coefficients Model 1

Standardized Coefficients

B 3.750

Std. Error 1.487

Beta

(Constant)

t 2.521

Sig. .045

.375

.292

.465

1.286

.246

a Dependent Variable: ku Average measure of knowledge utilization and the dimensions of knowledge utilization capability are found to be positively correlated that further corroborates our research framework as per Table 3. Table 3: Correlation coefficients ku1

Pearson Correlation Sig. (2‐tailed) N

ku2

Pearson

ku1

ku2

ku3

.646

.800(*)

.504

.917(**)

.084

.017

.203

.001

.646

.696

.302

.856(**)

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Manasi Shukla

ku3

Correlation

ku1

ku2

ku3

Sig. (2‐tailed)

.084

.055

.468

.007

.800(*)

.696

.433

.924(**)

.017

.055

.284

.001

.504

.302

.433

.465

.203

.468

.284

.246

.917(**)

.856(**)

.924(**)

.465

.001

.007

.001

.246

Pearson Correlation Sig. (2‐tailed) N

* Correlation is significant at the 0.05 level (2‐tailed). ** Correlation is significant at the 0.01 level (2‐tailed). Table 4 shows that the Knowledge Utilization capability can be explained as a single construct since its eigen values explain 81% of variance in the items used in this study. The Knowledge Utilization items are highly correlated and confirm to a single factor in Confirmatory Factor Analysis as per Table 5. Table 4: Exploratory factor analysis of knowledge utilization capability dimensions Initial Eigenvalues

Extraction Sums of Squared Loadings

Compon ent

Total

% of Variance

Cumulative %

Total

% of Variance

Cumulative %

2.430

81.004

2.430

81.004

.375

12.508

93.512

.195

6.488

100.000

Extraction Method: Principal Component Analysis.

Table 5: Single components extracted for knowledge utilization capability

Component

ku1

.909

ku2

.862

ku3

.928 Extraction Method: Principal Component Analysis. a. 1 components extracted.

The learning capability dimensions require a larger N for carrying out similar analysis like confirmatory factor analysis hence that is a future research agenda employing a survey data.

6.1 Final findings The hypothesis “Learning capability in a team would increase the likelihood of knowledge utilization capability in a team.” Is thus confirmed as depicted by Table 2. Average measure of knowledge utilization and the dimensions of knowledge utilization capability are found to be positively correlated as per Table 3 showing that the valid measure of knowledge utilization has been deployed. This is also confirmed by Table 4 that depicts the Knowledge Utilization capability as a single construct and further confirms to a single factor in

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Manasi Shukla Exploratory Factor Analysis as per Table 5. Additionally, learning capability dimensions require a larger N for carrying out similar analysis hence that is a future research agenda employing a survey data.

7. Conclusion Hence this study projects a scenario in a team environment in which it is proven that the dynamic capability of knowledge utilization requires learning capability as well is higher if higher learning capability is present. This is a preliminary exploratory study deploying focus group to validate initial hypothesis. In future research we would confirm additionally the five determinants of learning capability viz. learning intent, transparency, receptivity, dissemination and shared interpretation of knowledge would also moderate the degree of knowledge utilization in a team. Knowledge dissemination implies that it is shared and passed around the team to the relevant personnel and departments. Effective dissemination implies that knowledge can be seen in multiple and broadened contexts (Slater and Narver, 1995) within the team. Several authors (Arad, Hanson, & Schneider, 1997; Lock & Kirkpatrick, 1995; Samaha, 1996) indicate that such organizational learning activities encourage innovation. This processural tendency of a team is not actively promoted in a relationally governed organization. But, if present this increases the knowledge utilization capability of the team Shared interpretation of information or knowledge utilization implies that it has similar meaning to all in terms of the implications it has for the team. Slater and Narver (1995), classify this a learning organization’s core activity that is engendered by the top management’s directives and ethics. A shared vision from the top embodies the collective goals and aspirations of the members of an organization. When organization members have the same perceptions about how to interact with one another, they can avoid possible misunderstandings in their communications and have more opportunities to exchange their ideas or resources freely. Furthermore, the common goals or interests they share help them to see the potential value of their resource exchange and combination. As a result, organization members who share a vision will be more likely to become partners sharing or exchanging their resources. Several studies have shown that a shared vision (or a similar construct, such as goal congruence) may hold together a loosely coupled system and promote the integration of an entire organization (e.g., Orton & Weick, 1990). One can thus view a shared vision enabled shared interpretation of information’s meaning as a bonding mechanism that helps different parts of an organization to integrate or to combine resources and utilize knowledge in larger perspective.

References Abraham, J. L., and Knight, D.J. (2001). Strategic innovation: leveraging creative action for more profitable growth. Strategy and Leadership, 21‐26. Achrol, R. S. (1991). Evolution of the marketing organization: new forms for turbulent environments. Journal of Marketing, 55, 77‐93. Arad, S., Hanson, M.A. and Schneider, J. (1997). A framework for the study of relationships between organizational characteristics and organizational innovation. The Journal of Creative Behavior, 31(1), 42‐58. Backer, T. E. (1991). Knowledge utilization: The third wave. Knowledge: Creation, Diffusion, Utilization, 12(3), 225‐240. Badaracco, J. L. (1991). The knowledge link. Boston (MA): Harvard Business School Press. Cohen, W. M., and Levinthal, D.A. (1990). Absorptive capacity: a new perspective on learning and innovation. Administrative Science Quarterly, 35, 128‐152. Day, G. S. (1994). The capabilities of market‐driven organizations. Journal of Marketing, 58, 37‐52. Duncan, W. J. (1972). The knowledge utilization process in management and organization. Academy of Management Journal, 15, 273‐287. Fiol, C. M., and Lyles, M. A. (1985). Organizational learning. The Academy of Management Review, 10(4), 803‐813. Hamel, G. (1991). Competition for competence and inter‐partner learning within international strategic alliances. Strategic Management Journal, 12, 83‐104. Inkpen, A. (1998). Learning, knowledge acquisition, and strategic alliances. European Management Journal, 16(2), 223‐229. Johnson, J. L. (1999). Strategic integration in industrial distribution channels: managing the interfirm relationship as a strategic asset. Journal of Academy of Marketing Science, 27, 4‐18. Larson, A. (1992). Network teams in entrepreneurial setting: a study of the governance of exchange relationships. Administrative Science Quarterly, 37, 76‐104. Levinthal, D. A., and Fichman, M. (1988). Dynamics of interorganizational attachments: Auditor‐client relationships. Administrative Science Quarterly, 33(September), 345‐369. Levinthal, D. A., and March, J.G. (1993). The myopia of learning. Strategic Management Journal, 14, 95‐112. Lock, E. A., and Kirckpatrick, S.A. (1995). Promoting creativity in organizations. In C. M. Ford, & Gioia, D.A. (Ed.), Creative action in organizations: Ivory tower visions & real world voices (pp. 115‐120). London: Sage. Luhmann, N. (1986). The autopoiesis of social systems. In F. Geyer, & van der Zouwen, J. (Ed.), Sociocybernetic Paradoxes (pp. 172‐192). Beverly Hills, CA: Sage.

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Competency‐Based HRM and Lifelong Learning in Poland Lukasz Sienkiewicz1, Agnieszka Chłoń‐Domińczak2 and Katarzyna Trawińska‐Konador3 1 Educational Research Institute and Department of Human Capital Development Warsaw School of Economics, Warsaw, Poland 2 Educational Research Institute and Institute for Statistics and Demography Warsaw School of Economics, Warsaw, Poland 3 Educational Research Institute, Warsaw, Poland lukasz.sienkiewicz@sgh.waw.pl Agnieszka.Chlon@gmail.com k.trawinska@ibe.edu.pl Abstract: The strategic importance of intellectual capital and knowledge management is expressed primarily in the ability to build an organization capable of delivering value to the customers. This potential is demonstrated especially service sector companies, which due to the nature of value creation and value transfer to the customer, are largely knowledge‐ and intellectual‐capital dependent, particularly in the knowledge‐intensive services (KIS). However the representative empirical evidence of lifelong learning and competency‐based HRM practices in KIS in developing economies, such as Poland is scarce. Based on the results of the large‐scale representative survey of medium and large companies, we show that KIS sector engages employees more in various LLL activities compared to companies in less knowledge intensive service. Moreover, company approach to human resource management is an important determinant of decisions of educational activity of workers. Keywords: competency‐based human resources management, lifelong learning, human capital, knowledge‐intensive services (KIS)

1. Introduction Position of a company in the knowledge economy increasingly depends on the quality of its intangible resources, i.e. human capital. Today, human capital is a potential source of competitive advantage of a firm (Becker et al., 2001). Thus, contemporary organisations search for instruments to diagnose, evaluate and develop competencies of employees The position market leaders will be achieved not through physical and financial resources but through the stock of human capital (Perez & Ordonez de Pablos, 2003). Thus the theory of human resources may be linked in with the mainstream trend to promote significance of competencies, assuming that effective management of employees competencies builds the value of an organisation (Baron & Armstrong, 2008). Human resources management processes increasingly rely on relationships with employees formed on the basis of trust, reciprocity and provision of development opportunities within an organisation. As a result, the growing interest in alternative approaches to human resources management, including competency‐based perspective is observed. Competency‐based human resources management has been actively developing in the USA and Western European countries since the beginning of the 1990s as a practical manifestation of the popularity of the concept of “competencies” of employees in personnel management. It is a new trend in human resources management, which emphasises specific competencies utilised in a job, allowing for a more individual management and development of competencies within individual career paths (Brockmann et al., 2008; Klett, 2010). The competency‐based human resources management system is a set of coherent and mutually related human resources management practices from entering to an organisation by individuals, through their effective functioning, development to leaving the organisation by individuals. This approach seems especially significant in the case of knowledge‐intensive firms (KIFS), particularly those that provide knowledge‐intensive services (KIS). Strong base of knowledge and emphasis on the development of competencies are therefore key characteristics of knowledge‐intensive firms (Alvesson, 2009). Due to the scarcity of empirical data concerning the issue in the context of Polish companies, the quantitative study of Assessment of competency‐based human resources management in the context of lifelong learning (ZZL), has been designed and conducted in Educational Research Institute in Warsaw. The aim of the study was to investigate to what extent Polish employers adopt solutions related to the investment in their employees development and are aware of their role in a modern knowledge‐based economy. The study also looked at barriers they are to overcome in order to better contribute to economy development. Moreover, the results of

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Lukasz Sienkiewicz, Agnieszka Chłoń‐Domińczak and Katarzyna Trawińska‐Konador the study help to determine main obstacles to implement competency‐based management in large and medium enterprises in Poland and difficulties in implementing lifelong learning policy by entrepreneurs.

2. Methods To ensure the representativeness of the study and reduce the sampling error, the sample was stratified by: 1. size of enterprise (medium firms – employing between 50‐249 workers and large firms – employing over 250 workers enterprises); 2. area of activity divided into:

enterprises providing knowledge‐intensive services (KIS),

enterprises providing other services (less knowledge‐intensive ‐ LKIS),

industry enterprises.

Micro and small entities (employing less than 50 people) were excluded from the study due to the relatively low proportion of enterprises using formalised human resources management systems and. Pursuant to the international classifications of activities (NACE Rev. 2) sectors considered as knowledge‐ intensive services include: (1) knowledge‐intensive high‐tech services (NACE codes: 64, 72, 73); (2) knowledge‐ intensive market services, excluding financial intermediation and high‐tech services (NACE codes: 61, 62, 70, 71, 74); (3) knowledge‐intensive financial services (NACE codes: 65, 66, 67); (4) other knowledge‐intensive services (NACE codes: 80, 85, 92). The other services are classified as less knowledge‐intensive (NACE codes: 50, 51, 52, 55, 60, 63, 75, 90, 91, 93, 95, 99). The study also covered enterprises which conduct manufacturing activities. The total of 941 CAPI interviews were carried out. The basic structure of companies surveyed in specific subsamples is presented inTable 1, while the detailed structure is presented in Annex 2. In every subsample of the study results were obtained for more than 3% of the population of enterprises, which, in keeping with the sampling assumptions, which assured random choice of respondents, justifies extrapolation of the results to the whole population and generalisation of the study results. Table 1: Structure of surveyed companies’ sample Type of company KIS LKIS Industry TOTAL

Population (according to CSO) Medium Large TOTAL 7929 1107 9036 7121 1141 8262 9867 1850 11717 24917 4098 29015

Number of companies surveyed Medium Large TOTAL 248 34 282 235 35 270 328 61 389 811 130 941

Share of companies surveyed in total population (in %) Medium Large TOTAL 3,13% 3,07% 3,12% 3,30% 3,07% 3,27% 3,32% 3,30% 3,32% 3,25% 3,17% 3,24%

Source: own calculations based on data of Central Statistical Office of Poland and study results.

3. Competency‐based HRM and lifelong learning perspective 3.1 Lifelong learning context Lifelong learning is inextricably linked with the actions of employers oriented at the competency development of employees. The idea of lifelong learning is a recognition that adults learn not only at educational institutions but also, if not mainly, in the work environment. In knowledge‐based economy the learning culture in organisations becomes a factor of progress and social development. Therefore, the implementation of activities related to citizens development at all stages of their lives and in various contexts requires a policy which embraces all paths (formal, non‐formal and informal) of competency development and obtaining qualifications in a coherent manner. There are different approaches of studies related to the lifelong learning in the literature. Some authors focus on explaining participation in lifelong learning as such, assuming that it will improve skills and human capital (Arulampalam, Booth, & Bryan, 2004; Biagetti & Scicchitano, 2009; OECD, 1999). Others investigate the impact of lifelong learning on employment probabilities (Jenkins, 2004) or wage levels (Blanden, Buscha, Sturgis, & Urwin, 2010) or the level of workers’ skills compared to requirements on the job (OECD, 2011; Quintini, 2011). In several studies, micro data from individuals is used. (Arulampalam et al., 2004) using micro‐data analysis based on European Community Household Panel (ECHP) analyse determinants of gender access to lifelong

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Lukasz Sienkiewicz, Agnieszka Chłoń‐Domińczak and Katarzyna Trawińska‐Konador learning subject to various employment characteristics such as fixed term contract, work time, employment in public or private sector, educational attainment and wage distribution. Inequality in workers’ lifelong learning across European countries based on the EU‐SILC data is measured by (Biagetti & Scicchitano, 2009). They focus on inequality of workers’ human capital accumulation and complementarity between past education and training in 21 EU countries. They find out that young, better educated and unmarried workers are more likely to receive formal LLL. Additionally, large companies usually train their workers more, while medium sized does not indicate statistically relevant effects. (Bassanini & Brunello, 2010) develop a model focusing at training intensity at sector level based on the Labour Force Survey (LFS) data for 15 European countries and 11 industrial sectors from 1995 to 2002. They estimate association between regulation and training participation and other independent variables. Their findings indicate that regulatory reforms in Europe raised competition that in turn increased investment in workplace training. The sector of employment as explanatory variable is also used by (Huber & Huemer, 2009) Their results indicate that the most important factors affecting LLL participation and intensity are related to the labour market characteristics such as tenure, age, occupation, profession and sector of employment, while household‐related variables (marital status, number and age of children) have smaller impact.

3.2 Determinants of lifelong learning participation in Poland In this section we use the results of the LFS in Poland from 2011 to model the probabilities of participation in formal education or training from perspective of individuals, as EU‐harmonised LFS data is a frequently used source of information on lifelong learning activity among working‐age population in the EU.. Higher incidence of lifelong learning among workers that are better educated, younger and working in occupations or sectors requiring higher skill levels are consistently reported in the literature. Only around 5 per cent of Polish workers participate in LLL, which is much less than EU‐27 average Share of adults participating in education and training did not change over the past decade, while in the EU we could see increases between 2002 and 2006 from above 6 per cent 8 per cent (for men) to from below 8 per cent to around 10 per cent (for women). Thus, we use both LFS and ZZL data from the same calendar year. 3.2.1 LLL from employee perspective Participation in education and training is measured in LFS in the 4 weeks prior to the survey. In our analysis we use the approach and results of multinominal regression analysis presented by Chłoń‐Domińczak & Lis (Chłoń‐ Domińczak & Lis, 2013). They use microdata for the estimation of education and training probabilities. Regressions were estimated for two separate dependent variables: participation in formal education and participation in training courses, which are different patterns of participation of adults in LLL. Thus, separation of these two activities increases the robustness of the analysis and the results. For both dependent variables regressions were estimated for two populations: employed and total population. Independent variables used in both models include: gender, age, education level, sector of employment (based on NACE classification). For the total population the explanatory variables include also labour market status and for the employed population profession (based on ISCO qualification). Profession was not included for the total population logistic regression, due to co‐linearity of no data with sector. In the total population records with missing data on sector were included in the regression (reported as no data in the model in around 20% of all records). For employed population, estimated models include only those records, in which both profession and occupation were reported. For the interpretation of the results we use Average Marginal Effects (AMEs), which allow for quantitative analysis of the impact of the independent variable on the probability of participation in education or training. The reference characteristics (default) were as follows: men, aged 35‐44 with higher education level, working in market services and performing highly qualified jobs. Results of the regression models are presented in Table 2. The results show that young people have (statistically significant) higher probabilities or participating in this form of learning, both in the case of entire population (5 p.p.) as well as working one (around 4 p.p.). Age variables are not statistically significant in the case of participation in training. Educational attainment also affect probability of participation in education and training – the lower educational attainment, the smaller probability of participation both in education and in training, at statistically significant levels both for education and training for the entire population, as well as training participation for workers.

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Lukasz Sienkiewicz, Agnieszka Chłoń‐Domińczak and Katarzyna Trawińska‐Konador Table 2: Results of logistic regression for participation in education or training in Poland in 2011 All in age 25‐64 Education

Workers Training

Education

Training AME(p p‐ p) value

Explanatory variables

AME(pp)

sex: man (reference)

sex: woman

0,01

0,956

‐0,10

0,592

0,26

0,371

‐0,21

0,442

p‐value AME(pp)

p‐value

age: 25‐34

5,05

0,000

0,01

0,966

3,96

0,000

0,15

0,636

age: 35‐44 (reference)

age: 45‐54

‐1,66

0,000

‐0,08

0,763

‐1,36

0,000

‐0,07

0,838

age: 55‐64 labour market status: employed (reference) labour market status: unemployed

‐2,34

0,000

‐0,48

0,099

‐2,04

0,000

‐0,54

0,188

‐0,07

0,870

‐0,62

0,123

labour market status: inactive education level: higher (reference)

0,78

0,083

‐1,53

0,000

education level: secondary education level: lower secondary and below

‐1,57

0,000

‐2,79

0,000

0,20

0,559

‐1,95

0,000

‐3,30

0,000

‐3,21

0,000

‐0,52

0,590

‐2,68

0,000

NACE agriculture

‐1,71

0,000

‐1,39

0,000

‐0,95

0,12

‐1,43

0,00

NACE: industry NACE: market services (reference)

‐0,73

0,005

‐0,54

0,018

0,10

0,793

‐0,53

0,120

NACE: non‐market services

1,33

0,000

0,46

0,070

1,37

0,000

0,35

0,283

NACE no data Occupation: highly qualified (reference)

3,12

0,000

‐0,30

0,615

Occupation: white collar

‐1,37

0,00

‐2,08

0,00

Occupation: blue collar

‐3,17

0,000

‐2,38

0,000

Occupation: simple jobs

‐2,97

0,000

‐2,73

0,000

No of observations

225 640

146 810

pseudo‐R2

0,17

0,11

0,13

0,10

LR (Chi2)

975,79

447,08

513,13

347,04

p‐value

0,00

Source: (Chłoń‐Domińczak & Lis, 2013) Looking at the sector of employment and occupation we find out that in the case of all people in working age, NACE sectors are statistically significant. Those who are (or were) employed in agriculture and industry have lower probabilities of participating in education and in training compared to market services (which also include the knowledge‐intensive ones). Employment in non‐market services increases probability of lifelong learning activity. In the case of sub‐population of workers, we observe similar patterns, but for most cases the results are not statistically significant. The lower significance of sector variables in this regression can be explained by inclusion profession in the model, which is a statistically significant determinant of probability to participate both in education and in training. Compared to the individuals with highly qualified professions, all other categories have lower probabilities of participation in education or training, ranging from 1.4 p.p. to more than 3 p.p., depending on the type of education and profession type. Summing up, profession and sector of operations, together with educational attainment and age (in the case of formal education) affect the probability of acquiring skills and competences through participation in LLL activities.

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Lukasz Sienkiewicz, Agnieszka Chłoń‐Domińczak and Katarzyna Trawińska‐Konador 3.2.2 LLL from employers’ perspective In order to assess probabilities of lifelong learning at employer level we used the results of the ZZL quantitative survey. In the survey we asked companies, whether their employees participate in different forms of formal, non‐formal and informal learning. More than 88 per cent of respondents indicated that their employees participated in at least one form of learning. Use of LLL is more frequent in knowledge intensive services and less frequent in industry and less knowledge intensive services (Error! Reference source not found.), both for all types of learning as well as only formal and non‐formal learning. Companies from knowledge‐intensive services sector engage relatively more frequently in formal and non‐formal learning. The most frequent types of learning include informal ones (gaining knowledge from more experienced workers, learning by themselves)and non‐formal ones (training and courses, continuing education in schools or at higher education institutions) (Error! Reference source not found.).

Figure 1. Structure of companies Figure 2: Per cent of companies that indicated selected forms of LLL indicating different forms of LLL Source: own calculations based on ZZL data We look into determinants of LLL at the company level by estimating a multinomial logistic regression model. We model two sets of regressions explaining: (1) participation of workers in any form of lifelong learning activity, (2) participation in formal and non‐formal education. In the second case, we exclude items, which are related to informal learning: learning from more experiences workers, learning from literature and learning by themselves. Formal and non‐formal learning is indicated by around 80% of respondents. This shows, that around 8 per cent of respondents engage only in informal learning. The independent variables include size of the company (medium or large) and sector of activity, including knowledge‐intensive services (KIS), less knowledge intensive service (LKIS) and industry. Others independent variables used in the model refer to the practices of human resource management at company level, applied to employed workers who can participate in LLL. In our estimation, we use principal component analysis to reduce items in the questionnaire to generate independent variables for the regression. 1 Below we present main results of PCA. , which also allowed to construct variables that refer to unobserved characteristics of companies relating to their preferences in HR development. Competency profiles as a competency management tool. Based on the replies to question on the use of competency profiles, application of PCA reduced all variables into one, which shows propensity of the company to use competence profiles as human resource management tools. The extracted factor explains more than 99% of variance (Figure 3). Reasons of development of human resources in the organisation. In this case the respondents rated the list of items related to the reasons for HR activities (i.e. improvement of employee performance, increasing the motivation of employees, increasing the flexibility of employees and supporting acquisition of new competencies). Again, the PCA reduced the number of variables into one, which can be understood as

Detailed results are included in the annex.

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Lukasz Sienkiewicz, Agnieszka Chłoń‐Domińczak and Katarzyna Trawińska‐Konador incentives to apply human resource management. The factor explains more than 49% of variance (Error! Reference source not found.). Barriers to undertake activities aimed at development of human resources in the organisation. Respondents rated items assessing the importance of selected factors that limit engagement into human resource management activities, including: lack of funds; higher priority of other issues, projects or investments; lack time for developmental activities; employees' unwillingness to learn. The barriers also covered lack of awareness of the management of the importance of such actions and organisational culture adverse to change. Extracted factor explains more than 54% of variance (Figure 5). Selection of employees to competence‐based human resource management. Respondents were asked which employees participate in training most. They could choose from “egalitarian” approach, where everybody is trained and “selective” approach, where training was provided mainly to selected categories of workers, or chose an answer that neither of the groups if covered by competence‐based models. Three components were extracted in the PCA. As we can see from the component matrix, the first component is related to the application of selective model of workers subject to competence‐based approach at company level that is based on the hierarchy of employees. The second component may be referred to as purpose‐driven selective approach to competence‐based management, which is focused on employees involved in key projects for the company or those that have important talents or selected units. The third one is related to the egalitarian approach in competence‐based management, where all workers or no workers are included in such approach. Extracted components cover more than 66% of variance (Figure 6). Approach to competence development of workers. The variables related to the company approachs in competency development included: clearly defined goals and objectives of training, development of individual training plans, supporting individual workers, analysis of the efficiency of training, development of career paths and defining budget for training activities. Two components emerged in PCA: the first one indicates that respondents focused on the broader concept of human resource management. The second component is related to the direct assessment that competence‐based management helps to develop clearly defined goals and objectives of training. Extracted components cover 85% of the variance (Figure 7). Selection of employees to training. Selection to training is an important indication of approach to competence‐based management in organisation. In this case two components were extracted in PCA. The first one can be interpreted as training‐oriented profile of the company, as it is loaded with items related to the choice of workers to training. The second component is loaded with the last item in the questionnaire related to no training in the company, thus we can interpret this variable as a lack of propensity to train workers. Components explain 77% of the variance related to this set of variables (Figure 8). Cost of human resource management. Based on the PCA analysis, the cost items are reduced to one component, which may be interpreted as overall assessment of cost to HR management. The extracted component covers 81% of the variance (Figure 9). For two dependent variables we estimated three regression models. In the first model, we include as explanatory variables size of the company and sector of activity. In the second we use explanatory variables related to the approach to competence‐based human resource management of employees as described above, as are no strong correlations between them.. In the third model we apply both sets of variables together. In the models we focus on the following questions:

To what extent variance of size and sector of the company explains variance in lifelong learning participation of employees?

To what extent company approach to competence‐based human resource management explains variance in lifelong learning participation of employees?

How selected characteristics of the company affect probability of participation of workers in lifelong learning? Is this impact statistically significant?

Regression results for dependent variable related to all forms of lifelong learning are shown in Table 3. The first model, which takes into account size and sector of companies shows that these characteristics alone do 2 not explain variance in participation in lifelong learning, which is indicated by low pseudo R levels. However, we see that probabilities of LLL intensity at company level are higher in large enterprises as well as in those in

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Lukasz Sienkiewicz, Agnieszka Chłoń‐Domińczak and Katarzyna Trawińska‐Konador the sector of knowledge‐intensive services. In the second model we see that companies that have positive approach to competence development of workers have much higher probabilities of LLL intensity at company level (at statistically significant level). No interest in lifelong learning and training of workers reduces probability of workers’ learning. It should be also noted that variance in company preferences in competence management explains more observed variance in LLL participation compared to the first estimated model. In the third model we combine both sets of explanatory variables. In a broad model, size of the company loses its statistical significance. However, the knowledge‐intensive sector still has a statistically significant influence on the dependent variable. This model also confirms that approach to competence development of workers increases the intensity of LLL at company level, while no interest of respondents (HR or company managers) reduces it. Table 3: Multinominal logistic regression results – participation in all forms of lifelong learning (n=941) Model 1

Large enterprise Knowledge intensive services Less knowledge intensive services Medium sized enterprise Manufacturing Competency profiles Development of HR management Barriers to development of HR mgt. Selection – hierarchy Selection – purpose Selection – egalitarian Approach – broad Approach ‐ speficic objectives Selection ‐ training oriented Selection ‐ no interest Costs Cox and Snell Nagelkerke McFadden

Odds Ratio

p‐value

2,66 2,20 0,93 x x

0,02 0,01 0,75 X X

Model 2 Odds p‐value Ratio

0,88 1,23 0,89 1,05 1,15 0,83 17,35 1,49 1,07 0,78 0,89 Pseudo R2 0,02 0,04 0,03

0,18 0,13 0,29 0,73 0,41 0,07 0,00 0,00 0,52 0,01 0,33 0,16 0,31 0,24

Model 3 Odds p‐value Ratio 0,86 0,73 1,99 0,03 1,40 0,21 x x 0,86 1,24 0,90 1,07 1,12 0,83 15,16 1,47 1,07 0,76 0,89

X X 0,11 0,13 0,36 0,65 0,48 0,08 0,00 0,00 0,50 0,00 0,30

0,16 0,32 0,25

* reference category: medium size companies, manufacturing sector. * Statistically significant variables are indicated in bold. Source: authors’ analysis The same three estimations were made for the participation of workers in formal and non‐formal education. There are some interesting differences in the results that require some comment. First, the same independent variables prove to be statistically significant. Additionally, in the case of these forms of learning, contrary to informal learning, generate costs at company level, impact of importance of cost of learning in reducing intensity of LLL participation becomes statistically significant. We can also see that egalitarian approach to competence management is related with reduced intensity of LLL. We also see a change in the value of odds ratio in the case of competence profiles variable, which in the case of participation of workers in only formal and non‐formal education, has a positive impact on increasing probability of workers’ participation in these forms of LLL. Finally, odds ratios related to broad approach to competence development of workers become much smaller. This may indicate that respondents who declared that their workers participate only in informal types of learning more frequently declared that they had objectives of training clearly defined and linked to strategy of the company. In the light of obtained results, such declarations may not be fully in‐line with the observed practices.

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Lukasz Sienkiewicz, Agnieszka Chłoń‐Domińczak and Katarzyna Trawińska‐Konador Table 4: Multinominal logistic regression results – participation in formal and non‐formal education (n=941)

Large enterprise Knowledge intensive services Less knowledge intensive services Medium sized enterprise Manufacturing Competency profiles Dvelopment of HR management Barriers to development of HR mgt. Selection to competence mgt ‐ hierarchy Selection to competence mgt ‐ purpose Selection to competence mgt ‐ egalitarian Approach ‐ broad Approach ‐ speficic objectives Selection to training ‐ training oriented Selection to training ‐ no interest Costs Cox and Snell Nagelkerke McFadden

Model 1 Odds p‐value Ratio 2,90 0,00 2,58 0,00 0,86 0,43 x X x X

Pseudo R2 0,04 0,06 0,04

Model 2 Odds p‐value Ratio 1,07 0,44 1,02 0,85 0,87 0,14 1,07 0,51 1,13 0,30 0,75 0,00 4,62 0,00 1,50 0,00 1,03 0,72 0,67 0,00 0,79 0,02 0,22 0,34 0,24

Model 3 Odds p‐value Ratio 0,66 0,25 2,68 0,00 1,19 0,45 x x 1,04 1,03 0,89 1,09 1,11 0,77 3,77 1,50 1,04 0,65 0,78

x x 0,67 0,78 0,23 0,45 0,38 0,00 0,00 0,00 0,69 0,00 0,02 0,23 0,36 0,26

* reference category: medium size companies, manufacturing sector. * Statistically significant variables are indicated in bold Source: authors’ analysis

4. Summary Our results indicate that both at individual and company level there are many factors that affect lifelong learning activity. Individuals with better educational level, employed in services sector, performing high‐skill professions, including managerial and specialist levels are more likely to participate in education and training. Looking closer at company level we can see that at the company level variance in LLL incidence depends more on the approach of the company than sector as such. In particular, approach of the company to competence development plays an important role. Probability of lifelong learning increases with selective training policy, focusing either on hierarchical or purpose oriented approach to human capital development at company level. We can also see that sectors that are more knowledge intensive more frequently engage their employees in lifelong learning activities.

Annex 1: Results of the PCA analysis Positions

Component 1 All jobs 0,993 executive 0,996 lower and middle 0,996 senior managerial 0,996 in selected organisational divisions 0,996 Involved in key projects 0,996 employees regarded as talented 0,996 no competency profiles 0,994 Extraction Method: Principal Component Analysis a. 1 component extracted.

Reasons

Component 1 development of new competencies 0,659 adaptability 0,689 performance 0,751 desired behaviour 0,737 motivation 0,714 positive attitude 0,681 staying in organisation 0,686 initiative and creativity 0,680 Extraction Method: Principal Component Analysis a. 1 components extracted.

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Lukasz Sienkiewicz, Agnieszka Chłoń‐Domińczak and Katarzyna Trawińska‐Konador Figure 3: PCA analysis – jobs or groups of jobs in Figure 4: PCA analysis – the main reasons for taking organisation for which competency profiles actions related to the development of has been developed – Component Matrixa human resources ‐ Component Matrixa Component 1 no financial means 0,621 not important for part of mgt board 0,764 not important for part of mgt 0,795 organisational culture 0,799 other priorities 0,751 lack of time 0,714 lack of readiness to learn 0,706 Extraction Method: Principal Component Analysis. a. 1 component extracted.

Positions:

Component 1 2 3 All employess ‐0,594 ‐0,135 ‐0,687 executive 0,677 0,143 0,036 lower and middle mgt 0,840 0,080 ‐0,053 senior management 0,779 0,104 ‐0,060 selected departments/units 0,447 0,581 ‐0,047 key project employees 0,120 0,766 0,016 talented employees 0,008 0,810 0,008 No group ‐0,226 ‐0,066 0,910 Extraction Method: Principal Component Analysis. Rotation Method: Varimax with Kaiser Normalization. Rotation converged in 4 iterations.

Figure 5: PCA analysis – barriers to HR management ‐ Figure 6: PCA analysis – selection of employees to Component Matrixa competence‐based human resource management ‐ Rotated Component Matrixa Component 1 2 0,021 0,999

Positions

Component 1 2 all employees 0,734 0,076 Non‐managerial 0,898 ‐0,051 lower and middle management 0,917 ‐0,052 senior managerial 0,858 ‐0,041 selected departments/units 0,842 ‐0,056 none ‐0,026 0,996 Extraction Method: Principal Component Analysis. Rotation Method: Varimax with Kaiser Normalization. a. Rotation converged in 3 iterations.

clear objectives linked to strategy personal career development plan for 0,878 0,075 everybody Supporting own initiatives 0,935 ‐0,007 Focusing on effectiveness of training 0,945 0,019 Defininig career paths 0,923 ‐0,024 Setting maximum training budget 0,861 0,031 Extraction Method: Principal Component Analysis. Rotation Method: Varimax with Kaiser Normalization. a. Rotation converged in 3 iterations.

Figure 7: PCA analysis – approach to competence Figure 8: PCA analysis – selection of employees to training Rotated Component Matrixa development ‐ Rotated Component Matrixa Component 11 Financial cost 0,896 Non‐financial cost 0,905 alternative costs 0,911 Extraction Method: Principal Component Analysis. a. 1 component extracted.

Figure 9: PCA analysis – costs related to human resource management ‐ Component Matrixa

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Following Traces of Collective Intelligence in Social Networks: Case of Lithuania Aelita Skarzauskiene¹, Birute Pitrenaite‐Zileniene² and Edgaras Leichteris³ ¹Faculty of Social Technologies, Mykolas Romeris University, Vilnius, Lithuania ²Institute of Management, Faculty of Politics and Management, Mykolas Romeris University, Vilnius, Lithuania ³Knowledge Economy Forum, Vilnius, Lithuania aelita@mruni.eu birute.pitrenaite@mruni.eu edgaras@zef.lt Abstract: One might argue that social technologies continue to grow in popularity driving economic and societal changes and thus gain potential to influence policies. “In the last few years groups of people, connected by the Internet, collectively creating a very large and high quality intellectual products with almost no centralized control, determined emergence of a new kind of intellectual capital ‐ collective intelligence“ (hereinafter CI) (Goyal, Akhilesh, 2007). Volumes of literature published exhibit the growing interest in the field of CI thereby justifying the relevance of the problem CI’s emergence, development and employment. Despite some efforts (e.g. Luo et al. 2009, Gan et al. 2007, Malone et al. 2010), generally accepted frameworks for studying collective intelligence in human behaviour either does not exist or research is fragmented and lack of complex structure. Furthermore, due to the lack of a common framework, it is not possible to assess what is already known and to tie the efforts of different disciplines together (Salminen, 2012). The variety of mediums where products of CI could be introduced is extensive. Exploring the potential of CI could help communities become more productive and help societies solve their problems more effectively. The paper aims to investigate possibilities and barriers to employ social networks as participatory instruments in terms of introducing CI developed in these networks into public policy. An expansion of forms of public participation is extremely relevant for young democracies like Lithuania where the culture of participation in public policy is still ill developed. Therefore it is very important to stimulate and support the emergence of innovative participatory instruments that could foster public engagement in policy formation. In order to achieve the research goal, applying analytical and case study methods, following activities were undertaken. We analysed the phenomena of CI and its potential, benefits for tackling of societal changes as well as preconditions of co‐creation of value in social networks. Theoretical analysis is followed with examination of the environment of public participation in Lithuanian policy formulation in and overview of social technology based Lithuanian networks (platforms) that are targeting to influence public policy. The preliminary research demonstrates that the number of social projects, funded by public organizations or private persons, is constantly growing in Lithuania. However other researches demonstrate that Lithuanian policy makers are conservative enough in selection of participatory instruments. Thus introduction of intellectual capital in form of CI developed in social networks in public policy remains fragmented in Lithuania and requires the shaping of new framework of participation. Keywords: intellectual capital, social technologies, social network, collective intelligence, community management, public participation, virtual community project

1. Defining collective intelligence “While some researchers argue that learning is essentially an individual activity, most theories of organizational learning stress the importance of collective knowledge or collective intelligence as a source of organizational capability“ (Goyal, Akhilesh, 2007). Collective intelligence differs from individual intelligence because it encompasses a social dimension, groups and organizations develop collective mental models (Senge, 1990) and interpretive schemes which affect group decision‐making and action. In recent years, there has been a surge of research activity into collective intelligence. Massachusetts Institute of Technology, one of the most reputed academic institutions of USA has established a centre called the “MIT Centre for Collective Intelligence” for understanding and taking advantage of the phenomenon of collective intelligence. Some of the most notable outputs of collective intelligence, according to them, include Google, Wikipedia, and InnoCentive. “Study of collective intelligence in humans is a relatively new field, for which huge expectations are set, for example through speculations on the emergence of the Global Brain (Heylighe,1999). The detailed overview on collective intelligence definition is given by J.Salminen (2012). Approaches to studying collective intelligence have been diverse, from the purely theoretical (Szuba, 2002) and conceptual (Luo et al. 2009) to simulations (Bosse et al. 2006), case studies (Gruber 2007), experiments (Woolley et al. 2010) and systems design (Vanderhaeghen and Fettke, 2010). It is the general ability of a group to perform a wide variety of tasks

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Aelita Skarzauskiene, Birute Pitrenaite‐Zileniene and Edgaras Leichteris (Woolley et al. 2010). The phenomenon is closely related to swarm intelligence, which means collective, largely self‐organized behaviour emerging from swarms of social insects (Bonabeau and Meyer 2001). New forms of collective intelligence emerge because of the Internet, web 2.0, 3.0 and social media tools, no wonder that interest in the field is rising (Salminen, 2012). A wide range of different aspects and components of “collective intelligence” which have been studied, at various levels, directly or indirectly, include the following according Goyal, Akhilesh (2007): “social networks of individual and organization, social interaction, familiarity and interpersonal trust“ (Chang and Harrington, 2005; Akgun et al., 2005); “group cohesion“ (Wang et al., 2006); “diversity, strength of relationship, position in the network, group identification“ (Van der Vegt and Bunderson, 2005); “strategic communities, self‐organizing innovation networks, self‐managing teams“ (Rycroft and Kash, 2004); “inter functional linkages, public institution and policy frameworks, characteristics of the entire sociotechnical network of which a firm is part, informal ties and incubators“ (Smilor, 1987; Lumpkin and Ireland, 1988); and “between university and industry“ (Rothschild and Darr, 2005; Kreiner and Schultz, 1993); “shared governance, collaborative leadership or distributed leadership“ (Bradford, Cohen, 1998; Spillane, 2007) “collective intelligence is a form of universal, distributed intelligence, which arises from the collaboration and competition of many individuals“ (Levy, 2010). “collective intelligence could also be defined as a statistical phenomenon of the „wisdom of crowds‟ effect“ (Lorenz et al. 2011). The term „wisdom of crowds‟ was coined by Surowiecki (2005) and it describes a phenomenon where, “under certain conditions, large groups can achieve better results than any single individual in the group. For example, the average of several individuals‟ estimates can be accurate even if individual estimations are not” (Surowiecki 2005). We define collective intelligence as groups of individuals acting collectively in ways that seem intelligent in this paper. The field is also multidisciplinary according Salminen (2012) as it is related to psychology (Woodley and Bell 2011), complexity sciences (Schut, 2010), cognitive studies (Trianni et al. 2011), biology (Bonabeau and Meyer 2001), computer sciences and semantics (Levy 2010) and social media (Shimazu and Koike, 2007). At the moment, there is no theory capable of explaining how collective intelligence actually works (Schut, 2010). It is challenging for researchers from different disciplines to be aware of advancements in other fields, possibly under differently named concepts“ (Salminen, 2012). Scientific observation and analysis of the social impact of technology on development of collective intelligence raises a lot of problems. Following scientific questions could be formulated: how different social projects could become a possibility to effect positive changes in communities and government, how to increase engagement of passive society into decision making process, what technologies would help to structure the information, purify the positions, reconcile different opinions and formulate the real society voice. “The explosion of user‐ generated content referred to as Web 2.0, including blogs, wikis, videoblogs, podcasts, social networking sites, streaming, and other forms of interactive, computer to computer communication sets up a new system of global, horizontal communication networks that, for the first time in history, allow people to communicate with each other without going through the channels set up by the institutions of society for socialized communication”(Barahona et all, 2012). Through our research, theoretical analysis and conversations with academics, we could define these areas for exploring collective intelligence in community management (Lesser at all, 2012):

in generating new ideas for creating value using the experiences and insights of numbers of people around the world,

in disaggregating and distributing work in new and innovative ways,

in making better, more informed decisions about the future,

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in aggregating knowledge, insight and expertise of a diverse group

in targeting and motivating the right participants etc.

2. Development of virtual community projects in Lithuania “As people multiply their abilities to organize themselves through social technologies, there is the possibility to effect positive change in communities and governments. Social technologies could also help communities collaborate in political and non‐political ways, such as voting, organizing disaster aid, decision making for community and government etc.“(Malone,2010). This potential could be especially relevant in societies with relatively short extent of participation of society in public life and in public policy making process in particular. Sad to say Lithuania is one of countries where civic engagement is poor. The researches that are being accomplished by Civil Society Institute since 2007 exhibit low level of the society’s political self‐awareness – in 2012 the civic engagement was rated in average 38,4 of possible 100 points (Civil Society Institute 2013). Since 2007 this rate is increasing very slightly. The positive shift is noticed because recently Lithuanian public more actively defends public and collective interest in governing institutions. However this form of activity remains vague as only 17 per cent of the people that addressed governing institutions were concerned of the public problems. Worth mentioning that civic engagement of young people (form 15 to 29 years old) is distinguished as being significantly low. The social environment for civic engagement in Lithuania is revealed to be enough adverse. It is evaluated only 22,2 of 100 points in 2012 and does not improve during the last several years. 6–7 of 10 individuals have negative opinion on participation environment. That could be one of the reasons why society’s general interest in public issues remains only average (evaluated about 40 points of 100 during the last 3 years). The existing superposition between government and society could be one of the main obstacles to strengthen civil society in Lithuania (Ziliukaite et al. 2006). The research results prove the necessity to search for different tracks that could contribute to stimulation of civic engagement. Civil society interacting with governments is able to improve their effectiveness and responsiveness (Croissant et al. 2000, Merkel and Lauth, 1998). Therefore socially active Lithuanian people are challenged to relieve the rest of society form suffer of the syndrome of impotence (Civil Society Institute 2013a). As progressive means to tackle this issue is employment of social media. “Technology does not determine society it expresses it. But society does not determine technological innovation: it uses it” (Castells, 2000). Information and communication technologies support effective and sustainable development because they create conditions for the emergence of a new form of social organisations based on networking. The Lithuanian strategy on the public sector development emphasizes the extensive amplification of electronic services and wider use of them, but not a simple transfer into electronic environment. Rural Internet Access Points (RIAPs) are one of the most important sources of access to the global information society in Lithuania. Owing to RAIN I and RAIN II projects carried out by absorbing EU structural support funds, the fast and high‐quality internet became accessible not only in cities but also to rural areas public sector, business organizations and residents. It is planned that by the end of 2013 broadband internet will reach 98.7 percent of rural areas. There is no doubt that the widespread and availability of the internet in Lithuania is one of the prerequisites for the formation of networked societies. Such outbreak of social technologies conditioned possibilities to interconnect the public for social projects. The number of socially oriented network platforms, funded by public organizations or private entities, is constantly growing in Lithuania. Among them are such projects as manobalsas.lt (My Voice Lt, www.manobalsas.lt), Transparency International administrated project manoseimas.lt (My Parliament, www.manoseimas.lt), eVoting testing system ivote.lt (www.ivote.lt), Lithuanian civic initiative think tank Aš Lietuvai.lt (I for Lithuania, www.aslietuvai.org) and the platform of e‐democracy Lietuva2.0.lt (Lithuania 2.0, www.lietuva2.lt). According to the project developers My Voice LT is a rational voting system on the internet that uses questionnaire on public issues. People are invited to make a short test to find out which politicians and political parties are closest to their political views. In questionnaire is given questions based on public interest issues from a variety of areas ‐ education, health, economy, foreign policy and culture. It is believed that people, knowing politicians position toward issues that concern them, can make a rational decision what politician will represent their interests the best. At the same time the project contributes to the strengthening

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Aelita Skarzauskiene, Birute Pitrenaite‐Zileniene and Edgaras Leichteris of democracy in the country, civil society development, populism reduction, encourages people to vote responsibly and activates interest in politicians’ attitudes and political parties programs. Another project implemented by Transparency International Lithuanian Division and a group of active citizens of Lithuania My Parliament LT is dedicated for those, who care about the work of MPs and parties what are their positions on important state issues. Test basis ‐ 10 themes, which has been voted at the Parliament during the last 2008‐2012 years term. Both My Voice LT and My Parliament LT apply the same questionnaire. However there is significant distinction between them. The results of peoples’ voting in My Voice LT are compared with those of the candidates for MPs whereas My Parliament LT voting is based on standpoints of actual MPs. Thus even My Parliament LT displays actual positions of MPs, My Voice LT allows to reveal voices not only of the parliamentary parties but also of the others. The project iVote.lt is aiming to introduce citizens to new internet voting method and to allow them experience the method themselves. iVote.lt game model is based on online voting mode used in Estonia and adopted with attitude towards specifics of Lithuania. When designing voting game global online voting practice was studied and consultations with experts in fields of law, information technologies and elections were conducted. Based on groups in Google and Facebook new virtual community Aš Lietuvai.lt (I for Lithuania, www.aslietuvai.org) was created. This project strives to find original ways to tackle national problems and it is organized in the way that people propose ideas and solving of problems, participate in leading these ideas to practical application. At the moment this community is implementing public Senate idea. Another notable idea in process is creation of positive internet TV (equivalent to www.tvrain.ru). Many other ideas will be studied in the next chapter. In January of 2011, virtual community project Lietuva 2.0.lt (Lithuania 2.0, www.lietuva2.lt) was launched. It is identified as a social network of e‐democracy, a platform for socially active individuals aiming to contribute to the development of Lithuania. Lithuania 2.0.lt provides means for society to get involved in public life of the country by presenting ideas, voting, discussing and compromising proposals for Lithuanian legislation. We performed initial analysis of features of the Virtual community projects. They are presented above within Lesser’s et al. (2012) areas for exploring CI in community management (Table 1). This analysis allowed us to define which projects serve as the best platforms for the development of CI. Table 1: Analysis of Lithuanian virtual community projects as platforms for CI Virtual community project Areas for CI Generation of new ideas for value creation Innovative distribution of work Contribution to decisions about the future Aggregation of knowledge Targeting and motivating participants

My Voice LT

My Parliament LT

iVote.lt

I for Lithuania

Lithuania 2.0

Yes

The rough analysis demonstrates that some of the Virtual community projects are more sophisticated as platforms for CI than the others. My Voice LT, My Parliament LT and iVote.lt contribute to recognition of public perceptions on social problems, foster civic engagement and educate people about Lithuanian political life. However these projects are lacking such important attributes as possibilities to concentrate new ideas, attract and share knowledge, and distribute work in new and innovative ways. Meanwhile I for Lithuania and Lithuania 2.0 contain all the features for the development of CI. Therefore these virtual community projects are selected for more detailed analysis.

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3. Tracing collective intelligence in virtual community projects I for Lithuania and Lithuania 2.0 We analysed I for Lithuania and Lithuania 2.0 according aspects and components of CI which were listed in academic literature (see the chapter “Defining Collective intelligence”). In total we identified 11 components according which qualitative analyses of the virtual community projects were accomplished (see Table 2). Table 2: The components of collective intelligence in I for Lithuania and Lithuania 2.0 Component of CI Social network of individuals and organizations

I for Lithuania Any individual or organization can join the project. Currently more than 10 NGOs and other public institutions are connected to the project. Acceptance of the unique code of ethics is required.

Lithuania 2.0 Any individual or organization can join the project. Currently more than 10 NGOs are connected to the project. Acceptance of the rules of privacy and directions for use is required.

This virtual community identifies itself and sets the mission – to collect wisdom of crowds for tackling ultimate social issues in Lithuania.

Identifies itself as a network that strives to find solutions for social problems in Lithuania.

Policy frameworks

I for Lithuania strive to influence policies via collecting, analyzing and implementing ideas. Ideas are allocated to several levels: global value level, national (state) value level, organization or community value level, and individual value level.

Lithuania 2.0 strives to influence policies via collecting, analyzing and implementing ideas.

Sociotechnical network

I for Lithuania – virtual community project consisting of people that communicate using social technologies. For participation in the project hardware, software and internet connection are required. Google sites, Facebook, Google docs, Twitter etc. are employed to facilitate the project activities.

Lithuania 2.0 – virtual community project consisting of people that communicate using social technologies. For participation in the project hardware, software and internet connection are required.

Self‐organizing innovation network

Open innovations are the essence of I for Lithuania. Up to date about 5000 ideas including innovative ones were proposed, voted and discussed. Working groups focusing on specific ideas are being composed of these ideas joining people.

Innovative ideas are expected to arise within conceptions proposed for discussions at Lithuania 2.0. However the main focus is not on innovation but on relevance to Lithuanian society. People are free to join any conception that is developed within the network.

Social interaction, familiarity and interpersonal trust

People interact while discussing issues, voting and commenting on ideas. The registered Facebook users participate in the network. People recognize each other via profiles. Trust is built on believe that users follow the code of ethics that is accepted during the enrolment to the network.

People interact while discussing issues, voting and commenting on ideas. The registered users participate in the network. Applicants are asked to motivate the striving to participate in Lithuania 2.0 and describe their competences. Users can remain anonymous, but the network leaders publish their CV. Trust is built on believe that users follow the manifest, users’ requirements and privacy guidelines that are accepted during the enrolment to the network.

Group cohesion, strength of relationship

Virtual community project gains attributes of civic movement. A number of users connect to some idea and work for it’s development.

Lithuania 2.0 unifies socially active people for common goals. A number of users connect to some idea and work for it’s development.

Strategic community

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Aelita Skarzauskiene, Birute Pitrenaite‐Zileniene and Edgaras Leichteris Component of CI

I for Lithuania

Lithuania 2.0 Devotion to the ideas is expected and it is set in the manifest.

Diversity

Vast of micro projects. I.e. some of the ideas that are being developed by I for Lithuania include: strategies for Lithuania; crisis mapping; 9 Lithuanian principles; matters of survival; untouchable priority; the Solidarity Charter; success factor; Lithuanian equation; reverse creation; successful nation; open television etc.

Various ideas, diverse voting. I.e. some of the current problems that are being solved by Lithuania 2.0 are: alcoholism reduction; waste management; improvement of election system; contract on candidate’s political responsibility; implementation of national e‐ learning system for schools etc.

Self‐managing teams, collaborative leadership or distributed leadership, and shared governance

People join into the group elaborating some specific idea in informal, non‐hierarchical manner. The moderator is selected to lead the group. However during the idea development process leaders could change.

The platform is filled up with contents by registered Lithuania 2.0 users. Those are considered to be both managers and leaders as well. The more active some user is the more rights in the network he gains.

Inter functional linkages

Collaboration and competition of many individuals

One of the basic projects of I for Lithuania is creation and employment of public Senate. House of Lords and House of Commons are established for laws making. Several actions are linked until ideas become an Act: work in groups on some idea, preparation of documents, formal presentations, readings and debates, consideration in committees, reporting, and assent. Are counted several hundred of I for Lithuania participants residing in different countries. They compete when present ideas and collaborate when elaborate alternatives for problem solving.

Processes of presentation of ideas (or conceptions), explanation of problems, introduction of solutions, discussions, evaluation and voting for or against ideas and solutions are interlinked. Currently Lithuania 2.0 is joined by about a hundred participants competing in introduction of ideas and collaborating in searching the ways to tackle social problems.

The preceding cases demonstrate the growth of CI by linking socially active people through social media. Both I for Lithuania and Lithuania 2.0 contain all the most important features for CI building such as self organization, shared management, innovations, social interaction, collaboration etc. Furthermore, if we screened these virtual communities from the point of genome of CI (Malone, Laubacher, and Dellarocas, 2009), we could state that:

both networks have set a very clear missions and goals, that answers the question “What is being done?” is explicitly answered;

neither I for Lithuania nor Lithuania 2.0 limit who can participate in activities (“Who is doing it?”). As the general public is invited, there is possibility to engage people with diverse knowledge and skills;

contributors take part in the activities because of the opportunity to socialize, they can feel motivation to contribute to large goals, people can also be inspired by possibility to be appreciated (“Why are they doing it?”);

participants know the way CI will be used (“How it is being done?”). Both networks strive to reach some positive result in social problem solving. To find possible solutions different group decision making methods are applied such as voting, contest, averaging, and consensus or team decides on solution. Thus contributors know that their efforts will not be lost.

As I for Lithuania is compared to Lithuania 2.0 it is obvious that the first one is much more complex in its contents as well as in its extent in terms of the number of participants. On one hand, expansive characteristics of I for Lithuania exhibit its popularity, recognition and believe that this virtual community could stimulate positive social changes. On the other hand, such complexity aggravates operation of the network, requires from new participants lots of time and efforts to understand the processes within the network, a number of promising ideas could be lost in vast of information. Whereas Lithuania 2.0 is simpler, easier to understand and find information, follow ideas. However Lithuania 2.0 is very new platform and it holds potential of expansion.

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Aelita Skarzauskiene, Birute Pitrenaite‐Zileniene and Edgaras Leichteris This research could be valued as an introductory phase into subject because it reveals the facts of growth of CI in social networks. However there is no clarity yet how institutions of government could use collective intelligence to solve public problems. Researchers’ on stakeholders’ involvement in policy making in Lithuania demonstrate failings in application even of the “classical” participatory instruments like formal participatory decision and/or problem solving groups, committees and commissions etc. (i.e. Pitrenaite‐Zileniene and Mikulskiene 2012, Mikulskiene and Pitrenaite 2012). Both Lithuanian policy makers and participatory policy processes hardly are ready to employ ideas developed in social networks. Therefore the question of introduction of CI results to public policy making remains to be answered by further research.

4. Conclusions In this paper, we define collective intelligence as groups of individuals acting collectively in ways that seem intelligent. The field is multidisciplinary and there is challenge for researchers from different disciplines to understand how collective intelligence actually works. New forms of collective intelligence emerge because of the Internet, web 2.0, 3.0 and social media tools. Scientific observation and analysis of the social impact of technology on development of collective intelligence raises a lot of problems. The variety of mediums where products of CI could be introduced is extensive. Exploring the potential of CI could help communities become more productive and help societies solve their problems more effectively. Lithuanian society suffers lack of civic engagement. But the fact that country recently has burst with internet accessibility and application of electronic services, wide opportunities to foster public involvement through social media emerged. Socially active communities recognizing these opportunities have launched diverse virtual community projects that stimulate the society’s civic and political self‐awareness. The variety of virtual community projects testify the growing involvement of society members into public life and at the same time the rising assumptions and possibilities for the development of CI. For exploration of collective intelligence phenomenon in Lithuania such platforms as I for Lithuania and Lithuania 2.0 could be explored because they are built from the main components of creative CI network. The composition of I for Lithuania and Lithuania 2.0 clearly demonstrates to all the participants What, Who, Why and How is operating in these virtual communities. Therefore people know the goals, are welcomed to act, recognize possible benefits, and understand rules of contribution. Such medium is favourable for the growth of CI. However, even I for Lithuania and Lithuania 2.0 produces lots of intellectual output, there is no framework that could help to convert this output to actual policies. Following scientific questions could be formulated: how different social projects could become a possibility to effect positive changes in communities and government, how to increase engagement of passive society into decision making process, what technologies would help to structure the information, purify the positions, reconcile different opinions and formulate the real society voice.

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Relational Capital and Social Capital: One or two Fields of Research? Kaisa Still¹, Jukka Huhtamäki² and Martha Russell³ ¹VTT Technical Research Centre of Finland, Kaitoväylä 1, 90571 Oulu, Finland ²TUT, Intelligent Information Systems Laboratory, Korkeakoulunkatu 3, Tampere, Finland ³Human Sciences Technology Advanced Research Institute, Stanford University, Cordura Hall, USA kaisa.still@vtt.fi jukka.huhtamaki@tut.fi martha.russell@stanford.edu Abstract: In this paper, we start from relational capital, which is one of the components of intellectual capital addressing the intangible values of organizations. In popular usage, the concept seems to be closely related to social capital, with similar words (such as relationships and network) explaining it, and with claims that scholars use social capital instead of relational capital despite their differing origins. As we observe these terms to be used interchangeably in business management literature, we will elaborate on the question: should relational capital and social capital be seen as one or two fields of study? We proceed to use bibliographic data from Scopus with the method of social network analysis in finding and comparing the authorities for relational capital and social capital. We define authority with Kleinberg’s HITS algorithm, hence linking authority to citations (the number of citations as well as who does the citation), as citations are generally seen to indicate recognition and merit in the world of scientific writing. We then compare the resulting lists of top 20 authorities in the two fields as well as provide insights with network visualizations. Our findings reveal only 4 names on both lists (Hitt, Nahapiet, Ghoshal and Kogut), suggesting that the fields are separate but related, which is made explicit with the network visualizations that show these citation‐based linkages between the two fields. The visualized networks suggest further that relational capital literature is using social capital literature in its citations. Overall, these findings reveal the linkages between the concepts of relational capital and social capital in scientific literature as well as provide means for showing the roles of specific actors, in this case certain core authors. Hence, the findings provide a shared understanding for scholars and practitioners interested in these concepts and can provide support for future studies in these areas. Keywords: relational capital, social capital, social network analysis, bibliographical analysis, scientometrics, visual analytics

1. Introduction Business management has traditionally been concerned with value of the organization, valuing processes and the performance of the organization. In the late 1990s an approach of intellectual capital was born to address the exploring, understanding and measuring the intangible nature of innovation and value creation (Sveiby 1997, Edvinsson and Malone 1997). It is oftentimes seen as one entity, though it also seen to include components of structural capital, human capital and relational capital. Relational capital is explained as value that is created and maintained by having, nurturing and managing good relationships. Relational capital is framed as the totality of relations between a firm and its main stakeholders and is operationalized through image, customer loyalty, customer satisfaction, link with suppliers, commercial power, negotiating capacity with financial entities, environmental activities, etc. (Bronzetti et al. 2011). Relational capital has many definitions. It is generally explained with words such as alliances, exchange, resource, social network processes, relationships, relations, customers, suppliers, employees, and co‐operation, internal, and external in the arena of management studies ( see Table 1). The concept of social capital with roots in sociology has general agreement on the notion that social interactions yield benefits to actors (Brunie 2009) —but lacks a consensual and established definition (Grootaert and Van Bastelaer 2002), though many definitions do exist (see Table 2). It is one of the major research streams in organizational network scholarship, concerning the concept of “value of connections” (Borgatti and Foster 2003). It is seen as a useful concept in bringing attention to the role of social interactions in explaining individual and collective outcomes (Brunie 2009) and has been used for economic analysis by, for example, OECD (2002) and the World Bank (2007). As can be seen, these explanations also use words such as resources, network, relationships, and connections. Furthermore, social capital has been explained with components such as external and internal dimensions (referring to whether it is analysed within an organization or between on organization; in Partanen et al. 2008) as well as with relational, structural and generalized approaches (Brunie 2009). For example, a concept of “relational social capital” is seen to refer to

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Kaisa Still, Jukka Huhtamäki and Martha Russell the interpersonal connections that have been developed between actors through a history of social interactions such as trust and respect (Granovetter 1992; Nahapiet and Ghoshal 1998). Table 1: Explaining relational capital Definition Relational capital in alliances refers to a relational rent generated in an exchange relationship that cannot be generated by either firm in isolation. It has been identified as a resource that is created through social network processes. The value of firm’s network of relationships with its customers, suppliers, alliance partners and employees. Relational capital refers to “A stock of relations that a firm can entertain with other firms, institutions, research centers, measured through the intensity of cooperation among local actors”. Relational capital extends the definition of customer capital: it is a broader term that encompasses not only the value of customer relationships but also the value of relationships with shareholders, governments, suppliers, competitors, research institutes, industry associations or other external networks linked into the organizational value chain. Relational capital is defined as an intangible asset that is based on developing, maintaining and nurturing high‐quality relationships with any organization, individual and group that influences or impacts your business including: customers, suppliers, employees, governments, partners, other stakeholders, and even competitors. Relational capital is defined as the set of all relationships – market relationships, power relationships and cooperation – established between firms, institutions and people. The dimension of relational capital comprises of two sides: internal, relations among employees, and external, relations with stakeholders.

Source Dyer and Singh 1998; Wathne and Heide 2004 Gulati, Huffman and Neilson 2002 Cabello 2002

Ordonez de Pablos 2003

Adecco 2007

Welbourne and Pardo‐del‐Val 2008 Duparc 2012

Table 2: Explaining social capital Definition Social capital is the sum of resources embedded within, available through, and derived from the network of relationships by an individual or a social unit. Social capital is defined as the norms and social relations embedded in the social structures of societies that enable people to co‐ordinate action to achieve desired goals. Social capital refers to the goodwill and resources a firm amasses because of its connections and relationships with others. Social capital refers to the institutions, relationships, and norms that shape the quality and quantity of a society's social interactions … social capital is not just the sum of the institutions which underpin a society – it is the glue that holds them together. Theories rooted in the concept of social capital focus on the significance of relationships as resources for social action. Their central proposition is that social networks (i.e. personal relationships) often develop over time, provide the basis for trust and cooperation, and constitute a valuable actual or potential that aids the conduct of social affairs and improves the economic performance of firms. Social capital plays a major role in building mutually beneficial relationships between companies, enhancing value creation. Social capital is generally understood to exist in social and interpersonal networks, bridging and bonding individual actors with societies.

Source Nahapiet and Ghoshal 1998 OECD Glossary of Statistical Terms (term added in 2002) Arregle et al. 2007 World Bank 2007

Weber and Weber 2007

Zahra 2010 Kohtamäki et al. 2013

Scholars have claimed that “we are using social capital instead of relational capital“ (Nahapiet and Ghoshal 1998). In some cases, relational capital has been defined as a” form of social capital embedded in business relationships” (Kohtamäki et al. 2013). As the terms relational capital and social capital have been used in association with each other or interchangeably, this research elaborates on the question: should relational capital and social capital be seen as one or two fields of study within the context of business?

2. Research methodology We found inspiration in a related study that addressed whether innovation and entrepreneurship are one or two fields of study, achieving this through comparisons using traditional bibliographic methods (Garfield 1972) of core scholars and core works in each field: simply, if the scholar and key works are the same, then the fields are the same (Landström et al. 2012). We wanted to achieve similar comparisons with exploring the fields of relational capital and social capital, and applied social network analysis (SNA) towards this.

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Kaisa Still, Jukka Huhtamäki and Martha Russell Scientific collaboration and endorsement are well‐established research topics often approached with scientometrics using bibliographic databases to provide greater insights into the working of science (Braun et al. 2010). Nowadays, mainly three kinds of methods are utilized: survey/questionnaire, bibliometrics, and complex network analysis (Ding 2011). A major source of data for those methods is the citation data, which documents citing behaviours via scholarly publications. It is accepted that a cited paper refers to a point relevant to the subject at hand, though it is also seen that citation has social uses, e.g., self‐serving or for persuasion (Greenberg 2009). Citations are seen as tokens of recognition in the world of scientific writing and the number of times a paper is cited can be used as a rough‐and‐ready indicator of its merit (Peritz 1992). Social network analysis studies the network structure of social actors (Wellman and Berkowitz 1988). Complex network methods have been used explore macro‐level network features of co‐authorship and citation networks as well as for individual author rankings within different domains (Ding 2011): a citation network documents the citing behaviour with nodes of authors and links representing the citing of one author by another, which creates directed networks. A citation network has also been defined as a kind of information network that represents the network of relatedness of subject manner (Newman 2010). In this paper, we use SNA metrics (1) to identify core scholars of relational capital and social capital, and (2) to describe the relational capital and social capital literature and their relationships. We will concentrate on the citation networks in our exploratory sample, as citations continue to be viewed as legitimate objectives of research (Peritz 1992). The above mentioned indicator of a number of times an author has been cited corresponding to the indegree of an author (Wasserman and Faust 1994 is complemented with metrics of outdegree corresponding to the number of authors a particular author has cited. In addition, we use HITS algorithm that produces two additional metrics to authors, namely authority and hub (Kleinberg 1999). In addition to indegree, authority value is based on emphasizes the networks manner of citations, taking also into consideration “who cites”. Hub value indicates the authority of the cited authors.

2.1 Context of research Applying the research analytics process (utilized also for studying scientific collaboration by Liu et al. 2012), we used a bibliographic database called Scopus as our data source. Scopus, Web of Science and Google Scholar are the key bibliographic databases, each with their strengths and weaknesses, but Scopus is considered to be easy to use as well as to offer more coverage (Falagas 2007). We searched for terms “relational capital” and “social capital”. Limiting our search to physical sciences, social sciences and humanities, and to the subject areas of “business management and accounting”, “economics, econometrics and finance” and “decision sciences”, the results of our search returned instances in which the term appearing either in article title, abstract, or keywords produced 199 articles on relational capital and 2758 articles on social capital. Hence, we could see that the social capital literature is volumetrically more than 10 times the size of the relational capital literature. Using Scopus analytics, we saw that most of the relational capital, as well as social capital literature, addressed the subject area of “business management and accounting”, but much of it was also categorized under social sciences (Table 4). Table 4: Subject areas of relational capital and social capital Subject Area (Domain)

Relational Capital Social Capital

Business Management and Accounting Economics, econometrics, and Finance Decision Sciences Social Sciences

168 40 53 38

1748 1336 245 859

At this point, we also looked at the lists of authors with the most articles (Table 5). When looking at the top 20 authors, only one name, Hitt, M.A., can be found on both lists, indicating that the fields are separate in authorship.

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Kaisa Still, Jukka Huhtamäki and Martha Russell Table 5: Top 20 authors of relational capital and social capital based on the number of articles Relational Capital Social Capital 1 Hitt, M.A. (5) Yamamura, E. (16) 2 Lawson, B. (4) Lindstrom, M. (13) 3 Cousins, P.D. (4) Kawachi, I. (9) 4 Handfield, R.B. (4) Prashantham, S. (9) 5 Gonzales‐Loureiro, M. (3) Brunetto, Y. (8) 6 Delgado‐Verde, M. (3) Novicevic, M. M. (7) 7 Navas‐Lopez, J.E. (3) Hitt, M.A. (7) 8 de Castro, G.M. (3) Honig, B. (7) 9 Cavusgil, S.T. (2) Torgler, B. (7) 10 Cegarra‐Navarro, J.G. (2) Harvey, M.G. (7) 11 Cruz‐Gonzales, J. (2) Robison, L. J. (6) 12 Flavian, C. (2) Farr‐Wharton, R. (6) 13 Kohtamaki, M. (2) Molina‐Morales, F.X. (6) 14 Chang, K.H. (2) Autio, E. (6) 15 Jansen, J. (2) Chrisman, J. (5) 16 Chen, C.J. (2) Cobello‐Medina, C. (5) 17 Gotcher, D.F. (2) Jones, O. (5) 18 Hamzah, N. (2) Brown, T. T. (5) 19 Falcone, R. (2) Cannella, A. A. (5) 20 Chen, Y.S. (2) Cowan, R. (5)

2.2 SNA process Due to the high volume of the initial sample, we then performed proportional sampling and limited the number of social capital articles to the 2,000 most cited articles. Using the same ratio found in the population of articles, the number of relational capital articles to be analysed became 144, and the top cited were selected. Author names were extracted from the literature data with tailored code. Data refinement processes applied in bibliographical analysis as well as in data journalism (Gray, Bounegru and Chambers 2012) were used to clean the data, in particular by matching author names with varying spelling (so that for example Granovetter M.S. is the same person as Granovetter M.). As we understand the error‐prone nature of the process, major effort was exerted to verify accurate refinement of the data. For data analysis, a tailored batch script written in Python was developed to construct the networks with the help of NetworkX, a software package for network analysis. Gephi, an interactive network visualization and exploration platform available in open source (Bastian, Heymann, and Jacomy 2009), was used for visualizing the networks and for computing the metrics.

3. Findings Using the sampling methods, processes and SNA metrics described above, authority was calculated for the nodes of the two citation networks with directed links pointing from authors of the articles to the authors of the references, producing lists of authors with most authority (see Table 6 and Table 7). Author order is the same for both authority and indegree in the two tables. Outdegree value zero indicates that an author is only included through citation. Whereas Singh, H., as an example, has only cited half as many authors as Bontis, N., he has the highest hub value, showing that he often has cited authorities. Table 6: Top 20 relational capital authors and their SNA metrics Label Barney J. Bontis N. Ghoshal S. Edvinsson L. Nahapiet J. Singh H. Stewart T. Grant R. Malone M.

Authority Hub Indegree Outdegree 0.0027 0.0 116 0 0.0025 0.0343 107 213 0.0025 0.0 107 0 0.0024 0.0322 105 16 0.0023 0.0 99 0 0.0021 0.0380 92 103 0.0021 0.0 90 0 0.0020 0.0 88 0 0.0020 0.0 88 0

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Degree 116 320 107 121 99 195 90 88 88

Kaisa Still, Jukka Huhtamäki and Martha Russell Label Authority Hub Indegree Outdegree Sveiby K. 0.0019 0.0 84 0 Kogut B. 0.0019 0.0 82 0 Roos G. 0.0017 0.0211 74 35 Fornell C. 0.0017 0.0 74 0 Dyer J. 0.0017 0.0 73 0 Teece D. 0.0017 0.0 72 0 Hitt M. 0.0017 0.0280 72 350 Tsai W. 0.0016 0.0 70 0 Kale P. 0.0016 0.0285 68 103 Gulati R. 0.0016 0.0 68 0 Podsakoff P. 0.0015 0.0 66 0

Degree 84 82 109 74 73 72 422 70 171 68 66

Table 7: Top 20 social capital authors and their SNA metrics Label Authority Hub Indegree Outdegree Degree Putnam R. 0.0040 0.0204 1567 12 1579 Coleman J. 0.0038 0.0 1516 0 1516 Granovetter M. 0.0027 0.0 1070 0 1070 Ghoshal S. 0.0025 0.0128 980 251 1231 Burt R. 0.0024 0.0131 964 210 1174 Nahapiet J. 0.0023 0.0118 907 172 1079 Bourdieu P. 0.0021 0.0 813 0 813 Adler P. 0.0019 0.0099 750 187 937 Kwon S. 0.0018 0.0095 723 313 1036 Portes A. 0.0018 0.0 714 0 714 Fukuyama F. 0.0015 0.0 589 0 589 Lin N. 0.0014 0.0 556 0 556 Tsai W. 0.0014 0.0074 555 242 797 Woolcock M. 0.0013 0.0067 506 101 607 Uzzi B. 0.0012 0.0061 459 69 528 Kogut B. 0.0011 0.0057 416 82 498 Becker G. 0.0010 0.0 393 0 393 Powell W. 0.0010 0.0 379 0 379 Hitt M. 0.0010 0.0052 377 586 963 Knack S. 0.0009 0.0048 370 61 431

Comparing these two lists, Table 6 and Table 7, to the lists of authors with most articles reveals that very few authors have both written a large number of papers and are highly cited: in fact, only Hitt, M.A. can be found on both, interestingly, on all four lists. When comparing the top 20 authors based on SNA authority, we can see that in addition to Hitt, M.A. only three other authors are highly cited in both relational capital and social capital literatures: Nahapiet, J., Ghoshal, S. and Kogut, B. Hence, most of the top 20 authors (80 per cent) are separate based on SNA authority in relational capital and social capital literature. This would strongly suggest that the fields are separate, though the connections do exist in forms of citations. Network visualizations of citation networks show the connections between relational capital and social capital, with authors being nodes (author name size indicates the authority) and citations (lines) linking them. The following color‐coding is used: authors within social capital are red nodes, authors within relational capital green, and authors within both are yellow. The lines between the nodes follow the same logic: citations within social capital are red lines, within relational capital green, and between these two yellow. All three resulting networks (see Figures 1, 2 and 3) include a large number of nodes and links: The relational capital network is composed of 7,179 nodes with 36,584 links between them. The social capital network includes 50,633 nodes and 346,320 connections. The 53,008 nodes in the aggregated network are connected through a total of 377,488 edges. Through the networks, the connections between the concepts are made visible and the authors’ relationships are revealed. The visualizations can be seen to provide macro‐level presentations that bring transparency to not only to the connections, but also in describing the sizes of the literature, as well as the connections within and between those fields.

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Figure 1: Citation network of relational capital literature

Figure 2: Citation network of social capital literature

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Figure 3: Aggregated citation network of social capital and relational capital literature In the presented citation network visualizations, the green area (corresponding to citations from relational capital to relational capital literature) is much smaller than the red area (corresponding to citations from social capital to social capital). In addition, the green area in Figure 1 is largely on top of the yellow area (corresponding to citations from social capital authors to relational capital authors or vice versa.) This indicates that relational capital literature and social capital literature tend to cite the same sources, showing that the two fields are related. As the red area seems to dominate the yellow area in Figure 1, social capital literature draws heavily on its own literature for citations.

4. Conclusions Using citation networks to study scientific collaboration has become increasingly important to better understand scientific collaboration and enhance scholarly communication (Ding 2011), as well as to facilitate development of a subject area (Molka‐Danielsen et al. 2007). In this paper, we approached the concepts of relational capital and social capital to determine whether those are one or two fields of research. The process and result of answering the research question document the distinction between relational capital and social capital in: (1) definition, and (2) citation structure and (3) visualizing social structure. In summary, these findings reveal linkages between the concepts of relational capital and social capital in scientific literature; they provide means for showing the roles of specific actors, in this case certain core authors. Hence, the findings provide a shared understanding for scholars and practitioners interested in relational capital and social capital and can support future studies in these areas. The authors believe that further refining the semantics of relational capital and social capital is important for the study of the intangible values of organizations, open innovation, and intellectual capital.

4.1 Understanding definition of academic domains with social network analysis Using social network analysis and the metric of authority on publication and citation data from the subject areas of business management and accounting, economics, econometrics and finance, and decision sciences, we concluded that relational capital and social capital are different fields of study. Their authoritative authors are largely different; only 4 authors (corresponding to 20 percent of the top 20 authors) were shared between the two fields.

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4.2 Understanding citation structure with social network analysis The visualizations of the citations in the respective fields showed that there are significant connections between the fields in terms of citations; relational capital literature draws heavily on social capital literature in its citations. This clarification can provide a shared understanding for scholars and practitioners alike.

4.3 Visualizing the social structure The findings highlight the networked nature of scholarly works and the inherently social nature of academic science (Price 1970, 1976). They also show the preferential attachment process (sometimes referred to as the Matthew effect or simply “rich get richer”) in academic networks, driving more connections to network nodes that already have more connections. This occurs as authors search new knowledge, including citations, from the existing literature. The more a particular piece of literature is cited, the more likely it is that an author starts using a particular piece of literature.

4.4 Limitations of the study and future possibilities Our data was chosen through searching the exact terms, “social capital” and “relational capital”; this may have excluded some relevant literature and therefore posed a limitation for our results. Overall, a data‐driven approach is limited not only by the quantity and quality of the available data, but also by the chosen boundary specifications, metrics and analysis methods as well as visual presentation methods (Card et al. 1999) All these have an impact on the findings and their context for generalization. Explorations of related concepts using different boundary specifications, for example, could further contribute to the understanding of relational capital and social capital as constructs of intellectual capital. Co‐authorship networks within the context of this study were fragmented and vulnerable to data differences. Because of this, the representative value of this research in the context of relational capital and social capital literature should be considered exploratory. We acknowledge the importance of co‐authorships networks in documenting scientific collaboration, for example together with the metric of betweenness. At the same time, we recognize that co‐authorships and citations indicate semi‐terminal events in collaboration; reliance on these as indicators may overlook important relational and social processes. Correspondence, meetings and social activities are known to influence scientific collaborations (Edelstein, 2009.)

References Adecco (2007) The intrinsic link between human and relational capital: a key differentiator for today’s leading knowledge economy companies, http://www.eepulse.com/documents/pdfs/adecco_wbf_exec_summary_bro_final_1.pdf (accessed May 10, 2013) Arregle, J‐L., Hitt, M.A., Sirmon, D.G. and Very, P. (2007) “The Development of Organizational Social Capital: Attributes of Family Firms”, Journal of Management Studies, Vol. 44, No. 1), pp 73‐95 Bastian, M., Heymann, S. and Jacomy, M. (2009) “Gephi: An Open Source Software for Exploring and Manipulating Networks”, Third International AAA1 Conference on Weblogs and Social Media. Borgatti, S.P. and Foster, P.C. (2003) “The Network Paradigm in Organizational Research: Review and Typology”, Journal of Management, Vol. 29, No. 6, pp 991‐1013 Braun, T., Glänzer, W. and Schubert, A (2010) “The footmarks of Eugene Garfield in the jornal Scientometrics”, Annals of Library and Information Studies, Vol. 57, pp 177‐183 Bronzetti, G., Mazzolta, R., Puntillo, P., Silvestri, A. and Veltri, S. (2011) Intellectual Capital reporting practices in the non‐ profit sector. Virtus Interpress: Ukraine, http://www.virtusinterpress.org/IMG/pdf/Sample_Chapter‐2.pdf (accessed May 14, 2013) Brunie, A. (2009) “Meaningful distinctions within a concept: Relational, collective and geeralized social capital”, Social Science Research, Vol. 38, pp 251‐265 Capello, R. (2002) “Spatial and Sectoral Characteristics of Relational Capital in Innovaton Activity”, European”, Planning Studies, Vol. 10, No. 2, pp 177‐200 Card, S.K., Mackinlay, J. & Shneiderman, B. (1999) Readings in Information Visualization: Using Vision to Think, San Francisco, CA: Morgan Kaufmann. Collins, J.D. and Hitt, M.A. (2006) “Leveraging tacit knowledge in alliances: The importance of using relational capabilities to build and leverage relational capital”, Journal of Engineering and Technology Management, Vol. 23, No. 3, pp 147‐ 167 Ding. Y. (2011) “Scientific collaboration and endorsement: network analysis of coautorship and citation network”, Journal of Informetrics, Vol. 5, No. 1, pp 187‐203 Duparc, D. (2012) “A Theoretical Contribution: Web 2.0 and Entrepreneurial Corporte Culture Linked to Radical th Innovation”, Proceedings of 7 European Conference on Innovation and Entrepreneurship ECIE, 20‐21 September, Santarem, Portugal.

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Kaisa Still, Jukka Huhtamäki and Martha Russell Dyer, J.H. & Singh, H. (1998) “The relational view: Cooperative strategy and sources of interorganizational competitive advantage”, Academy of Management Review, Vol. 23, No. 4, pp 660‐679. Edelstein, D. (2009) “Humanism, l’Esprit Philosophique, and the Encyclopédie.” Republics of Letters: A Journal for the Study of Knowledge, Politics, and the Arts 1, no. 1: http://rofl.stanford.edu/node/27 (accessed June 24, 2013). Edvinsson, L. and Malone, M.S. (1997) Intellectual Capital: Realizing your compay’s true value by finidng its hidden brain power, New York, NY: Harper Business. Falagas, M. E.; Pitsouni, E. I.; Malietzis, G. A.; Pappas, G. (2007) "Comparison f PubMed, Scopus, Web of Science, and Google Scholar: Strengths and weaknesses", The FASEB Journal Vol. 22, No. 2, pp 338‐342 Garfield, E. (1972) “Citation analysis as a tool in journal evaluation”, Science, Vol. 178, pp 471‐479 Greenberg, S. A. (2009), “How citation distortions create unfounded authority: analysis of a citation network”, BMJ 339, b 2680 Granovetter, M. (1992) “Problems of explanation in economic sociology”, In N. Nohria, N. and . Eccles R.G. (Eds.), Networks and organizations: Structure, form, and action, Boston: Harvard Business School Press, pp 25‐56 Gray, J., Bounegru, L. and Chambers, L. eds. (2012) Data Journalism Handbook. Available: http://datajournalismhandbook.org/1.0/en/ Grootaert, C. and van Bastelaer, T. (2002) Understanding and Measuring Social Capital: A Multi‐Disciplinary Tool for Practitioners. Washington: World Bank. Gulati, R., Huffman, S. Neilson, G. (2002)”The Barista Principle. Starbucks and the Rise of Relational Capital”, strategy + business, July 17, http://www.strategy‐business.com/article/20534?gko=582b3 (accessed May 10, 2013) Kleinberg, J.M. (1999) “Authoritative Sources in a Hyperlinked Environment”, Journal of the ACM, Vol.46, No. 5, pp 602‐ 632 Kohtamäki, M., Partanen, J. and Möller, K. (2013) “Making a profit with R&D services—The critical role of relational capital”, Industrial marketing management, Vol.42, No. 1, pp 71‐81 Landström. H., Harirchi, G. and Åström, F. (2012) “Innovation and Entrepreneurship studies: One of Two Fields of th Research?”, Proceedings of 7 European Conference on Innovation and Entrepreneurship ECIE, 20‐21 September, Santarem, Portugal. Lawson, B., Tyler, B.B. and Cousins, P.D. (2008) “Antecedents and consequences of social capital on buyer performance improvement”, Journal of Operations Management, Vol.26, No. 3 pp. 446‐460 Liu, X., Guo, Z., Lin, Z. and Ma, J. (2012) “A local social network approach for research management”, Decision Support Systems (article in press), http://dx.doi.org/ 10.1016/j.dss.2012.10.055 (accessed May 14, 2013) Molka‐Danielsen, J., Trier, M., Shlyk, V., Bobrik, A. and Nurminen, M. (2007) “IRIS (1998‐2006) Historical Reflection through th Visual Analysis”, Proceedings of 30 Information System Research Seminar in Scandinavia IRIS 2007. Nahapiet, J. and Ghoshal, S. (1998), “Social capital, intellectual capital, and the organizational advantage”, Academy of Management Review, Vol. 23, No. 2, pp 242‐266 Newman, M. (2010) “Networks of information”, In Networks: An Introduction, Oxford: Oxford University Press, pp 63‐77 Ordonez de Pablos. (2003) “Measuring and reporting on relational and social capital”, Conference OLKC4, http://www2.warwick.ac.uk/fac/soc/wbs/conf/olkc/archive/oklc4/papers/oklc2003_ordonez.pdf (accessed May 10, 2013) OECD (2002) Glossary of statistics. http://stats.oecd.org/glossary/detail.asp?ID=3560 (accessed May 10, 2013). Partanen, J., Möller, K. Westerlund, M., Rajala, R. and Rajala, A. (2008) “Social capital in the growth of science and technology‐Based SMEs”, Industrial Marketing Management, Vol. 37, No. 5, pp 513‐522 Peritz, B. C. (1992) “On the objectives of citation analysis: Problems of theory and method”, Journal of the American Society for Information Science, Vol. 43, No. 6, pp 448‐451 Price, D. D. S. (1970) "Citation Measures of Hard Science, Soft Science, Technology, and Nonscience", in Nelson, C. E. & Pollock, D.K. (eds.), Communication among Scientists and Engineers, Lexington, MA: D.C. Heath and Company, pp 3‐ 22. Price, D. D. S. (1976) "A general theory of bibliometric and other cumulative advantage processes", Journal of the American Society for Information Science, Vol. 27, No. 5, pp 292‐306 Sveiby, K.E. (1997) The New Organizational Wealth: Managing & Measuring Knowledge‐based Assets, San Francisco, CA: Barrett‐Koehler Publishers. Walthne, K.H. and Heide, J.B. (2004) “Relationship governance in a supply chain network”, Journal of Marketing, Vol. 68, No. 1, pp 73‐78 st Wasserman, S. and Faust, K. (1994) Social Network Analysis: Methods and Applications, 1 Edition, New York, NY: Cambridge University Press. Weber, B. and Weber, K.(2007) “Corporate venture capital as a means of radical innovation: relational fit, social capital and knowledge transfer”, Journal of Engineering and Technology Management, Vol. 24, No. 1‐2, pp 11‐35 Welbourne, T. and Pardo‐del‐Val, M. (2009) “Relational Capital: Strategic Advantage for Small and Medium‐Size Enterprises (SMEs) Through Negotiation and Collaboration”, Group Decision and Negotiation, Vol. 18, No. 5, pp 483‐497 Wellman, B. and Berkowitz, S.D. (1988) Social Structures: a network approach, New York: Cambridge University Press. World_Bank (2007) Overview of Social Capital, http://go.worldbank.org/C0QTRW4QF0 (accessed May 23, 2013) Zahra, S.A. (2010) ”Harvesting Family Firms’ Organizational Social Capital: A Relational Perspective”, Journal of Management Studies, Vol. 47, No. 2, pp 345‐366

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The Personalised Computer Support of Knowledge Management Stefan Svetsky, Oliver Moravcik and Jana Stefankova Faculty of Material Sciences and Technology of Slovak University of Technology, Trnava, Slovak Republic stefan.svetsky@stuba.sk oliver.moravcik@stuba.sk jana.stefankova@stuba.sk Abstract: In real life, one must understand such terms as knowledge acquisition, management, transfer, creation, dissemination, sharing, as well as intellectual capital, knowledge management systems, organisational knowledge and learning. From another point of view, there are different types of knowledge ‐ tacit, explicit, and some authors use the term ‐ embodied knowledge. However, the concept of knowledge is often poorly defined; it has different meanings from the various points of view belonging to knowledge management, information technology, education, or philosophy. In terms of computer support, individuals work mostly with unstructured knowledge. This complicates any computer support related to the processing of knowledge. Such knowledge issues mentioned above were first solved at an individual’s level within an industrial R&D laboratory (a knowledge‐based organisation). Afterwards in the university, this system was modified with knowledge processing by the use of the in‐house developed, supportive personalised system of BIKE (Batch Information and Knowledge Editor). This took place within the research on Technology‐enhanced Learning at the Slovak University of Technology. The preliminary results of the long‐term research were gradually presented at conferences in the USA, Asia, Australia, and the EU. The system of BIKE was also presented as a multipurpose knowledge based system, including its application as a teacher’s personalised management system. It covers several fields in informatics that are ongoing from an interdisciplinary definition of knowledge, knowledge tables, and the formulated batch knowledge‐ processing paradigm. Knowledge is defined as being a set of information structured and unstructured, having a specified content stored in one row of the knowledge table within a default structure. This paper explains and illustrates how it works in practice on a personal level for individuals, i.e. as a supportive informatics tool for knowledge processing, creation, management, including some examples of the conversion of tacit knowledge into explicit knowledge, and the sharing of explicit knowledge within customer oriented knowledge management or teaching and learning processes. Keywords: knowledge sharing, knowledge management, knowledge processing, technology‐ enhanced learning, knowledge management systems

1. Introduction There are many attributes or terms relating to knowledge as was mentioned in the abstract, e.g. knowledge acquisition, creation, sharing, management, or intellectual capital, and knowledge management systems. Furthermore, different types of knowledge (tacit and explicit) are described in the actual knowledge management literature, including issues of conversion and interactions between them (the so‐called SECI model). When one wants to solve knowledge processing by computer, one may not be aware of the complexity of knowledge or knowledge based processes, a further complication is in the absence of any universal definition of knowledge. That is, knowledge has different meanings in the fields of knowledge management, information technology, education, philosophy, cognitive sciences, or neuroscience. This can be briefly demonstrated by the following examples: In the Oxford dictionary (2013) knowledge is defined as “facts, information, and skills acquired through experience or education; the theoretical or practical understanding of a subject ‐ the sum of what is known ‐ the transmission of knowledge information held on a computer system”. This definition is user friendly from a knowledge management point of view. The role of knowledge in cognitivism or constructivism theories can also be illustrated as is described in the Instructional Design Knowledge Base (Dabbagh, 2013) or by the example of constructivism in the classroom (Jorda, Campbell, 2013). Comprehensive information concerning the analysis of knowledge can be found in the Stanford Encyclopaedia of Philosophy (Ichikawa, Steup, 2012). Another category of papers discusses the issue of information and knowledge in the framework of critique of Artificial Intelligence, e.g. Searle (1996) discusses “if the brain is a digital computer.” He argues, “the sense of information processing that is used in cognitive science, is at much too high a level of abstraction to capture the concrete biological reality of intrinsic intentionality”. Therefore, “the brain, as far as its intrinsic operations are concerned, does no information processing.” Similarly, Chomsky argues that in opposite to the Artificial Intelligence approach regarding any real system “you can talk about the neurophysiological level, … there's no real algorithmic level, … there's no calculation of knowledge, it's just a system of knowledge” (Chomsky, 2012). However, this is not very understandable for laics. This example was mentioned in order to give a better understanding of why a precise

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Stefan Svetsky, Oliver Moravcik and Jana Stefankova definition of knowledge is missing, and to illustrate the interdisciplinary issue of knowledge more deeply. Moreover, due to the possibility to compare this approach with the approach presented in this paper, i.e. how processed information and knowledge, via the in‐house developed database application, works as a personalised knowledge management system (Svetsky, Moravcik, Stefankova, Schreiber, 2012). On the other hand, all individuals mostly intuitively understand a hierarchy of what are data, information, knowledge, and wisdom, respectively. In this context, a unique approach is also absent within Computer Science because data or information is often presented as the same thing as knowledge. For example, a Computer Science engineer utilises such a term as “knowledge discovery in databases” arguing that he wants to find, in this way, important knowledge for decision‐making. However, nonprofessionals only understand it as the steps needed for finding important information. In other words, other people do not understand the programmer’s abstraction of the real World. Computers are for people, not visa versa. One thing should yet be mentioned, in terms of computer support, individuals work mostly with unstructured knowledge within knowledge‐based processes, which are very complex and uncertain. All of this complicates any computer support focused on the processing of information and knowledge. Hence, a knowledge management system commonly consists of more than one piece of software. Whilst the acquisition of information from the internet or various sources is covered with many software applications, including very rich internet services (search engines, retrievals, educational and information portals), the applications which would solve how to share knowledge between knowledge workers, how to convert them and how to construct them, are rarely described in literature. This paper presents how these issues were solved within an industrial knowledge based R&D laboratory based on empirical research (the approach was based on information processing), following in the university’s environment within research on Technology‐enhanced Learning (the approach was based on knowledge processing).

2. Information processing in an industrial knowledge based organisation The issue of knowledge management is especially important in knowledge ‐ based organizations, because working with information and knowledge is the basis for their sustainability and competitiveness, for example, an industrial R&D Centre oriented on materials and technologies. This consisted of several departments, each of which had its R&D staff and its own testing laboratory. As shown on the schema in Fig. 1, the basis for knowledge management was:

The internal information flow between researchers and laboratory staff

Horizontal information flow between departments and laboratories

Vertical information flow between departments and central management and economic departments

The external information flow between departments, customers, R&D project partners, also including the flow from information sources (mostly standards and technical documentation – although not from the internet).

Figure 1: R&D company’s schema related to information flow

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Stefan Svetsky, Oliver Moravcik and Jana Stefankova The role of such R&D departments is important because as accredited laboratories they must provide testing services at the highest level. Therefore, the shared information must also be at a high level. In this case, the level of cooperation between departments and information management create the basic level of intellectual capital of the whole centre, i.e. its ability for solving customers’ orders. For information processing, a database application called WritingPad was developed, which served mainly for sharing tacit and explicit information with a focus on information management for customers of the automotive and electronics industries. All information about tests, technical reports, protocols, and projects were concentrated in the database tables of WritingPad and processed for decision‐making purposes. Thus, WritingPad worked as a personalised supportive information management system. Figure 2 represents a shared environment of the WritingPad which was used by the laboratory’s staff for writing notes related to each customer (as shown on the left scrollable part); on the right you can see data for neutral salt spray according to the international standard ISO 9227/NSS and the German standard DIN 50021‐SS.

Figure 2: Example displaying evidence of a laboratory’s tests in the use of a knowledge table Figure 3 illustrates a navigation screenshot of a Laboratory’s Information System (an off‐line html ‐ file). This system was developed within a national research project focused on technical testing. Arrows mark the content of the final report, folders, and other elements. The WritingPad’s graphical browser on the bottom‐left illustrates linking with one of the knowledge tables (i.e. laboratory’s standard 412).

Figure 3: Example of the menu of the laboratory’s information system It should yet be mentioned that this informatics support resulted in increasing the level of testing services from a regional level to an international level.

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3. Knowledge processing within a university The principle of bulk data and information processing by WritingPad was modified for knowledge processing within the research on the implementation of Technology‐enhanced Learning at the Slovak University of Technology ‐ Faculty of Materials Science and Technology in the teaching of bachelors. That is, the challenge to the knowledge processing mentioned above was solved by the implementation of a batch knowledge‐ processing paradigm. This paradigm was performed by the in‐house developed BIKE system (Batch Information and Knowledge Editor) this enabled knowledge workers or students to process knowledge in larger batches. Within this approach, knowledge is defined as being a set of structured and unstructured information, having a specified content stored in one row of the knowledge table within a default structure (more details in Svetsky, 2012). This definition seems to be universal and interdisciplinary, i.e., it is acceptable from both a laics’ and informatics’ point of view. It is also in compliance with the actual approach for Technology‐enhanced Learning that is understood as automation of teaching and learning processes. Moreover, because BIKE is a universal informatics personalised tool for knowledge processing, it can be logically suitable for knowledge management. Hence, it was also presented as a teacher’s personalised knowledge management system (Svetsky, S. 2012). BIKE’s applied application in the knowledge management field was consulted with the Department of cybernetics and artificial Intelligence of the Faculty of Electrical Engineering and Informatics, which is focused on such specific fields as the support of Knowledge Management in a distributed environment (Paralic, J., Paralic, M., Mach, 2001). In the framework of BIKE’s author’s lecture at this faculty, the students categorised presented BIKE’s examples as a knowledge base repository system. It must be emphasised that the principle of knowledge sharing in university teaching is the same as in a knowledge based industrial organisation. When teaching bachelors, activities are also knowledge based, thus the personalised approach is especially important due to the synchronisation of knowledge management between an individual’s level and an institutional level. In this case, it can be demonstrated by examples of linking a teacher’s system with the Academic Information System (university’s LMS) and the participated action research on Technology‐enhanced Learning when teaching bachelors at a regional detached workplace. In this context, the BIKE system or its part WritingPad, which is installed on computers in the classroom, enables individuals to share knowledge by using its knowledge tables where content is inputted by them. The knowledge tables can be converted into html‐format in order to be browsable by a common internet browser (default is OPERA and Internet Explorer). Institutional data from the Academic Information System of the Faculty can be copied to BIKE’s environment. Another way for knowledge sharing is by communication channels, i.e. a personalised network between the teacher and students. Furthermore, within actual research on Technology Enhanced Learning, the possibility for knowledge sharing or exchange by using specific functions of the OPERA browser is actually investigated. The method of knowledge sharing mentioned above is illustrated in Figure 4. There are examples of a list of teaching documents extracted from the university’s LMS system to the BIKE environment (1); a navigation html‐template made from an internet source (2); communication channel with bibliographical data shared within international cooperation of two faculties (3); OPERA browser environment demonstrating the so called relations that are used for knowledge sharing from browsing records made in this browser (4).

4. Conclusion Some aspects of knowledge were discussed in terms of its interdisciplinarity and the absence of a unique definition of knowledge. This is a big challenge for anyone when solving knowledge management or informatics support of knowledge based processes running in industry or university teaching and learning. The approach for information and knowledge processing presented how it was solved and worked in practice on a personal level for individuals, i.e. for either knowledge workers or teachers and students. Presently, the informatics support approach is understood as the automation of knowledge processing by use of the in‐house developed system of BIKE, which is a basic tool for performing the batch knowledge‐processing paradigm. In this case, a working definition of knowledge is used; it is relatively user friendly from a knowledge management point of view. Data, information, and knowledge is inserted in a system of knowledge tables of the BIKE system, i.e. this enables individuals to share knowledge, and it does not matter whether it is in industrial R&D activities or in university’s teaching and learning. Thus, the BIKE system works as well as a personal teacher’s knowledge management system. Two examples of how to share information and knowledge was presented in this paper, one from an industrial R&D company and the other from when teaching bachelors. Such information sharing resulted inter alia in the systematic creation of shared

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Stefan Svetsky, Oliver Moravcik and Jana Stefankova intellectual capital, and in assuring the sustainability of knowledge workers such as researchers, laboratory staff, and teachers. Therefore, the presented approach for knowledge management based on computer support had a significant benefit in both cases when increasing the quality level of the industrial or faculty department from a common level to an international level. In this context, the future development will be focused on investigating interdependencies between personal and institutional knowledge management, as in this case, benefits for individuals or an organisation are shared, and therefore this may have a positive synergic effect. In this line of approach, further research will be oriented towards investigating the creation of knowledge templates for the conversion of tacit knowledge into explicit (externalization), and programming a set of informatic tools for process‐driven knowledge sharing.

Figure 4: Screenshots illustrating knowledge sharing within teaching bachelors

Acknowledgements This work was supported within the funded project KEGA No. 047STU ‐ 4 / 2012: The building online teaching room for education in the field of machining complex shaped components.

References Dabbagh, N. (2013) “The Instructional Design Knowledge Base”, [online], Nada Dabbagh's Homepage, George Mason University, Instructional Technology Program, http://classweb.gmu.edu/ndabbagh/Resources/IDKB/models_theories.htm. Ichikawa, J. J. and Steup, M. (2012) “The Analysis of Knowledge”, [online], The Stanford Encyclopedia of Philosophy (Winter 2012 Edition), Edward N. Zalta (ed.), http://plato.stanford.edu/archives/win2012/entries/knowledge‐ analysis/. Jorda, M. and Campbell, S. “Cognitivism and Constructivism. Part II, Educational Implications”, www.coe.fau.edu/faculty/cafolla/courses/eme6051/cognitivism_and_constructivismII.htm. “Noam Chomsky on Where Artificial Intelligence Went Wrong” (2013), [online], The Atlantic Monthly (interviewed by Yarden Katz Nov 1 2012, 2:22 PM ET), www.theatlantic.com/technology/archive/2012/11/noam‐chomsky‐on‐where‐ artificial‐intelligence‐went‐wrong/261637/4/. Oxford Dictionaries Online (2013), [online], Oxford University Press, http://oxforddictionaries.com/definition/english/knowledge. Paralic, J., Paralic, M. and Mach, M. (2001) “Support of Knowledge Management in Distributed Environment,” Informatica, Knowledge Based Software Engineering Information Technology, Vol. 25, Ljubljana, 2001. Searle, R. J. (1996) “Is the Brain a Digital Computer?”, [online], https://mywebspace.wisc.edu/lshapiro/web/Phil554_files/SEARLE‐BDC.HTM. Svetsky, S., Moravcik, O., Stefankova, J. and Schreiber, P. (2012) “IT Support for Knowledge Management within R&D and Education”, ICL 2012, 15th International Conference on Interactive Collaborative Learning and 41st International Conference on Engineering Pedagogy, Villach, Austria, Piscataway, IEEE, 2012. Svetsky, S. (2012) The practical aspect of knowledge construction and automation of teaching processes within Technology‐enhanced Learning and eLearning, Habilitation thesis, Slovak University of Technology.

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The Moscow State University of Economics, Statistics and Informatics (MESI) on the way to Smart Education Vladimir Tikhomirov Moscow State University of Economics, Statistics and Informatics (MESI), Russia, Moscow VPT@mesi.ru Abstract: The paper deals with the penetration of the smart concept into society with respect to education development. Emerging “smart” technologies have a great impact on students, course design, and knowledge communication, and this needs to be taken into account in the development of e‐learning and knowledge management at universities. The author examines methods of university development using the MESI example. Keywords: smart, e‐learning, knowledge management, academic knowledge

1. Introduction Knowledge flow has a big influence on the development of modern society and the economy. New information and communication technologies (ICT) have led to the emergence of a new world – Smart ‐ where are no barriers to the creation, sharing and dissemination of knowledge. This is mainly due to the development of the Internet and new ICT to reduce the number of steps on the way form knowledge creation to implementation. ICT makes knowledge flow independent of rules and stereotypes. Moscow State University of Economics, Statistics and Informatics (MESI), founded in 1932, is now one of the top Russian universities combining breadth of experience in strong scientific traditions with innovative processes in training professionals in economics, management, statistics, information technology, law and the humanities. Accoding to Russian and Moscow annual ratings, MESI is traditionally among the best Russian educational institutions. Today MESI is a venue for many students including foreign students from 52 countries. MESI is an innovative complex, which has a network of more than 30 regional branches located in different parts of Russia and abroad. The new challenge for the university is integrating students into the new knowledge environment to provide them access to emerging knowledge and technologies. At the same time, universities actively impact on the knowledge environment. Educational technologies allow the learning process to leave the classroom and the campus. Smart technologies should meet the increasing public demand for high quality educational services. Approaches to education should be completely reconsidered in terms of both content and learning methods, including methods of knowledge management (Hwang, D.J.; Yang, H.; Kim, H. 2010). The important educational task is to organize students for self‐learning through knowledge acquisition and implementation for professional development. A shift to Smart Education is absolutely necessary for developing countries to take an appropriate part in the changing world. Smart education will allow students to use ICT in the future to work effectively. Valuable competencies are becoming collaboration via the Internet, the ability to work with a huge amount of information. The main effect of Smart education is the ability to combine the efforts of a lot of people to create new knowledge and innovations. Implementation of the principles of Smart education by classical approaches to the educational content development will not obtain the desired effect. A new type of educational resources is necessary for smart society development. The educational paradigm is changing from the traditional model to Smart Education via e‐learning. The role of universities is changing from that of knowledge vendor or navigator to being a knowledge generator to the facilitator for student self‐learning and innovative activities. Smart education is able to create a new university, where the set of ICT and faculty leads to an entirely new quality of the processes and outcomes of the educational, research, commercial and other university activities.

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2. Smart society The important distinctive feature of the present stage of social development is ICT penetration. Innovative communication technologies have led to the emergence of a new world, as well as a reassessment of the values and needs of the market. Today knowledge is a commodity increasingly in demand. The possession of knowledge is not enough ‐ knowledge must be updated constantly. The amount of knowledge doubles every 72 hours. New technologies like web 2.0 influence the increasing amount of knowledge in the era of the information society especially (Figure 1). These technologies are the key for delivering relevant knowledge to students. Many countries ‐ such as South Korea ‐ are more advanced in their technological development than Russia. What should be done to overcome the gap between Russia and more technologically advanced countries? The governments of many developed countries support and promote the concept of Smart in education development for both economic and social development. The core of the Smart concept is based on three main issues:

Mobile access. The ability to produce all kinds of services via mobile networks anywhere in the world. The services are targeted to each user individually;

Generation of new knowledge. There is no way for any country to develop without access to new knowledge. The only right direction based on new knowledge generation as the engine of the modernization process of the national economy;

Smart environment design. Despite the fact that the present level of computer systems development doesn’t allow us to talk about the creation of artificial intelligence, some services and technological developments have reached the point where the IT environment is almost identical to natural intelligence. The Smart environment facilitates the emergence of innovative goods and services as the basic value of the Smart economy.

Figure 1: Comparable amounts of knowledge increase in the context of technology development The first digital divide was discussed around the world several years ago. The first digital divide means the gap in the development of the IT industry; lack of technology, low rate of Internet penetration, broadband, low‐ skilled people in IT, etc. This allowed us to asses the positions of countries, nations, and continents on the intensity of digital technology implementation and development. The indicators were mostly quantitative. There was the following link: countries with a large number of technologies get more advantages for their development. At present, Russia has overcome this gap. There are enough computers and other equipment in universities and schools, and quite skilled people to work with it. What is next step towards being a developed country?

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Vladimir Tikhomirov New aspects are emerging in the second digital divide. A large number of human functions have been transferred to machinery and equipment. People have had the opportunity to focus on creativity and self‐ development. The question arose: what new effects and new efficiencies are gained by those with these new technologies and capabilities? Let's see, we have been starting to prepare books in an electronic format. This has new outcomes for students and faculty. The philosophy of the second digital divide involves the extraction of a new effect from ICT. Information and communication technology implementation depends on motivation and the involvement of people in the use of technological diversity. Knowledge has become open and accessible. One example of this are open educational resources. People's attention can be attracted to the problem only by opening their own knowledge. In this case, discussion will arise and a new approach to the problem will be found. The active use of new knowledge is the marker of the second digital divide. In education this is expressed as follows. Some universities use open educational resources, others ignore them. Some create a new one open educational resource and massive open online courses (MOOC) ‐ others protest against it. Economic development in the Smart direction requires appropriate modernization of all industries without exception. Along with the concept of the "Smart Economy", arises the concept of the "Smart society" (Tikhomirova, N.; Tikhomirov V. 2012). These concepts partly overlap each other. But the second concept is more connected to the conditions in which people live. The population of the country has to be plunged into a complex Smart concept including smart universities, a smart transportation system and a smart government. Some developed countries ‐ for example South Korea and Ireland ‐ have developed the Smart concept. Their ideas regarding the development of this concept are reflected in relevant documents. For example, Ireland approved the document: «Building Ireland’s Smart Economy». The transformation of the economy on the way to Smart is the chain in which the transition of one industry entails the development in other activities (Urintsov, A.I. 2003). Today, most people have become accustomed to concepts such as e‐money and e‐commerce. But these innovations are already out of date, because progress continues. These areas have long been developed in accordance with the concept of Smart. For example, «e‐money» transformed into Smart money namely new payment systems allowing financial transactions anywhere in the world and in individual circumstances.

3. Smart education Economic transformations affect education. In many countries, the concept of Smart education is already a de facto standard. What is the main idea of Smart education? To answer this question it is necessary to consider the progress of approaches to education development. Conventionally, it can be divided into three stages, and to consider in the context of the five visions, such as knowledge, technology, learning, faculty and business. In the past there was the only one source of knowledge for students, namely the teacher. There were no opportunities to learn anywhere else. Students gained new knowledge in the classroom or in the library. The purpose of a university was to prepare human resources for industrial production. Now, knowledge sources and the operative tools technology has provided have changed dramatically. There is no one‐way traffic for knowledge transfer. Faculty and students are equal in sharing their knowledge and in creating new knowledge. At the same time thanks to new educational technology, faculty can carry knowledge outside the classroom as in the case of MOOC. Business in turn submits new demand for creativity and independent personalities. There is no doubt that in the future the main knowledge stream will flow through the Internet, and technology will be individually oriented and aimed at creating new knowledge. The learning process will support knowledge sharing between lecturer and student, and this time it will be a two‐way road. The graduate will be able to become not only an employee but to join the business environment as a partner or business owner. Over the past decade, the information society has taken shape. There are attributes of it such as the knowledge economy, e‐culture, e‐health, e‐government, and e‐science. E‐learning is implanted into the

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Vladimir Tikhomirov structure of the information society and is its central backbone. However, the e‐learning approach emphasizes primarily the technological aspect. The technological development of the leading universities has reached a point where further development of the IT base would not bring new quantitative or qualitative changes in educational outcomes. E‐learning is no longer an innovation for education development. Educational content is freely available to students, and by providing feedback on lecturers and students, they share knowledge between them, and the automation of administrative tasks. All of this relates to technology. But what is the next step? What are people doing with these technologies, what is the effect? These issues are already in the context of Smart education. It is able to provide the higher level of education according to the challenges and opportunities of modern world. Smart education will allow young people to adapt to the rapidly changing environment. Smart education is an integration of educational institutions and faculty members to carry out joint educational activities via the Internet. We are talking about collaborative development and use of the content of co‐education. An example is the project of the next decade in the European education system. One European university will provide a common administration for other universities, who will accompany the students as they move from one higher school to another. The Bologna process gives universities the opportunity to enroll students without a re‐examination. Thus the Smart education system is being designed in Europe. The European University will implement a collective learning process through a common repository of training materials. Smart education means flexible learning in an interactive learning environment with content from around the world, in the public domain. The key to Smart education is the wide availability of knowledge. In turn, the purpose of smart learning is to make the learning process more effective by the transfer of educational processes in the electronic environment. This approach will copy the knowledge of the lecturer and to provide access to anyone interested. Moreover, it will expand the boundaries of learning, not only in terms of the number of students, but also in terms of temporal and spatial factors. Education will be available everywhere at anytime and to any person. One of the conditions for the transition to smart e‐learning is the movement from fixed educational materials to the design of active content. In this case, the knowledge increment is getting in the active content immediately. Knowledge should be placed in the repository equipped with the intellectual search engine. Knowledge objects should be interconnected by the metadata system. The quality of the repository should be continuously monitored through the introduction of the systems such as e‐metrics and a learning management system. Smart education is a concept that involves a comprehensive modernization of all educational processes including methods and techniques. The Smart concept in education entails the emergence of technologies such as smart boards, smart screens and wireless Internet access everywhere. Each of these technologies offers a new way to design the process of developing, delivering and updating educational content. The active content is a main element of Smart education concept which removes all restrictions (Figure 2).

4. Academic knowledge management The Smart education concept highlights individual learning outcomes. This means that educational content has to meet the individual needs of every student. Academic knowledge management focuses on identification and description of new knowledge objects (elements). The information system of the university facilitates the gathering of knowledge objects, its combination and creation a unique content which would meet the needs of each student. Such an approach is useful in terms of content creation as well as updating. Management of academic knowledge is closely linked to the process of administrative knowledge management (Figure 3). The knowledge created in an academic environment can be actively used in administration work and vice versa. The processes of academic and administrative knowledge management are quite similar. There is a phase of generating new knowledge in both processes. In the academic environment, this phase is conducting research and development. Next comes the phase of the sharing and transfer. If we take education, this process would

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Vladimir Tikhomirov be described as training. And the last phase is the implementation of knowledge, and, in the academic environment, this phase would be transformed into the commercialization of knowledge. Commercialization stimulates the first phase of the knowledge management cycles through educational services providing and research support by public and private funds.

Figure 2: Smart education concept

Figure 3: Knowledge management for university The university using traditional educational forms can provide a very small amount of knowledge in comparison to the new educational forms. A lot of the content would never be printed as a book. Operating with only hard copies, lecturers and students deprive themselves. Stagnation of the education system will lead to stagnation of society in less than a decade. Smart education will create the conditions for the synchronization and harmonization between the emergence of new knowledge and the accessibility for students.

5. Smart university Smart education requires changes in university structure and the management system. The core changes concern training course development. Moreover, this process should be distributed; thereby many lecturers would be involved in new knowledge generating and dissemination. This helps to build the professional community. Collaboration between lecturers can establish a continuous process of development and improvement of university courses, which will subsequently be transferred to an e‐learning system, and to the external repository. The process of content development should be kept under the control of the quality management system to evaluate the satisfaction of student and other results. In particular, the element of this system is the e‐metrics (Figure 4).

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Vladimir Tikhomirov The system of continuous audit of the educational process quality

Satisfaction with quality of education. Proposals for improving

The e‐Metricx system in real time reads performance of the whole network of the parent institution and branches

course

Students Activities in the Information Centers for Courses Work in the e‐campus

Transferring content and updating courses

Training Course The key role of the

Communication and development Departments

faculty in developing courses

Figure 4: Collaboration to develop training courses The transition to the Smart education concept is not possible without the introduction of e‐learning. The implementation takes at least two years (Hwang, D.J.; Yang, H.; Kim, H. 2010). MESI designed the solution "Quick Start" to the e‐learning for any university. The solution is based on cloud computing, so that MESI’s resources and content could be used by other universities. Implementation of “Quick Start” aims to:

increase the effectiveness of management;

avoid the pitfalls of the choice of technological solutions and methods for e‐learning;

reduce the financial and time costs significantly by using packaged software for e‐learning;

gain e‐learning experience;

improve faculty skills;

facilitate the development of e‐learning technologies and methods.

These proposals should allow better use of intellectual and information resources for the training of students and research through enhanced educational space, and reduce the cost of training, further training and retraining of staff.

6. Conclusion Smart education means flexible learning in an interactive learning environment that allows student to adapt quickly in a changing world, to study everywhere and at any time. Smart education is based on the approach of individual trajectory of learning to meet every need of every student. The main objective of the new concept of education is to design an environment which would provide new opportunities for student abilities through the improvement of training courses and educational materials. Smart education is the response to the challenges of the information society, such as cooperation, communication, social responsibility, the ability to think critically, and efficient and qualitative problem solving. The development of the education system focuses on the integration knowledge objects, e‐metrics and systems management process. Due to the development of approaches, methods and technologies of e‐learning is inevitable transformation of education in the direction of Smart. MESI was the first Russian university which has over the way to e‐learning. MESI is the first on the way to Smart education.

References Hwang, Dae Joon; Yang, Hye‐Kyung; Kim, Hyeonjin (2010) ‘E‐Learning in Republic Korea’ Mosscow, UNESCO Institute for Information Technologies in Education. Tikhomirova, N.; Tikhomirov V. ed. (2012) ‘Russia on the Way to Smart Society’ Moscow, IDO press. Urintsov, A.I. (2003) ‘Instrumental means of adapting an economic organization to permanent changes in the environment’ Automatic documentation and mathematical linguistics Vol.34, No.4, New York, Allerton Press, Inc.

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The Management of the Intellectual Capital in the Russian Industrial Networks Elena Tkachenko1 and Sergey Bodrunov2 1 Saint Petersburg State University of Economics, St. Petersburg, Russia 2 New Industrial Development Institute, St. Petersburg, Russia eletkachenko@ya.ru inir@inir.ru Abstract: One of the most vital issues of intellectual capital management is the transformation of the Intellectual Capital (IC) in the results of activity of the industrial enterprises. The solution of this problem can be offered, based on the formation of the Network Navigator of The Intellectual Capital (NNIC). The theoretical basis of the creation of IC navigators was developed for the micro level (see J.Roos, S.Payk and L.Fernstrem (2006) or the Skandia Navigator model developed by L. Edvinsson(1997). Authors suggest using these principles on the meso level. Considering the network of innovative and production firms as macro‐corporations, we have an opportunity to extend approaches to the management of the intellectual capital from the micro level to the meso level. In the paper, authors consider the case of constructing NNIC for the automobile cluster in St. Petersburg (Russia). Keywords: intellectual capital, navigator of the intellectual capital, Russian industrial networks

1. Background The development of the world economy is now defined by the growing importance of the intellectual capital. In assessing the reliability of the company in terms of its credit solvency or participation in long‐term common projects, the appraisers and investors judge by the fact that the value of the intellectual capital must be at least 40% of the overall capital structure of the company (S.M. Klimov, 2002). According to the estimates of the Accounts Chamber of the Russian Federation (2009), the average value of the intellectual assets of Russian companies does not exceed 0.5% of the book value of assets. The analysis of investment in nonfinancial assets clearly shows that the share of investment in intangible assets, research and development in recent years does not go beyond 1% of the total nonfinancial investment. The economy of St. Petersburg is now experiencing a new industrialization. The data obtained by the authors in the course of their research is different from the official statistics. This applies to the information about the state of the technological competitiveness of industrial enterprises in St. Petersburg. As a result, according to the survey of senior managers of 120 industrial enterprises conducted by the authors, 81% assessed the technological level of production as competitive, i.e. not worse than that of its main competitors, and 13% stated their technological advantage. Taking into account the increasing role of innovation, researchers call the modern economy "an economy based on knowledge," "learning," information or an innovative economy (D. Foray, 1999). Obviously, in these conditions, management of intellectual capital becomes extremely important. Networks become the main form of the organization of the industry. The hypothesis of our research is the possibility of the application of methods of intellectual capital management developed for the corporate level in the industrial networks.

2. Development of industrial networks and clusters during new industrialization The analysis of the advantages of network forms of the organization and formation of a network paradigm of industrial development is submitted in scientific literature of the 80th and 90th years of the XX century. Questions of the formation of networks were considered by Snow С. С., Miles R. E., Coleman H. J., Jr.(1992), Hakansson H., Johanson J. (1993), Smith K. G., Carroll S. J., and Ashford S. J(1995). The detailed analysis of concepts of network development is carried out by V. S. Kat’kalo (1999). The main advantage of industrial networks is ensuring the competitive advantages to their participants at the expense of a synergy effect. M. Porter (1990) formulated the principles of a cluster form of industrial development. In Russian economic science, the cluster concept was logical continuation of the theory of the territorial and production complexes developed by N. N. Kolosovsky (1953). New industrialization (new industrial development) is a response to the invalidity of the concept of post‐ industrial development in relation to the objectives of regional sustainable development. New industrialization

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Elena Tkachenko and Sergey Bodrunov is the development of industry on the new technological base and total introduction of energy‐saving and green technologies. New industrialization aims to address not only economic but also social problems that require a large‐scale introduction of innovative technologies, including the intellectual capital management. Regional innovation and industry clusters are based on the current stable system of dissemination of new technologies, knowledge, products and the so‐called technological network supported by the joint research base. The actual combination of cycles of the knowledge and technology exchange under a single management system will help to build the institutional support structure (the core and backbone) of the future cluster that brings together several organizations in their brand new, non‐existent industries today. The process of cluster formation is heuristic in nature. In the essence a cluster is a network structure formed through the cooperation of companies for the creation of a particular product. It should be noted that the extent of cluster formation depends strongly on the specific core process that ensures the creation of the final product. The number of cluster formations can be quite significant, as well as the number of companies belonging to the system. One of the most significant problems of intellectual capital development management is to build a model of interaction between the separate elements of the industrial complex that contributes to the transformation of the intellectual capital into the result of the industrial companies’ activities. The concept "intellectual capital" includes assets such as brand, customer relationships, patents, trademarks, and, of course, the experts’ knowledge of the enterprise. The so‐called unrecorded assets of the organization are often equal to the booked assets or even exceed them. The intellectual capital indicators to be measured are some important aspects of company activities (results, processes), and the measuring instrument or procedure is a selected or designed feature that allows measurement of the quantitative and qualitative criteria. There are several reasons for keeping records of intangible assets, in particular:

it reflects the value of the company more adequately;

the measurement process allows you to look deep into the driving forces supporting the productivity of the enterprise;

demands for effective management of intangible assets are raised both by the management company and other interested parties, including state and society;

investment in the company increases if the reporting system provides existing and potential investors with more complete information about the company.

Each organization faces the issue of its performance: how to use resources to maximize the potential of creating a value? We should not forget that there is no correlation between the amount of resources available to an organization and the value that an organization can create. If an organization gets more money, equipment, customers, or suppliers, improves the processes, and uses more qualified staff or more intellectual property (IP), it does not automatically mean that it will produce a higher value. The value that is produced by the company depends on how it uses its resources. It all comes down to making a firm decision on what resources will be transformed into other resources (specify) and in what order. Any organization in a sense represents a resource portfolio with a unique transformational structure wherein there are no two similar companies. All the resources of the organization are in some way related.

3. Approaches to management of intellectual capital Problems of the management of intellectual capital are a subject of scientific research for many scientists. Klein, D. A. and Prusak, L. (1994), Etzkowitz, H. (1997), Roos, J.; Roos, G.; Dragonetti, N.C.; and Edvinsson, L. (1997), Bontis, N. (1998), Leibowitz, J. and Wright, K. (1999) Van den Berg, H.A. (2002) created the methodology of assessment and management of intellectual capital. A. Kok (2007) in his article made the brilliant review of the main approaches to IC management. We completely share the opinion of the author of this article concerning advantages of the model of L. Edvinsson.

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Elena Tkachenko and Sergey Bodrunov Model "Skandia Navigator" developed by L. Edvinsson (1997) evaluates the intellectual capital of a company in terms of “creating value”. Companies today need to understand that the purpose of their activities is not knowledge generation but the creation of values. Therefore (and as it has been said before that "Skandia Navigator" is inappropriate to determine the market value, but it helps managers look at the problem more broadly while making their decisions), it should be deemed an important management instrument, rather than as a method of determining the value of intangible assets. "Skandia Navigator" allows you to reveal information about the "hidden values" that otherwise would never have been discovered, but it doesn’t assign any value. This model provides information that may be more likely to exist in the form of a supplement to the financial statements rather than be integrated in terms of the traditional accounting indexes. It allows you to manage a more complete picture of the true nature and value of the company, but not to calculate its value in the end. It is important to remember that all transformations are possible, but not all of them are appropriate in the particular company. A graphical display of the impact of resources on each other creates a logic diagram that is used by the management of a particular company in the allocation of resources. You should not confuse NIC with other models that attempt to visualize the actual course of events in the company. NIC is at a higher level of abstraction than these flow models, and at the same time, it is more useful (as we shall see further) as it gives a complete picture of all the transformation processes and the actual assets that are involved in the creation of values in the company. All managers have a more or less clear model of how value is created in their companies. This model structures both the way of thinking and decision‐making. This is particularly noticeable in critical situations when managers do not have enough time to make an informed decision. And they make decisions intuitively, based on existing mental models. Moreover, mental models are based on personal experience, so they will differ for different members of the leadership team. The team that has worked together for a long time is likely to have a more uniform pattern than people who have just started working together. The experience of the authors suggests that a significant advantage of NIC is the integration of views on ways of creation values that have the members of the management team involved in the development of NIC. The intellectual capital of the industrial complex needs information‐exchange platforms to effectively organize the interaction of various companies. The solution to this problem may be offered through a network of the intellectual capital navigator (NNIC). The theoretical bases for the construction of navigators IC have been developed for the micro‐level (see, for example, work by J. Ruus, S. Pike, and L. Fernstrem(2006)). We propose using these principles to a network or cluster level. The navigator of the intellectual capital is a digital and graphical representation of how management sees the deployment of resources to create value in the company. NIC displays the transformation of one resource to another. At the industrial level, NIC provides the management subject with a better understanding of the true nature and the contribution of certain companies in shaping the overall value of the intellectual capital that allows us to offer a set of promoters and regulators to increase the efficiency of the industrial intellectual capital. The graphical display of the IC assets influence on each other creates a logic diagram that reflects the movement of IC assets flows. NNIC gives a complete picture of all the processes of IC asset transformation in the results of industrial production. The process of creating NNICis a consensus process designed to identify the regional and industrial environment of implicit knowledge about how information‐exchange processes take place in the areas that affect IC operation. The main steps of this process are:

compiling an asset tree, at least, up to the third level;

determination of asset weights according to their ability to influence the IC value creation;

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Elena Tkachenko and Sergey Bodrunov

assessment of identified assets in terms of their suitability to become the basis of competitive advantage, as well as the evaluation of the quality and quantity of each asset;

assessment of the dynamics of how these resources are used;

assessment of the resulting using structure.

It is reasonable for the regional and industrial level as well as at the micro level to make two NNICs: for the current state and desired stable state in the future. The basis for the graphic reflection of the NNIC development is the process of handling a matrix showing the relation of IC assets in order to build a normalized and refined transformation matrix. The graphic version allows NNIC to visually demonstrate and identify the complex types of organizational relationships, and therefore represents a qualitative tool for strategic decision making. Usually, there shall be two NIC’s: one for the current state and the other for the desired stable state in the future (the future here is defined with the strategy for cluster development and strategic time horizon). Considering the network as a mega‐corporation, we get a possibility to extend the approaches to the intellectual capital management from the micro to middle level of management. And as in a corporation the cluster requires specialized units that organize the intellectual capital management. Investment and industrial agencies that perform essentially the function of the coordination of investment processes, coupled with the modernization of the industrial complex can perform as such divisions. Typically, these companies have an expressed industry specialization that increases the efficiency of their use as agents for information exchange.

4. NICC‐method application in industrial networks: Case of an automobile cluster of St. Petersburg Let’s consider the process of NNIC creation for the agency based on the needs of the automotive cluster of St. Petersburg. Now, control of the intellectual capital in an automobile cluster isn't exercised. In the calculations, The authors used the method of NIC construction developed by J. Roos, S. Pike and L. Fernstrem. The authors carried out interviews with 12 independent experts. Interview questions reflected key parameters of an assessment of assets of the company and its influence on company value. It was offered to experts to estimate a contribution of each asset to value formation. Statistical processing of results was carried out by a method of statistical expectation. The NNIC creation should begin with the construction of the Asset Tree for territorial and industrial organization on which the Agency specializes. We’ll make up a third‐level asset tree on the basis of expert 1 assessments of qualified employees of “Investment Agency St. Petersburg” . It will have the following form (Fig. 1). Since the basis of this cluster is intangible assets — technology, licenses, patents, industrial designs, goodwill, brands, etc., in the future intellectual assets must amount to at least 60% of the total value of the cluster. In this case, it is not about assembly plants, but about the manufacturers of automotive components. Currently, the proportion of IC does not exceed 40%. Proceeding with the consequence of creation of the Intellectual Capital Navigator we determine the weight of cluster assets according to their ability to influence the value creation. And we make up a normalized transformation matrix for the cluster (Table 1). Then the second‐level matrix should be cleared of the minor assets by weight. We use the method of statistical approximation to identify half the value of the average of the assets transformation. The average assets transformation is =4/2=2, anything less than it will be treated as noise. Then we get the following standardized and cleared transformation matrix (Table 2):

The agency name was changed in accordance with the confidentiality agreement

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Figure 1: Actual asset tree for an automotive cluster of St. Petersburg. Table 1: Standardized and cleared matrix generalized to the second level

financial physical relational managerial human Total

financial

0.5

2.5

1.5

0.25

physical

16.5

5.5

8.25

relational

managerial

human

Total

18.5

17.5

18.5

100

Table 2: Standardized and cleared second‐level matrix

financial physical relational managerial

human

financial

2,5

physical

16.5

5.5

8.25

relational

managerial

human

Total

18.0

11.5

16.5

18.25

On its basis one can clearly identify the need to change the growth directions of the cluster assets. It is obvious that it is necessary to pay attention to the development of the intellectual assets that form a large part of the value compared with the traditional ones and adjust the direction of investment accordingly. The asset tree for the strategic perspective takes the following form (Figure 2) As a recommendation, it is necessary to pay attention to the managerial resources of the cluster, including brand equity, industrial property, etc., that are not sufficiently developed at present.

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Figure 2: Target asset tree for an automotive cluster of St. Petersburg.

5. Conclusion The research conducted by the authors shows that it is the effectiveness of the intellectual capital management that gives industrial networks a competitive edge in the new industrialization. The use of innovative approaches to network interaction management makes it possible to use the benefits of open innovation systems to the full extent and minimize the risks of open innovation. Management of intellectual capital in industrial networks in Russia is on an embryonic phase. Measures of industrial policy for stimulation of cluster development lead to the transformation of clusters and networks to corporations. New industrial clusters quickly lose flexibility of the network organization on pressure of a rigid hierarchical control system. The few networks keeping independence need consulting support for adequate assessment and development of intellectual capital. Our recommendations allow use of the combinations of expert estimates with convenient graphic representation of results that has to simplify the management of IC and stimulate the development of intellectual assets of industrial networks.

References Bodrunov S. (2010) Report on the research project “Development concept for the industrial complex St. Petersburg 2020” (2010) FINEC Bontis, N. (1998) “Intellectual capital: an explanatory study that develops measures and models”, Management Decision,Vol. 36, No. 2, pp63‐76. Edvinsson, L. (2002) Corporate longitude, London, Prentice Hall. Edvinsson L., Malone Mike S. (1997), Intellectual Capital, Harper Business Press, 1997 Etzkowitz, H. (1997) “The entrepreneurial university and the emergence of democratic corporatism.” In: H. Etzkowitz, and Foray D. Cris (1999), A Cost or an Investment? Models of Innovation in the Information Age //CRIS 98 Conference. – Luxembourg, 1999. Hakansson H., Johanson J. The network as a governance structure: Interfirm cooperation beyond markets and hierarchies //The Embedded Firm. The Socio‐Economics of Industrial Networks / Ed. by Grabber G. London, 1993. http://www.wto.ru/ru/newsmain.asp http://ec.europa.eu/enterprise/dg/objectives/index_en.htm Катькало В.С. (1999) Межфирменные сети: проблематика исследований новой организационной стратегии в 1980‐ 90‐е годы Вестник Санкт‐Петербургского Университета сер. 5. вып. 2 (№ 12) Klein, D.A and Prusak, L. (1994) Characterising Intellectual capital, Cambridge, MA, Centre for Business Innovation, Ernst and Young. Klimov S.M. (2002) Intellectual Assets of a Company. ‐ M.: Znanie, 2002.‐199p. Kok, A (2007) “Intellectual Capital Management as Part of Knowledge Management Initiatives at Institutions of Higher Learning” The Electronic Journal of Knowledge Management Volume 5 Issue 2, pp 181 ‐ 192 , available online at www.ejkm.com Leibowitz, J. and Wright, K. (1999) “Does measuring knowledge make ‘cents’?”, Expert systems with applications, Vol.17, No.1, pp99‐103. Miles R.E., Snow C.C. Fit, failure and the hall of fame: How companies succeed or fail. New York, 1994.

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Elena Tkachenko and Sergey Bodrunov Рюэгг‐Штюрм Й., Янг М. Значение новых сетеобразных организационно‐управленческих форм для динамизации предприятий // Проблемы теории и практики управления: Международный интернет‐журнал. — 2001. — № 06 Roos, J.; Roos, G.; Dragonetti, N.C.; and Edvinsson, L. (1997) Intellectual capital: navigating the new business landscape, London, Macmillan Press. Roos G., Pike S., Fernstrom L. (2006) Managing Intellectual Capital in Practice. A Butterworth‐Heinemann Title ( 2006) Russia in Figures – 2012. Annual Abstract of Statistics. M.: Federal State Statistics Service, 2012 Simkina L.G. (2003) Human Capital Asset in Innovation‐Based Economy. –St. Petersburg.: ENGECON, 2000. – 152 p. Smith K.G., Carroll S.J., Ashford S.J. Intra‐and interorganizational cooperation: Toward a research agenda. Academy of Management Journal. 1995. N 1. P.7‐23. Snow С.С., Miles R.E., Coleman H.J., Jr. Managing 21st century network organizations. Organizational Dynamics. 1992. Vol.20. N3. P. 19. Stewart, T.A. (1997) Intellectual capital: the new wealth of organisations, London, Nicholas Brealey. Sveiby, K‐E. (2004) “Methods for measuring intangible assets”, [online],http://www.sveiby.com/articles/IntangibleMethods.htm Tkachenko E.A., Sobolev A.S., Kudrina E.S. (2012) Managerial Approach to the Intellectual Capital Management in the Industrial Complex./ Economic Science Journal, 2012, No.2 The Accounts Chamber Urged the Ministry of Education and Science to Take the Budget‐Financed Intellectual Property Under the State Control [online] http://www.rbsys.ru/ Van den Berg, H.A. (2002) “Models of Intellectual capital Valuation: A Comparative Evaluation”, [online],http://business.queensu.ca/knowledge/consortium2002/ModelsofICValuation.pdf

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Intellectual Capital Practices of SMEs and MNCs: A Knowledge Management Perspective Mariza Tsakalerou and Rongbin Lee Knowledge Management and Innovation Research Center, Hong Kong Polytechnic University, Hong Kong mariza.tsakalerou@connect.polyu.hk WB.Lee@inet.polyu.edu.hk Abstract: Intellectual capital (IC) –from intellectual property and patents through staff technical skills to relationships and networking with customers– has been identified as a critical business success factor and as a source of competitive advantage. In fact, the key conjecture in IC research is that IC is more likely to be the key source of a firm’s competitive advantage than tangible resources. Yet the empirical evidence on the causal relationship between IC and organizational value has provided mixed results. This is due to the fact that IC is a complex phenomenon of interactions, transformations and complementarities and thus IC measurements are difficult and often vague. Divergent, even suspect, standards of recording IC practice worldwide and forced generalizations across business sectors and regions at varied stages of development do exasperate the problem. This paper is based on the premise that IC praxis follows varied patterns exhibiting distinct characteristics across industry sectors, company types and geographical locations. Knowledge management and IC practices of small and medium enterprises (SMEs) and multinational corporations (MNCs) are compared and contrasted to examine this premise. The thesis of this paper is that IC praxis will have to be customized for individual companies based on their particular traits and that the appropriate term for such an approach is mass customization of IC recommendations. Keywords: intellectual capital, knowledge management, SMEs, MNCs

1. Introduction In an era of globalization, many markets have become increasingly international and competitive. The mobility of resources (such as capital and labor) and the information flows of a wired world create unique challenges and opportunities for enterprises today. Information flows take many forms:

non‐contractual, such as public knowledge, trade fairs, conferences, informal networks; and

contractual, such as transfers within the enterprise, joint ventures, licensing, and franchising.

Yet information flows do not readily lead to information absorption. Absorbing information flows requires a conscious effort by the enterprise and also the environment in which it operates (suppliers, customers, technology support providers, training partners etc.). The rapid move towards the knowledge society is making intangible assets and knowledge the ultimate sources of competitive business edge. Future success will be determined by the ability to manage knowledge, a universal resource that contains technology and intellectual capital. The general consensus is that effective management of intangible (intellectual or knowledge) assets within an enterprise often serves as a source of competitive advantage and hence value creation for the organization (Bonfour & Edvinsson 2008; Lee 2008). National governments, international organizations and professional accounting associations have promoted the development of intellectual capital reporting to assist with company valuations (European Federation of Financial Analysts Societies 2008; Hofmann 2008). Indeed, intellectual capital –from intellectual property and patents through staff technical skills to relationships and networking with customers– has been identified as a critical business success factor (Organization for Economic Cooperation and Development 2008). Intellectual capital (IC) though is a complex phenomenon of interactions, transformations and complementarities. Accordingly, IC research is at the crossroads – attempting to bridge the gap between theory and practice. The current issue of the premier journal in the field, the Journal of Intellectual Capital, reflects upon 21 years of IC theory and practice and provides a critical examination of the third stage of IC research and beyond (Dumay 2013).

STAGE 1 ‐ “raising awareness”

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STAGE 2 ‐ “creation of guidelines and standards”

STAGE 3 ‐ “critical analysis of IC practices”

STAGE 4 ‐ “beyond measuring and accounting”

Considering that Stage 1 was the period of raising awareness and Stage 2 dealt with the creation of guidelines and standards, Stage 3 –the crossroads from theory to practice– is defined by its critical analysis of IC practices. While pioneers of the field, such as Leif Edvinsson, are already treading new ground in Stage 4 and talking about the IC ecosystem (Edvinsson 2013), there is a lot of systematic work still needed in Stage 3 to cement the practical legitimacy of the field (Dumay & Garanina 2013).

2. The IC conjecture The key conjecture in IC research is that IC is more likely to be the key source of a firm’s competitive advantage than tangible resources. The term conjecture, a term borrowed primarily from the field of mathematics, is used here to indicate an assertion that is likely to be true but has not been formally proven. In a philosophical context, a conjecture is a proposition that is thought to be true despite being primarily based on something inconclusive. The practical implications of the IC conjecture, if proven true, will be that leveraging knowledge at the company level is essential to an organization’s prosperity, and that at the state level, there should be a shifting of emphasis from commodities and manufacturing towards a service and intangible‐based economy. While both implication of the conjecture can have paradigm‐shifting effects, this Holy Grail of the IC field has remained elusive. Indeed, over 4000 research articles in the literature investigating the empirical relationship between IC and financial performance did not yield a coherent picture. There are of course possible explanations for this failure. Prominent among these are:

Lingering issues of IC definition, reporting and accounting.

Forced generalizations across business sectors and countries at varied stages of development.

Divergent, even suspect, standards of recording IC practice worldwide.

Fortunately, there is a clearer picture coming from advanced economies where IC reporting is mandatory, consistent and reliable. Hassett and Shapiro in the latest version of their recurring report for the American economy, estimate the total USA intellectual capital at 7.7 trillion USD, up 28% from about 6 trillion in 2005 (Hassett & Shapiro 2009). Their methodology looks at both the intellectual capital reported by publicly held companies on their balance sheets and their off‐balance sheet intellectual capital, using data from the Bureau of Economic Analysis (BEA). More importantly, their report shows that intellectual capital now pervades most industries, from traditional manufacturing to the most advanced goods and services. The following tables adapted from (Hofmann 2008) indicate the

top 5 most IC intensive industry sectors in terms of percentage share of their market value, and the

top 5 industry sectors with the largest stocks of the American IC, out of a total of 24 types of industries that cover the American economy based on the Global Industry Classification standard.

Table 1: Most IC intensive industry sectors in USA #

TOP 5 USA SECTORS

IC as a Share of Market Value

Media

75%

Telecommunication Services

72%

Automobiles & Components

62%

Household & Personal Products

61%

Food, Beverage & Tobacco

58%

24 SECTOR AVERAGE:

44%

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Mariza Tsakalerou and Rongbin Lee Table 2: Industry sectors with the largest stock of American IC #

TOP 5 USA SECTORS

IC as a Share of Total Country IC

Energy

10%

Software & Services

10%

Insurance & Other Finance

10%

Capital Goods

08%

Pharma, Biotech & Life Sciences

07%

TOTAL COUNTRY IC:

$ 7.7 trillion

Comparing the two tables reveals that three sectors combine size and intensity with regard to IC. Software and services; pharmaceuticals, biotech and life sciences; and healthcare equipment and services are the only industries in the top ten in terms of both the value of their intellectual capital and the portion of their market value that intellectual capital represents. It is also interesting to observe the importance of IC in a traditional manufacturing sector such as automobiles and components. Within this context, it is hard to reconcile the incoherent picture of the research literature and the quality of the data for an advanced economy like the American one. The only logical conclusion is that:

IC has distinctly different characteristics across industry sectors.

IC profiles within industry sectors vary between small and medium enterprises (SMEs) and multinational corporations (MNCs).

IC issues and practices may vary very widely across different countries.

In other words, IC praxis follows varied patterns, which is a major thesis of this paper. Our hypothesis is that IC praxis will have to be customized for individual companies based on their particular traits and that the appropriate term for our approach is mass customization of IC recommendations. In the sequence we examine our hypothesis across one of the three main dichotomies, namely SMEs vs. MNCs, and compare and contrast their respective knowledge management and IC practices.

3. IC and multinational corporations Multinational corporations (MNCs) are firms that control production across national boundaries through intra‐ firm (non‐market) operations. The importance of MNCs is growing as they collectively control about 2/3 of world trade and their role is particularly large in high‐tech manufacturing. MNCs dominate technology flows in all forms. The newest and most valuable technology is internalized, while others are licensed. Large firms perform most R&D worldwide and most innovative firms are globalized. Innovation is highly concentrated with a very fine vertical specialization by function/component between countries The role of internal technology flows within the global production networks of MNCs is constantly growing. Large MNCs are unsurprisingly giving increased level of attention towards knowledge management (KM). The literature review on KM reveals that intensive research in the field is mostly focused on large firms. KM systems in MNCs seek to accumulate intellectual capital that will create unique core competencies and lead to superior results. Knowledge is central to innovation for MNCs, both in terms of knowledge stock and knowledge flows. MNCs efforts on KM focus on attributes of organizations and individuals that enable the enterprise to innovate, to adapt to new situations, and to take advantage of new opportunities. The following figure captures the strategic KM goals of the MNCs.

4. IC and small to medium enterprises Small and medium‐sized enterprises (SMEs) are companies whose personnel numbers fall below certain limits. SMEs face tough challenges in the global economy and need KM just as much as MNCs. Yet KM efforts in SMEs

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Mariza Tsakalerou and Rongbin Lee are significantly lagging. SMEs are technologically weak, invest little in innovation and development, and only a few are in high tech (e.g. Singapore’s lack of high‐tech SMEs). SMEs have simpler communication and information requirements than MNCs and typically adopt less‐formal strategies in KM. Admittedly though, innovation may not be critical for success for SMEs and a “fast follower” strategy may be preferable. Figure 1: Knowledge management goals of MNCs In fact, to the question whether SMEs do KM or simply manage what they know, Hutchinson and Quintas (Hutchinson & Quintas 2008) observe pointedly “… small firms do indeed manage knowledge informally as part of their normal activities, without the use of the terminology and concepts of KM. However, contrary to expectations, on occasion some also engage in formal KM.” Desouza and Awazu (Desouza & Awazu 2006) identify five unique peculiarities of KM at SMEs:

Socialization is the predominant way through which knowledge transfer occurs from owner to employees and between employees in SMEs.

Common knowledge between employees, in terms of both depth and breadth, leads to a shared context for interpretation and communication.

Knowledge loss is not a real problem, with deliberate mechanisms in place to prevent it from becoming one.

SMEs are resource constrained, and thus have a knack for exploiting external sources of knowledge.

SMEs knowingly or unknowingly, manage knowledge the right way – via people based mechanisms while technology is seldom part of the knowledge management equation.

In a more organized context, KM strategies for a SME evolve along two axes:

Partnership with a MNC within its value‐chain.

Partnership within a network of similar SMEs (business cluster).

For the first type of partnership, and given the asymmetry of resources and long‐term objectives, SMEs are at a disadvantage. The critical factors of success in such an endeavor is to know their main value proposition, to adopt effective methods for connecting with MNCs and to understand the challenges of partnering with MNCs (Conference Board of Canada & Business Development Bank of Canada 2009).

5. Research hypotheses and methodology Our research aims to examine the validity of the following main research hypothesis: H.1.

IC profiles of praxis differ between SMEs and MNCs.

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Mariza Tsakalerou and Rongbin Lee In order to certify this hypothesis we have to account for the variables of industry sector (service vs. manufacturing), company size (big vs. small) and geographic location (developed vs. developing countries). Due to the enormous spectrum spanned by these variables, we propose a schema where the effect of each variable is examined in turn while the other two remain constant. In this schema, three sub‐hypotheses emerge: H1.1. IC profiles of praxis differ between service and manufacturing companies of approximately the same size and operating in the same type of economy. H1.2. IC profiles of praxis differ between big and small companies of the same industry sector and operating in the same type of economy. H1.3. IC profiles of praxis differ between geographical locations for companies of the same industry sector and of similar size. The proposed methodology is based on an exhaustive literature search to identify relevant empirical quality studies, classify the results across the manufacturing/services, big/small and developed/developing economies axes and the design of a statistically valid meta‐study. Identification of initial benchmarking standards from the results of previous studies will determine the appropriate quantification of the variables defined above.

6. In conclusion Governments still view SMEs as the growth engines of the new knowledge‐based economy, primarily because of their effect on local employment. SMEs and MNEs are different enterprises that want to succeed while existing in different habitats and behaving in diverse ways. SMEs are not small MNEs and many SMEs do not want to be MNEs. KM is a critical capability for SMEs to master because it helps them leverage their most critical resource, knowledge, to overcome their deficiencies in traditional resources, like land, labor, and capital. Effective KM for SMEs requires collaboration, commonly recognized now as the new engine for knowledge creation and innovation. SMEs with a clear KM strategy collaborate better internally and participate effectively in knowledge networks with customers, partners, communities of practice and business clusters. Collaboration sounds simple but requires a work culture that needs to be actively developed. SMEs and MNEs require distinctly different information flows, absorption patterns and KM strategies to stay competitive and profitable. The proposed meta‐study will demonstrate conclusively that viewing KM practices of SMEs as scaled down versions of the ones found in larger organizations is patently incorrect.

References Bonfour, A. and Edvinsson, L. (2005) Intellectual Capital for Communities – Nations, Regions, and Cities. Butterworth ‐ Heinemann, Oxford. Conference Board of Canada and Business Development Bank of Canada (2009) Big Gains With Small Partners / Small Companies Big Connection. Special reports, AERIC, Ottawa. Desouza, K.C. and Awazu, Y. (2006) “Knowledge Management at SMEs: Five Unique Peculiarities”, Journal of Knowledge Management, Vol. 10, No. 1, pp. 32‐43. Dumay, J. (2013) “The Third Stage of IC: Towards a New IC Future and Beyond”, Journal of Intellectual Capital, Vol. 14, No. 1, pp. 5‐9. Dumay, J. and Garanina, T. (2013) “Intellectual Capital Research: a Critical Examination of the Third Stage”, Journal of Intellectual Capital, Vol. 14, No. 1, pp. 10‐25. Edvinsson, L. (2013) “IC21: Reflections from 21 years of IC Practice and Theory”, Journal of Intellectual Capital, Vol. 14, No. 1, pp. 163‐172. European Federation of Financial Analysts Societies (2008) Principles for Effective Communication of Intellectual Capital, [online], EFFAS, www.effas.com. Hassett, K.A. and Shapiro, R.J. (2011) What Ideas are Worth: The Value of IC and Intangible Assets in The American Economy. Special report, Sonecon, Washington DC. Hofmann, J. (2008) Disclose Intangibles ‐ Effectively! Talking point. Deutsche Bank Research, Frankfurt. Hutchinson, V. and Quintas, P. (2008) “Do SMEs do Knowledge Management? Or Simply Manage what they Know?”, International Small Business Journal, Vol. 26, No. 2, pp. 131‐154. Lee, W.B. (2008) “On The Relationship between Innovation, Intellectual Capital and Organizational Unlearning”. Essays in Honor of Professor Karl‐Erik Sveiby on his 60th Birthday, Swedish School of Economics and Business Administration (Hanken), Helsinki, p. 120. Organization for Economic Cooperation and Development (2008) Intellectual Assets and Value Creation ‐ Synthesis Report. OECD Directorate for Science, Technology and Industry, Paris.

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Managing Knowledge and Overcoming Resistance to Change: A Case Study at Firat University Nurhayat Varol1 and Serkan Varol2 1 Firat University, Elazig, Turkey 2 Lamar University, Beaumont, Texas, USA Nurhayat_varol@yahoo.com Serkanvarol23@gmail.com Abstract: The purpose of this paper is to examine the relationship between knowledge and overcoming resistance to change in class projects at Firat University in Turkey. Resistance to change among departments is a major issue for individuals at organizations. It may cause stress and frustration at work environments. Even, the change can extend to layoffs which eventually destroy the overall work atmosphere within departments. Employees often set in their ways, and don’t want to modify their routine. However, organizations need to change to continue delivering the right products and services on time and effectively due to emerging demands in industry. The major reason behind resistance to change is strongly correlated to knowledge management aspects. The lack of individual’s knowledge in another field may fear him or her losing the job if an interchange occurs among departments. Because the employee is assigned to adopt a designated duty, it is unlikely to appoint the individual in another field that he or she is unfamiliar with. Thus, the learning curve for an existing experienced operator is much longer than a new employee. Besides organizations, the assignment changes in classroom project tasks may also create similar atmosphere among students. However, the causes of this hypothesis are firmly depending upon managing motivation and knowledge factors. Therefore, in this research, a matrix chart will be presented that evaluates students’ performances at different in class projects at Firat University, which interprets how managing knowledge leverages the issues of resistance to change in order to increase productivity and decrease potential conflicts among students. Keywords: learning curve, managing knowledge, motivation, organization, resistance to change

1. Introduction Resistance to change among departments is a major issue for individuals at organizations. It may cause stress and frustration at work environments. Even, the change can extend to layoffs which eventually destroy the overall work atmosphere within departments. Majority of employees set in their ways, and often don’t want to modify their routine. However, organizations need to change to continue delivering the right products and services on time and effectively due to emerging aspects in industry. Most companies seek for the latest technological devices and methods in order to reduce the corruption in tacit and explicit knowledge. One of the major tools of tacit knowledge is to transfer complex information from one unit to another one for sharing and distributing purposes. The best communication among departments can be performed via connecting knowledge pieces from intergroup in organizations (Stewart & Roth, 2011). Transferring tacit knowledge is not always satisfactory, due to fundamental characteristics of it. For example: Culture is an obstacle in terms of understanding and implementing the information. Different cultural aspects may bring different methods on the table. Culture concept varies among departments in organizations. Each department has different work ethics and methods. Therefore, a crisis may arise when an interchange occurs. Another major barrier is the load and quality of knowledge. Depending upon the load of training, individuals may confront with knowledge pollution. Too much information in a short time can reduce the performance of the employee and causes uncertainties. The major reason behind resistance to change is strongly correlated to knowledge management aspects. Because tacit knowledge is hard to transfer, individual may fear losing the job if an interchange occurs among departments. In this paper, resistance to change among departments is evaluated and exampled with a case study, Harwood Management Cooperation, for the purpose of conducting how managing knowledge and other factors impact on resistance to change and may offer better work environment. The research is also supported by an in class project conducted at Firat University in Turkey.

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Nurhayat Varol and Serkan Varol

2. A case study related to resistance to change The case study is about the main plant of Harwood Management Cooperation which is located in Marion Virginia. The factory produced pajamas with 500 women and 100 men employees. The company is in debt and not profitable over years. On the other hand, there was always a positive relationship between employees and management. They are free to talk about any problems that they must confront. The plant was one of the friendliest places that people could ever want to work at. The employees were using any amenities including health services, music and recreation programs for free of charge. Foreman was given a lot of incentives to find problems in human relations by utilizing form of using conferences and role playing methods. Regular orientations were prepared to overcome any frustration or conflict in the organization (Natemeyer & Hersey, 2011). Partial work incentive system was used at Harwood Manufacturing Cooperation. In other words, the weekly average efficiency ratings have a direct impact on the amount of pay received. Everybody in the plant knew the most successful worker for that specific month. The ratings were calculated daily and published to all operators. If an employee is more efficient than others, a bonus incentive was provided to appreciate his/her hard work. It is such a pleasure for an employee to see his/her name on the “employee of the month” board. This truly increases his/her work performance to the peak. This also enhances the employee to another department in the same organization. This is supposed to be an award for the worker, but it turns out that majority of the employees did not want to shift to another position because they were worried about their performance on the new position. Therefore, the piecemeal incentive method could be a major risk for them (Natemeyer & Hersey, 2011). Harwood Management Corporation needed a change to adopt to the emerging technology and more importantly to fixing their financial situation. In order to achieve this all, the company wished to offer a new style of pajamas along with new piece rates. To perform this, the atmosphere of the work environment was investigated and findings are addressed and discussed with departments. All incentives and rights for employees were screened and feedback was given to the management. Inspection groups were prepared to simulate the work environment. In parallel with these activities, the learning curves for successful and unsuccessful transferred workers were examined. Thus, a control group and experimental group were assigned to the job oppositely. According to results, in the control group, the productivity did not change a lot and remained the same, but during the first 40 days, 17 percent quit and there was considerable distrust of management. Things were much better in the experimental groups where productivity increased as expected. Eventually, not a single employee quit during the first 40 days and they appeared to keep their confidence on management 100 percent (Natemeyer & Hersey, 2011). For those who recover to standard production at Harwood Organization, the average learning curve was about 8 weeks for on the simplest type job. The surprising part was that the experiment was for the new learners. It turned out that the learning curve for an existing experienced operator was much longer than a new employee (Natemeyer & Hersey, 2011).

3. Cure for resistance to change The case study is very informative and the fact that some people out there not willing to modify their routine is evident. Eventually, we all have to change because the world around us is changing, altering the conditions of our existence. Uncertainties and misunderstanding can only vanish by the positive atmosphere at the plant (De Jager, 2001). Understanding organizational and individual resistance to change problems can help in perceiving ways to reduce resistance and encourage compliance with change. Resistance may also occur if employees do not understand the purpose of the change or distrust the intentions behind it. As a manager, you need be clear about your message, employees need to understand your instructions and how you want the job done. Change is not always for the worse if you know how to turn this into an advantage (Davidson, 2002). It is also crucial to define resistance and implement strategies to reduce the load of stress in organizations. As organizational change brings about a disruption in the equilibrium of a group, stress is caused by the resultant change in balance of the individual. A common method in which people cope with stressful events is through the use of defense mechanisms along with denial (Curtis & White, 2002).

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Nurhayat Varol and Serkan Varol Developing trust is another strategy to stand against resistance to change, and confrontation is the only element of trust. Face‐to‐face meetings either individually or in a group, to confront the issues causing resistance allow everyone to communicate their feelings or to work collectively to resolve the issues identified. This building of a trusting relationship ensures that the methods used for dealing with resistance are transparent (Curtis & White, 2002).

4. The role of knowledge in resistance to change at organizations Managing knowledge in an efficient way can also be an effective power on resistance to change. It is an assumption that cannot be proved without finding the cause of demoralization and decrease in performance in organizations when an interchange occurs among departments (Conroy & Pincus, 2011). Therefore, all these doubts encouraged research teams to investigate the role of knowledge management in resistance to change at organizations (Bruckman, 2008). A class of 38 was taught robotic fundamentals as a part of their degree planning. The class was broken down into 2 main projects. The first project was about building a car wash facility with Legos that is controlled by special engineering software (number of participants is 20). The second assignment was to build a robot model that paints a piece of metal in different colors when needed (number of participants is 18). In our case study, we particularly focused on the students who had assigned to build a Lego car wash facility. All these students were lectured together in the first three class periods, then they were divided in into two separate teams (Group A and B) and each was assigned a case study based on their abilities and preferences. The scope and budget of the project were initialized by the instructor. Group A was the labor group which structures and executes a variety of duties including manufacturing robots and sustaining the same quality for the rest of production also aiming to preserve value with less work. Group B consisted of students acted like engineers whose responsibilities were not limited to calculating the material loads and building & drawing the whole structure of the robot, but also managing and utilizing all provided sources. After three class periods, the groups were trained separately due to different characteristics held in classes. Group A’s training lasted about 6 class periods. They were loaded intensively with fundamental and advanced management and engineering skills in which they later used in class project. Group B’s training session took also 6 class periods. They were specially trained and instructed to work under given criteria and requirements by the instructor. Two groups were met every 3 class periods to exchange information and keep themselves on track for the project. In group B, the crucial part of the project was to select the best four performers depending upon the votes among group members. As a reward, the members were shifted to Group A and offered better work atmosphere, because of their engineering background, such as taking extra time off and dealing with less stressed conditions. The only requirement was to attend to mandatory bridge course that took two class periods, but there was no failure at the end. The promoted students were assigned to manufacturing robot models. The learning curve of such a job was pretty high as there was nothing special skills required. The most expected outcome was 4 units in each class period, so 16 units in every 4 class periods. th Four (4) students are promoted from Group B at the end of the 8 class period. Meanwhile, Group A hired 4 other students from the painting project. They are called as “New Hires”. The instructor measured the students’ performances based on peer evaluation, personal observation and productivity results with existing students. As shown in Table 1, the learning ratings for the promoted were fairly low compared to “New Hires” and “Existing Workers”. The student who promoted from Group B to A (transfer students) felt pressure and mostly frustrations, whereas new hires outperformed transfers and scored more number of built robots within four class periods. Existing workers felt no pressure and frustrations and their success level on assembling robots was close to perfect.

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Nurhayat Varol and Serkan Varol Table 1: Feelings about position changes

Promoted students struggled adapting the concept of their new positions. They often taught that quick decision making was better than having no decisions at all. Thus, transferred students misconstrued the requirements of their duties and made such unfavorable mistake. They also found themselves in a comparison with existing workers in terms of the number of models built in a certain time period which led them to believe they are incapable of reaching the desired productivity level. Eventually, all other students that were outside of the transformation process neglected the change and wished no promotions until the end of their project. In other words, they sacrificed their holiday time and all other extra benefits that group a position could offer. The scenario was more optimistic for “New Hires”. In spite of the fact that they started to attend the mandatory bridge lectures and assembling robots at the same time, fewer problems were reported and general work attitude was found to be better. Most of them felt pressure for proving their worth to the instructor, since there was only one person who was frustrated and panicked. Their productivity skills were more preferable which reflected into the number of models that they built.

5. Conclusions and future work Involvement of knowledge management principles into our case study appears to be intensively high. Identical trainings and lectures, which represent typical tacit knowledge objectives, are given to all groups within the same period of time. We neglected the quality of incoming students to our department as they had to take the placement exam to join our group. This clearly indicates that the educational background level of the students is relatively similar. As discussed before, the transformation of tacit knowledge into explicit knowledge is an uneasy operation that may not always result in favorable to the individual. Therefore, the major weakness of interchanging between departments was that the students had no control over their knowledge after they transferred. They could not apply what they had learned in previous positions at the new position.Thus, switching between various tacit knowledge models under the same organization was a frustrating and time consuming event.

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Nurhayat Varol and Serkan Varol The transferred students were asked to identify four frequent problems that they confronted during the transformation period as shown in Table II. Initially, they informed that they had difficulties with the complexity of given trainings and lectures, which is highly correlated to an important tacit knowledge principle; Organizations are in need of converting tacit information into appropriate forms usable by all employees. The course must address all sort of incoming employees, not only for advanced level candidates. Conflicting what they have learned in the past was another issue that can be tied into the main principle of tacit knowledge which is hard to be transferred into. To sum up, as it can be understood from the chart below, 50% of the problems that are relevant with resistance to change are directly related to knowledge facts Table 2: Findings

DNA of tacit knowledge is restricted and can only be altered to extent of a point where there is no benefit unless the individual uses the change in a right way. We may increase our productivity level by adjusting the level of the course so that it can address any students who would like to start from scratch. Lectures and trainings can be put in a form where tacit knowledge interacts and enhance the chance of success for outcome.

References Bruckman, J.C. (2008). Overcoming resistance to change: Causal factors, interventions, and critical values. The Physiologist‐ Manager Journal, 11, 211‐219. doi: 10.1080/108871 50802371708 Curtis, E., & White, P. (2002). Resistance to change. Nursing Management ‐ UK, 8(10), 15‐20, Retrieved April 10, 2013, from http://emmerson.csc.wilkes.edu:2060/ehost/detail?vid=5&hid=122&sid=6f26c685‐d6d8‐406d‐afe3‐ 37717c2562e9%40session Conroy, D.E, & Pincus, A.L. (2011). Interpersonal impact messages associated with different forms of achievement motivation. Journal of Personality, 79(4), 675‐706. doi:10.1111/j.1467‐6494 .2011.00693.x Davidson, J. (2002). Overcoming resistance to change, Public Management (00333611), 84(11), 20, Retrieved March 10, 2013, from http://emmerson.csc.wilkes.edu:2060/ehost /detail?vid=7&hid=122&sid=6f26c685‐d6d8‐406d‐ 737717c2562e9%40sess ionmgr113&bdata=JnNpdGU9ZWhvc3QtbGl2ZQ%3d%3d#db=bsh&AN=8690586 De Jager, P. (2001). Resistance to change: A new view of an old problem. Futurist, 35, 24. Retrieved April 11, 2013, from http://emmerson.csc.wilkes.edu:2060/ehost/detail?vid=21&hid=8&sid=339e65b4‐93b0‐49fa‐875b‐ 33df33df4ae43b2d%40 sessionmgr11&bdata=JnNpdGU9ZWhvc3QtbGl2ZQ%3d%3d#db=aph&AN=4339215 Natemeyer, W.E., & Hersey P. (2011). Classics of organizational behavior (4th ed.), Long Grove, IL: Waveland Press, Inc Stewart, W.H., & Roth, P.L. (2007). A meta‐analysis of achievement motivation differences between entrepreneurs and managers. Journal of Small Business Management, 45(4), 401‐421. doi:10.11 11/j.1540‐627X.2007.00220.x

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Organizational Learning Rate Dependence on National Wealth: Case Study of Business Schools Karen Voolaid and Üllas Ehrlich Tallinn School of Economics and Business Administration at Tallinn University of Technology, Akadeemia tee 3, 12618 Tallinn, Estonia karen.voolaid@ttu.ee ullas.ehrlich@ttu.ee Abstract: Globalization presents new challenges to business schools: all of them are rivals in the global market. In order to look for better opportunities to survive in growing competition it is needed to be a learning organization which helps to increase the competitive advantage of the business school. Business schools worldwide are facing unequal situations economically, since their operating environments vary largely in terms of economic welfare. The research seeks to clear up whether the prosperity of the business school’s location country provides an advantage to the school, which is indirectly revealed in a higher organizational learning rate or whether a smaller GDP per capita in the location country is an obstacle to the school’s development. The paper investigates the dependence of business schools’ (BS) organizational learning rate (LR) on GDP per capita of the school’s country of location. The authors used an instrument invented by them for measuring the BS learning rate. The structure of the instrument is based on Watkins and Marsick’s learning organization questionnaire, but it takes into consideration the specific features of BS. The measuring instrument consists of three levels, which in turn are divided into 7 dimensions and 45 characteristics addressing all learning aspects of BS as organizations. They measured the organizational learning rates of 105 BSs from 44 countries. To identify the dependence between the BS LR and the school’s location country they conducted a regression analysis for average LR as well as separately for individual components of learning (levels, dimensions, characteristics). The results of the analysis reveal that the dependence of BS’ average LR on GDP per capita of the school’s location country is not statistically significant. However, they identified a negative correlation between the rates of individual BS learning components and GDP per capita of the school’s location country. Additionally they investigated dependence of the possession of international accreditations awarded to BSs that show organizational quality (EQUIS, AACSB) on GDP per capita of the school’s location country. Here the analysis shows a strong positive correlation. Keywords: business school, organizational learning, business school learning rate measurement instrument, business school learning rate dependence on GDP

1. Introduction High‐quality economic and business education is one of the most important factors of economic competitiveness and sustainability. Economic and business education must satisfy the competitiveness of graduates in the labor market, satisfy employers’ demands and provide graduates with a potential for life‐long learning. All this shows the magnitude and scope of expectations and responsibility the business schools have to face ahead and which a business school will be capable of responding to only when it constantly improves and upgrades its organization true organizational learning. Organizational learning means learning related activity and the process by which organizations eventually reach the ideal of a learning organization (Finger and Brand 1999). The learning organization is defined as a form of organization, and the learning organization literature has an action orientation that is geared toward using specific diagnostic tools which can help to identify, promote and evaluate the quality of learning processes (Easterby‐Smith et al. 1999). Globalization presents new challenges to business schools: all of them are rivals in the global market. In order to look for better opportunities to survive in growing competition it is needed to be a learning organization which helps to increase the competitive advantage of the business school. According to Lorange (1997), organizational learning, in fact, can be expected to be a key driver for any leading business school which wants to advance and to respond to the challenges of its customers. Several authors have suggested that in the increasingly sharpening international competition business schools should also be learning organizations (Dill, 1999; Kristensen, 1999; Mulford 2000). The learning rate of business schools as organizations has been studied insufficiently so far. The authors have worked out a special instrument for measuring the learning rate of business schools as organizations (Voolaid

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Karen Voolaid and Üllas Ehrlich and Ehrlich, 2011). Using this instrument the authors have carried out a worldwide empirical study of 105 business schools in 44 countries on all continents. As a result, the authors have measured the organizational learning ability of more than hundred of business schools worldwide, which has enabled to map the learning rate of these business schools as organizations, analyze it across levels and dimensions, identify the learning‐related bottlenecks and propose possible ways of removing them. The paper has also identified the dependence of the organizational learning rate on business school’s geographical location and economic welfare of the school’s country of location, expressed in the gross domestic product per capita. Business schools worldwide are facing unequal situations economically, since their operating environments vary largely in terms of economic welfare. The research seeks to clear up whether the prosperity of the business school’s location country provides an automatic advantage to the school, which is indirectly revealed in a higher organizational learning rate or whether a smaller GDP per capita in the location country is an obstacle to the school’s development. To verify the correlation between business schools’ learning rate and GDP per capita in the country of location the authors investigated, using the measuring instrument worked out by them, 105 business schools from 40 countries with largely varying GDP. Using regression analysis they identified the dependence of schools’ organizational learning rate on GDP as well as of its individual components (levels/dimensions/characteristics). Considering that one of the main organizational quality indicators of BSs is the possession of international accreditations (AACSB, EQUIS), the authors also investigated the dependence of accreditations on the GDP of the location country.

2. Organizational learning as business school success factor in global competition Today there are more than 12,000 known business schools across the globe, and the number is increasing every year. This is due to an increasing demand from the global student population, people at work who wish to advance their careers, and of course the employers of graduates in business and management (Yazdani, 2012). The current financial crisis and the eroding of corporate reputations have given rise to strong criticism of business schools and their role in those events. For these reasons, business schools will have to change if they want to keep having positive impact on people, firms and societies (Canals, 2010). These criticisms fall into two categories. The first is related to factors external to business schools, mainly the financial crisis, globalization, and the notion of the firm and its reputation. Together with the current economic crisis, globalization and its impact on the business world is another area of concern. Many Western companies have failed in their efforts to become more efficient in their international operations. In this area, business schools have not done a good job in making clear and explicit the specific demands of globalization, cross‐cultural management and the variety of dimensions, experiences, and human and cultural values to be taken into account. As institutions educating managers and business leaders, business schools have to rethink the role of companies in society, the job of business leaders and how to include these dimensions in their programmes. Unfortunately, the challenges for business schools do not come only from outside world. There are some areas with major deficits at many business schools: mission, governance, humanistic approach, financial and relevance. Some business schools do not have a clear sense of mission of the role they want to play in society (Canals, 2010). It is clear that all of them want to help educate people and develop new knowledge. The question is what balance between those activities business schools want to have. There is no single answer but it is nevertheless important to understand why a business school exists and what it wants to do. Each school has its own view but it is good to make them explicit and connect them with its strategy, faculty development, programme design and research initiatives. Business schools are influential institutions. As such, their governance matters. Unfortunately, academic institutions in general have a poor track record in this area. Good governance requires a stronger faculty commitment to the long‐term development of their schools. Good governance needs to give faculty an appropriate role in business schools, one that neither blocks change nor makes faculty members alienated from the management of the school (Canals, 2010). Dealing with organizational development challenges inside the institution, handling the expectations from the external world while allocating scarce resources and enabling the validity of business school activities requires true leadership (Sattelberger, 2011). According to Sattelberger, the role of deans in their institutions will become more important and more complex. More

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Karen Voolaid and Üllas Ehrlich diversity is a plus at all levels in business schools: in the board, in management, faculty and staff, and in the student body; diversity also in a geographical, religious and gender sense. In order to cope with the new challenges and changes the business schools need to be ready to learn, so as to change through learning. The learning rate of business schools as organizations and business school learning rate dependence on external and internal factors has been studied insufficiently so far. In the current paper the authors have examined business school learning rate dependence on GDP per capita of business shool’s country of location.

3. Methodology The authors have elaborated a special business school learning rate measurement instrument and have measured the learning rate of 105 business schools in 44 countries worldwide (Voolaid and Ehrlich, 2012). They have used the Watkins and Marsick questionnaire for measuring the learning rate of business schools in their previous research (Voolaid and Ehrlich, 2010) where they perceived that this questionnaire did not take into consideration the specific features of business schools and therefore they worked out a special business school learning rate measurement instrument. The authors replaced the characteristics in Watkins and Marsick’s questionnaire on the basis of the survey conducted at TSEBA, at the same time not changing significantly the structure and hierarchic setup of Watkins and Marsick’s questionnaire, since their objective was not to make a completely new questionnaire but to adjust Watkins’ and Marsick’s questionnaire to make it more suitable for measuring the university as a learning organization. A criterion the replacement was based on is the median score of characteristics. The characteristics which received this amount or more points were not replaced and their formulation was not changed significantly. In order to detect new university specific characteristics for the questionnaire to measure universities’ learning rate, all seven dimensions of Watkins and Marsick’s DLOQ were analyzed separately and discussed regarding their importance in the business school context. As a result, the authors give new specific characteristics for measuring business schools´ learning rate. With this instrument the authors have measured the learning rate of 105 business schools in 44 countries worldwide. A database of 700 business schools worldwide was compiled using for that the data of EFMD, the organization uniting business schools worldwide, and the worldwide business schools ranking organization EDUNIVERSAL catalogue (Eduniversal, 2010). The questionnaire which included 45 questions was sent to 700 business schools. Finally 105 business schools worldwide participated in the survey, which is 15% of the questionnaires sent out. Responses were received from business schools from all continents (except Africa), from 44 states. The survey was conducted from 1 February to 30 April 2012.

4. Results 4.1 Business schools’ learning rate dependence on GDP per capita of the BS location country In Table 1 the BS location countries are listed in order of average learning rate of the schools in that country. In addition to the average learning rates the table describes the level averages. A glimpse to the GDP in the school’s location countries (Table 1) implies that there is no direct relationship between GDP per capita of the BS’s location country and the learning rate of that BS. Statistically this impression is supported by the results of a regression analysis provided in Table 2, which do not confirm a statistically significant correlation between the BS’s learning rate (LR) and the location country’s GDP per capita (prob. 0.1114). The average BS learning rate across countries ranges from 5.7 (Malaysia) to 2.7 (Saudi Arabia, Table 1). The average learning rate of all BSs is 4.6. Among the location countries of BSs with ten largest average LR are countries with a quite modest GDP per capita (Georgia, 3,000 USD), countries with an average GDP (e.g. Croatia, 14,000 USD), as well as countries with a large GDP (Denmark, 63,000 USD). Exactly the same is the situation with location countries of BSs with lower LR (Table 1), with Latvia (GDP per capita 12,000 USD), Australia (67,000 USD) and Pakistan (1,200 USD) side by side. This indicates that the GDP per capita of the country of BS location and average LR of BS are not correlated. Looking at the ranking of BS by average LR it is difficult to find any other logic either; the table contains side by side (i.e. with similar average LR) countries from extremely different world regions. The location countries of

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Karen Voolaid and Üllas Ehrlich BSs with ten highest average LR include 6 East‐European (Bulgaria, Georgia, Slovenia, Croatia, Lithuania, Poland), 2 West‐European (Denmark, Italy) and 2 South‐East Asian countries (Malaysia, Taiwan). Among the location countries of BS with ten lowest average LR are 2 East‐European (Ukraine, Latvia), 2 West‐European (Norway, Ireland) and 4 Asian countries (Indonesia, Singapore, Pakistan, Saudi Arabia). Additionally among the last ten are also Australia and Canada. A common denominator is missing between the BS location countries with both highest and lowest LR, from both geographical, cultural and economic aspect. On the basis of data gathered by the authors it is possible to draw a conclusion that average LR of BSs is directly correlated neither to the wealth of the location country measured with GDP per capita nor to the geographical and cultural affiliation of the school’s location country. Table 1: Average learning rate of business schools by countries COUNTRY

No of schools

GDP per capita, USD

Malaysia

Bulgaria Georgia

Average Learning Rate Total

Individual level

Team or group level

Organiza‐ tional level

8,617

5.7

5.5

5.8

7,243

5.6

5.7

5.5

3,098

5.3

5.2

5.3

5.4

Denmark

63,003

5.3

5.4

5.2

5.3

Taiwan

21,592

5.3

5.4

5.3

Slovenia

25,939

5.3

Croatia

14,529

5.2

4.8

5.3

Lithuania

13,190

5.2

5.0

5.4

Italy

37,046

5.1

5.3

5.2

5.1

Poland

13,967

5.1

5.3

5.2

5.0

Czech Republic

20,938

5.0

5.1

4.8

5.1

Spain

33,298

5.0

5.1

5.2

5.0

USA

48,147

5.0

4.7

5.1

China

5,184

5.0

4.7

5.3

Finland

50,090

5.0

5.1

4.9

5.0

Republic of Korea

23,749

5.0

4.9

5.0

Austria

50,504

5.0

4.6

5.0

5.1

Russia

13,236

5.0

4.9

5.0

Brazil

12,917

4.9

5.0

4.9

Germany

44,558

4.8

5.0

4.7

4.8

Lebanon

10,474

4.7

4.8

5.0

4.7

New Zealand

38,227

4.7

4.8

4.5

4.7

Mexico

10,803

4.7

4.8

4.5

4.6

Japan

45,774

4.5

4.8

4.3

4.5

Belgium

48,110

4.5

4.2

4.6

4.7

Colombia

6,980

4.5

4.7

3.8

4.6

Netherlands

51,410

4.5

4.4

4.5

Estonia

16,880

4.4

4.5

4.0

4.5

India

1,527

4.4

4.2

4.5

Thailand

5,281

4.3

3.9

4.3

4.6

39,604

4.3

4.4

4.1

4.3

France

44,401

4.3

4.2

3.8

4.5

Bosnia and Herzegovina

4,715

4.2

3.8

4.2

4.4

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Karen Voolaid and Üllas Ehrlich COUNTRY

No of schools

GDP per capita, USD

Cyprus

Ukraine

Indonesia Norway

Average Learning Rate Total

Individual level

Team or group level

Organiza‐ tional level

31,435

4.2

4.4

4.2

4.1

3,575

4.2

4.4

4.3

4.0

3,469

4.1

3.8

4.5

4.2

96,591

4.1

4.3

3.9

4.1

Canada

51,147

3.9

4.0

3.5

4.0

Singapore

50,714

3.9

4.2

3.8

Latvia

12,226

3.9

3.7

3.9

Australia

66,984

3.8

4.0

3.7

Pakistan

1,164

3.2

2.8

3.3

Ireland

48,517

3.1

3.3

3.0

3.1

Saudi Arabia

19,890

2.7

3.6

1.8

2.5

Source: Authors A more detailed overview of the relationships between the GDP per capita of the BS location countries and schools’ LR was obtained by analyzing individual learning rate components. The results of the analysis are provided in Table 3. The learning BS questionnaire comprises three levels (individual, team or group and organizational), which in turn are divided into seven dimensions and 45 characteristics. Dependence of all levels, dimensions and characteristics on the location country’s GDP per capita is analyzed separately, by building respective regression equations. The results of the analysis imply (Table 2) that from three levels a statistically significant correlation to GDP per capita of the school’s location country is missing only at the individual level (prob. 0.3483). The team or group level (prob.0.0749) as well as organizational level (prob. 0.0786) show a statistically significant correlation to the GDP per capita of the location country. The correlation is negative, i.e. LR of BSs in location countries with lower GDP per capita at these two levels is statistically higher in comparison with BSs in countries with larger GDP per capita. Table 2: Correlation between business schools’ Average Learning Rate and GDP per capita of the country of the business school Dependent Variable: SCHOOLAVER Method: Least Squares Included observations: 105 Variable

Coefficient

Std. Error

t‐Statistic

Prob.

C GDP

4.825039 ‐0.006071

0.146600 0.003781

32.91289 ‐1.605659

0.0000 0.1114

R‐squared Adjusted R‐squared S.E. of regression Sum squared resid Log likelihood F‐statistic Prob(F‐statistic)

0.024419 0.014948 0.779085 62.51829 ‐121.7672 2.578141 0.111411

Mean dependent var S.D. dependent var Akaike info criterion Schwarz criterion Hannan‐Quinn criter. Durbin‐Watson stat

4.623781 0.784974 2.357471 2.408023 2.377956 2.187238

Source: Authors Analysis of the seven dimensions shows (Table 3) that three of them have a statistically significant correlation with the average LR of the dimension and with GDP per capita of the school’s location country: 3rd dimension „promote collaboration and team learning” (prob. 0.0749), 4th dimension „create systems to capture and transform learning” (prob.0.0727) and 7th dimension “provide strategic leadership for learning” (prob.0.0284).

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Karen Voolaid and Üllas Ehrlich 3rd dimension is from the „team or group” level and 4th and 7th dimension from the „organizational” level. The LR levels of all these dimensions are also negatively correlated to GDP per capita of the school’s location country, showing clearly that BSs in countries with smaller GDP per capita are more learning in terms of these dimensions. Analysis of the other dimensions (”provide continuous learning opportunities”, „foster inquiry or dialogue”, „foster movement towards a collective vision”, „connect organization to its external environment”) did not identify any statistically significant correlation. Table 3: Correlation between business schools’ learning rate (by components) and GDP per capita of the country of the business school

Coefficient

Prob.

Individual level

‐0.003469

0.3483

1.dimension: provide continuous learning opportunities

‐0.002818

0.4587

2. dimension: foster inquiry and dialogue

0.001107

0.8153

Team or group level

‐0.007969

0.0749

3. dimension: promote collaboration and team learning

‐0.007969

0.0749

Organizational level

‐0.006935

0.0786

4. dimension: create systems to capture and transform learning

‐0.007965

0.0727

5. dimension: foster movement toward a collective vision

‐0.005007 ‐0.006176

0.2607 0.1448

‐0.008611 ‐0.006071

0.0284 0.1114

6. dimension: connect the organization to its external environment 7. dimension: provide strategic leadership for learning Average LR of all observed business schools

Source: Authors The findings allow drawing a conclusion that although the average LR of BSs has no statistically significant correlation to GDP per capita of the school’s country of location (prob.0.1114), the correlation exists in the above analyzed three levels, and it is negative. In order to identify the correlation the authors had to analyze the relationships of the characteristics of which the related dimensions comprise (Table 3). In the third dimension, the 15th characteristic „in my business school, team/groups focus both on the group th task and on how well the group is working” (prob.0.0246); 17 characteristic „In my BS teaching integrated courses and helping people to think in an integrated way is supported in different ways” (prob.0.0917) and 19th characteristic „In my BS, rewards and performance management strategies are tied to effective teamwork” (prob.0.049) have statistically significant correlations. Hence the BSs in countries with smaller GDP per capita have greater emphasis on teamwork, integrated thinking promoted and rewarded by management. This may be explained by that under the relatively scarce resources (smaller GDP per capita) more attention is being focused on teamwork to compensate for the insufficient resources, which helps to improve schools’ competitiveness. nd In the fourth dimension, 2 characteristics are statistically correlated to the location country’s GDP: 22 “My BS rd enables people to get needed information at any time quickly and easily” (prob.0.0349) and 23 ”In my BS, a mechanism for sharing interdisciplinary knowledge leading to the publication of interdisciplinary research is established” (prob.0.0696). These characteristics imply better and faster access to information and greater weight on the interdisciplinary approach and its support mechanisms in BSs in countries with smaller GDP per capita. This phenomenon requires further research. In the seventh dimension, four characteristics are negatively correlated to GDP per capita of the BS location country: 41st „In my BS leaders share up to date information with employees about competitors, industry trends and organizational directions” (prob.0.0164); 42nd „In my BS leaders mentor and coach those they lead” (prob.0.0538); 43rd „In my BS leaders continually look for opportunities to learn”(prob.0.0527), and 44th „In my BS, leaders ensure that the organizations actions are consistent with its values” (prob.0.0965). These statistically significantly correlated characteristics evidence managerial staff’s greater support to learning, information exchange between leaders and employees, and participation of leaders in teaching the employees. The results allow drawing a conclusion that BSs in countries with smaller GDP per capita have a less formal atmosphere and close contacts between leaders and those they lead, which ensures a higher

462

Karen Voolaid and Üllas Ehrlich organizational learning rate both in the 7th dimension, „provide strategic leadership for learning”, and at all‐ organizational level. Briefly it may be argued that BSs in countries with smaller GDP per capita have from many important aspects higher organizational learning ability.

4.2 Dependence on the possession of accreditations and GDP per capita of the BS location country While the organizational learning rate is assessed by members of the organization themselves, then accreditations are awarded to BSs by respective international organizations among which the most well‐known and respected are EFMD, awarding EQUIS, and AACSB, awarding an accreditation of the same name. The authors of this paper set the task to investigate the correlation between the possession of accreditations and GDP per capita of the school’s country of location. Getting an accreditation as a matter of course presumes a good organizational learning ability so as to make the organization satisfy the requirements set by the accrediting organization. To identify the correlation the authors used the Logit model of regression analysis. The results reveal that possession of accreditations and GDP per capita of the school’s location country are in a strong correlation (prob. 0.0025). This means that BSs in countries with a bigger GDP per capita are much more likely to get authoritative accreditations that are awarded for organizational quality. This confirms that higher organizational LR does not give an advantage to getting accreditations. One possible explanation is that applying for accreditations costs a lot and schools in countries with smaller GDP per capita, notwithstanding from many aspects a relatively better LR, are short of funds for applying for accreditations. Another reason might be that since majority of organizations which award accreditations are from countries with large GDP per capita, they tend to favour organizations which are more similar to their country of location, regarding smaller formality and flexibility of BSs in countries with smaller GDP per capita as a weakness rather. The problem of accreditations presumes further research. Table 4: Correlation between business schools’ accreditations (EQUIS and AACSB) and GDP per capita of the country of the business school Dependent Variable: ACCREDITATIONS Method: ML ‐ Binary Logit (Quadratic hill climbing) Variable

Coefficient

Std. Error

z‐Statistic

Prob.

C GDP

‐1.797260 0.035323

0.474020 0.011666

‐3.791525 3.027841

0.0001 0.0025

McFadden R‐squared S.D. dependent var Akaike info criterion Schwarz criterion Hannan‐Quinn criter. Restr. deviance LR statistic Prob(LR statistic)

0.077653 0.482856 1.245459 1.296011 1.265944 137.4463 10.67307 0.001087

Mean dependent var S.E. of regression Sum squared resid Log likelihood Deviance Restr. log likelihood Avg. log likelihood

0.361905 0.461328 21.92079 ‐63.38662 126.7732 ‐68.72316 ‐0.603682

Obs with Dep=0 Obs with Dep=1

67 38

Total obs

105

Source: Authors

5. Conclusions The authors of this paper measured, using the instrument created by them, the organizational learning rate of 105 BSs. The schools were from 44 countries which are extremely different in terms of national wealth (measured by GDP per capita), geographically and culturally. The paper attempted to identify the correlation between BSs’ LR and GDP per capita of the school’s country of location, as well as correlation between the international accreditations awarded to BSs and GDP per capita of the school’s country of location. A regression analysis was conducted to identify the correlations.

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Karen Voolaid and Üllas Ehrlich The results of the analysis showed that the BSs’ average organizational LRs have no statistically significant correlation to GDP per capita of the school’s country of location. However, individual learning components are dependent on the location country’s GDP. Two levels out of three proved to be correlated to GDP per capita of the location country: „team or group” and „organizational” level. From seven dimensions three were correlated to GDP: 3rd dimension „promote collaboration and team learning” (prob. 0.0749), 4th dimension „create systems to capture and transform learning” (prob. 0.0727) and 7th dimension „provide strategic leadership for learning” (prob. 0.0284). And it is important to point out that the correlation between GDP per capita of the school’s country of location and the 3rd, 4th and 7th dimension of organizational learning is negative, i.e. schools in countries with smaller GDP per capita are more learning in these dimensions. This can be explained by the fact that under the relatively limited resources (smaller GDP per capita) they focus more attention to teamwork and exchange of information in the organization in order to compensate for the insufficient resources. Also, on the basis of the results it may be assumed that BSs in countries with smaller GDP per capita are less developed as organizations, where relationships between leaders and those they lead are less formal, expressed by better sharing of information, participation of leaders in teaching those they lead and deliberate promotion of interdisciplinarity by BS managements. A strong correlation was identified between the accreditations (EQUIS and AACSB) and GDP per capita of the school’s country of location (prob. 0.0025). It means that BSs in countries with bigger GDP per capita are much more likely to get authoritative accreditations awarded for the organizational quality. It is very likely that a higher organizational LR does not provide any advantage to getting accreditations. One possible explanation is that the applying for accreditations is expensive and schools in countries with smaller GDP per capita are short of funds for applying for accreditations. Another reason might be that since majority of organizations which award accreditations are from countries with large GDP per capita, they tend to prefer organizations which are more similar to their country of location. Smaller formality and organizational flexibility of BSs as organizations in countries with smaller GDP per capita is regarded rather as a weakness in comparison with a more developed organization and clear hierarchy of BSs in countries with bigger GDP.

References Canals,J. (2010) “Can they fix it?“, Global Focus, Vol.4, No. 1, pp 14‐18. Dill, D. (1999) “Academic Accountability and University Adaptation: The Architecture of an Academic Learning Organisation”, Higher Education, No. 38, pp 127−154. Eduniversal Ratings&Rankings (2010) The best 1000 business schools worldwide. Eduniversal,France. Kristensen, B. (1999) “The Entrepreneurial University as a Learning University”, Higher Education in Europe, Vol. XXIV, No.1, pp 35‐46. Lorange, P. (1997) “A Business School as a Learning Organisation“, The Learning Organisation, Vol. 3, No. 5. Mulford, B. (2000) “Organisational Learning and Educational Change”, in: Hargreaves, A.A., Lieberman, M., Fullan, M. & Hopkins, D. (Eds.), International Handbook of Educational Change, Vol. 5, No. 1. London: Kluwer International Handbooks of Education. Sattelberger, T. (2011) “Business education 2025: what`s in store“, Global Focus, Vol.5 No. 3, pp 10‐14. Voolaid, K and Ehrlich, Ü. (2010) “Universities' Organizational Learning Rate Dependence on the Level of Participation in the Higher Education Market: The Case Study of Estonia”. Eric Tsui (ed.). Proceedings of the 7th International Conference on Intellectual Capital, Knowledge Management & Organizational Learning, The Hong Kong Polytechnic University, Hong Kong, China: Academic Publishing Limited, pp 480 ‐ 488. Voolaid, K. and Ehrlich, Ü. (2011) “Organizational Learning Rate Measurement Instruments for Business Schools“. Vincent Ribiere and Lugkana Worasinchai (eds). Proceedings of the 8th International Conference on Intellectual Capital, Knowledge Management & Organizational Learning. Bangkok, Thailand: Academic Publishing Limited Reading, UK, pp 593‐601. Yazdani, B. (2012) „Defining the role of business schools“, Global Focus, Vol. 6, No. 1, pp 34‐37. Watkins, K. and Marsick, V.J. (1996) Dimensions of the Learning Organization Questionnaire, [online], http://www.partnersforlearning.com/questions2.asp Voolaid, K. and Ehrlich, Ü. (2012) “How Business Schools as Learning Organizations Meet New Challenges: a Worldwide Study”. F. Chaparro (ed.). Proceedings of the 9th International Conference on Intellectual Capital, Knowledge Management & Organizational Learning, Bogota, Colombia: Academic Publishing International, pp 286–294.

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Ready For Open Innovation or not? An Open Innovation Readiness Assessment Model (OIRAM) Naphunsakorn Waiyawuththanapoom1, Thierry Isckia2 and Farhad Danesghar3 1 IKI‐SEA, School of Business, Bangkok University, Bangkok, Thailand 2 Telecom Ecole de Management, Institut Mines‐Telecom, Evry, France 3 IKI‐SEA, School of Business, Bangkok University, Bangkok, Thailand ronnakorn.v@bu.ac.th thierry.isckia@telecom‐em.eu f.danesghar@unsw.edu.au Abstract: Since the Chesbrough's seminal contribution many academic papers have documented the challenges associated with open innovation strategies. Furthermore, several case studies have illustrated the benefits that firms can derive from open innovation initiatives but only a few of them analyzed the difficulties associated with the implementation of these initiatives. The implementation of open innovation is a tricky business and there is a gap in the literature for a conceptual framework that guides managers in such a process. The aim of this paper is to provide a conceptual framework that can help managers assessing and enhancing readiness for open innovation. Based on an in‐depth review of the current literature, this study proposes a synthesized conceptual framework called Open Innovation Readiness Assessment Model (OIRAM) that provides a set of guidelines on how companies can both assess, as well as improve their ability to implement open innovation initiatives successfully. Keywords: open innovation, innovation implementation, readiness assessment, innovation framework

1. Introduction Since the introduction of open innovation concept by Chesbrough (2003) many organizations throughout the world, especially in knowledge intensive sectors, have adopted open innovation strategies (Arnold, 2009, Bigliardi and Dormio, 2012, Ditttich and Duyster, 2007, Harison and Koski, 2010, Van der Meer, 2007). The main objective of such strategies has been to improve the innovation process' efficiency, leveraging external knowledge, monetizing sleeping patents through various IP agreements, thus extending the role of R&D far beyond firm’s boundaries (Bianchi et al., 2010, Giarratana and Luzzi, 2009, Hakkim and Heidrick, 2008, Huang, 2011, sarkar and Costa, 2008, Lichtenthaler, 2009). Within the academic literature various case studies illustrated the benefits of open innovation as compared with closed innovation models (Chesbrough, 2006b, Chirstensen et al., 2005, Gann, 2005, Giarratana and Luzzi, 2009, Goduscheit et al., 2011, Lee et al., 2008, Xiaoyuan and Yanning, 2011). However, open innovation is a tricky business and beyond the success stories recounted in academic work, some failures or problems have questioned the successful implementation of open innovation (Lindergaard, 2011). Therefore, there is a need to clarify our understanding of open innovation implementation, hence the concept open innovation readiness. To date, there are very few studies on this issue and we need a comprehensive framework to appropriately respond to this problem. In this perspective, Bevis and Cole (2010 ) elaborated an open innovation readiness tool for newcomer SMEs in order to encourage and support open innovation programs. However, their analysis is focussed on companies that are not familiar with open innovation (Bevis and Cole, 2010 ). Later, Enkel (2011) developed an open innovation maturity framework that makes it possible to evaluate the performance of open innovation initiatives within organisations regardless newcomer’s readiness (Enkel, 2011). In the same vein, Piller (2011) also proposed an open innovation readiness framework based on four building blocks: strategy, culture, organisational structure, and system/methods, but it did not address external activities such as intellectual property right management, networking, and condition of the market that may play significant roles in an organization’s readiness. In this paper, we try to address the above‐mentioned shortfalls by providing a comprehensive framework synthesized from the existing literature in order to evaluate open innovation readiness, that is, the ability for successful implementation of open innovation initiatives. The remaining parts of the paper are as follows: Section 1 provides an overview of open innovation models. In section 2 the proposed open innovation readiness assessment methodology is presented in detail. And section 3 provides a summary of our findings.

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Naphunsakorn Waiyawuththanapoom, Thierry Isckia and Farhad Danesghar

2. Embracing open innovation Several case studies have been conducted and documented the implementation of open innovation initiatives but the concept of open innovation is still unclear (Huizingh, 2010, Lichtenthaler, 2008). Indeed, innovation is context specific and often relies on idiosyncratic characteristics (Antonelli, 2000). Innovation is a proteiform phenomenon. As a result, various dimensions of open innovation have been studied in the academic literature. Based on the work by Gassmann and Enkel (2004) one can distinguish three different types of open innovation process (see Figure 1). The outside‐in process focuses on the utilization of external knowledge, whereas the inside‐out open innovation process emphasises the benefits of sharing resources with external players. The coupled process refers to a mix between outside‐in and inside‐out approaches (Gassmann and Enkel, 2004). Basically, there is no silver bullet when it comes to the implementation of open innovation. Open innovation can be achieved via different processes and approaches.

Figure 1: Open innovation process by Gassmann and Enkel 2004 Based on Gassmann and Enkel (2004) work, Lichtenthaler and Lichtenthaler (2009) adopted a knowledge‐ based approach by integrating concepts such as absorptive capability and dynamic capability in order to complement existing framework. These authors complement the previous framework by adding three types of knowledge flows including knowledge exploration, knowledge retention, and knowledge exploitation and taking into account both the external and internal dimensions (see Figure 2). In Figure 2 each cell represents a knowledge capacity that the firm needs to develop in order to operationalize open innovation. However, as stated before, open innovation is context‐specific and the implementation process must handle these specificities. As a result, this capacity‐based approach needs further investigations (Lichtenthaler and Lichtenthaler, 2009, Huizingh, 2010).

Knowledge exploration Inventive capacity

Internal (Intra‐firm) External Absorptive capacity (Inter‐firm)

Knowledge retention Transformative capacity

Knowledge exploitation Innovative capacity

Connective capacity

Desorptive capacity

Figure 2: Open innovation process by Lichtenthaler and Lichtenthaler 2009 Later, Dahlander and Gann (2010) investigated the openness issue. Their analysis exhibits four categories of activity associated with open innovation (Inbound and outbound) and monetary involvement (pecuniary and non‐pecuniary) as shown in Figure 3. As a result, these authors identified four core activities depending on the nature of open innovation processes and the nature of the interactions between players. Inbound Innovation Outbound Innovation Pecuniary Acquiring Selling Non‐pecuniary Sourcing Revealing

Figure 3: Open innovation activities by Dahlander and Gann 2010 Capitalizing on Gassmann and Enkel (2004) work, Chiaroni et al. (2011) tried to complete our understanding of open innovation, integrating three core dimensions: the type of open innovation, the adoption process

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Naphunsakorn Waiyawuththanapoom, Thierry Isckia and Farhad Danesghar (Armenakis and Bedeian, 1999, Lewin, 1947), and the levers used by mangers in order to facilitate the adoption of open innovation (see Figure 4).

Figure 4: Open innovation framework by Chiaroni et al. (2011)

3. Open innovation readiness Based on previous studies, the current study have identified six key characteristics that can be used in order to evaluate open innovation readiness. These characteristics are classified into two distinct categories: internal and external. This classification is also consistent with previous studies (Chesbrough and Crowther, 2006, Bunganza et al., 2011). Internal characteristics refer to activities or tasks that organizations need to realize by themselves, whereas external characteristics refer to activities that are realized externally i.e. outside the firm’s boundaries. These two sets of characteristics are detailed below.

3.1 Internal characteristics associated with open innovation The implementation of open innovation initiatives often requires organizational and cultural changes (Poot et al., 2009, Dogson et al., 2006). These changes deal with activities carried out inside the organisation. Bunganza et al. (2011) advocate that organizations have to create specialized units and processes in order to manage both internal and external sources of knowledge. From this point of view, change management is a critical dimension associated with the implementation of open innovation. In addition, firm’s capabilities ‐ especially dynamic capabilities and absorptive capabilities ‐ are also important characteristics that must be integrated within the analysis of open innovation readiness (Lichtenthaler and Lichtenthaler, 2009, Teece et al., 1997, Cohen and Levinthal, 1990). Briefly, such capabilities can be considered as organizations’ ability to recognize both the opportunities and constraints associated with external knowledge in relation to the organisational requirements for innovation (Remneland‐Wikhamn and Wikhamn, 2011, West and Gallagher, 2006). Based on these elements, three sub characteristics associated with the internal context can be identified in the fields of knowledge management, change management and strategic management, which are explained below. 3.1.1 Relevant knowledge management characteristics Knowledge management capacity is a step forward in theoretical foundation of open innovation (Lichtenthaler and Lichtenthaler, 2009). The aim of a knowledge management system is to leverage both absorptive and dynamic capacities of organizations (Eisenhart and Martin, 2000, Lichtenthaler and Lichtenthaler, 2009). Following Chiaroni et al. (2011), open innovation refers to the adoption of a knowledge management system that can foster the diffusion, sharing and transfer of knowledge both internally and externally. In Kirkman’s view (2011), such a system relies on the codification of tacit knowledge and requires exploration and exploitation strategies (Kirkman, 2011) to cultivate organizational ambidextry. These approaches are also consistent with the Von Krough’s position (1998) who argues that knowledge management systems are mechanisms for identifying, capturing and leveraging the collective knowledge to sustain organization’s competitive advantage. In order to embrace these characteristics, we built upon the work of Lichtenthaler and Lichtenthaler (2009), taking into account knowledge exploration, knowledge exploitation and knowledge

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Naphunsakorn Waiyawuththanapoom, Thierry Isckia and Farhad Danesghar retention in our proposed framework. However, it also has to be pointed out that knowledge exploration is closely related with knowledge capitalization. Therefore, there are five main characteristics in a knowledge management system: knowledge strategic analysis, knowledge capitalisation, knowledge transfer, knowledge exploitation, and knowledge retention. 3.1.2 Relevant strategic management characteristics According to Huizingh (2010), the success of open innovation adoption depends on organization characteristics and strategy characteristics. Within this contingency approach, organization characteristics include size, competencies (both collective and individual), market position, and strategic goals; while strategy characteristics include strategic orientation, aspects or goals of the innovation strategy, and organizational culture. Some characteristics of innovation strategies are also relevant such as the nature of the existing business network, the type of innovation (disruptive, incremental, etc.) (Huizingh, 2010, Lichtenthaler and Ernst, 2009). All these characteristics can help understanding how organizations can prepare themselves for open innovation via their strategic management (Lee et al., 2010, Laursen and Salter, 2006). 3.1.3 Relevant change management characteristics Moving from closed to open innovation is not easy and it often implies a significant cultural and organisational change (Dogson et al., 2006, Poot et al., 2009, Van der Meer, 2007). Researchers have developed various models and approaches to facilitate change within organizations. In Leavitt’s view (1965), four levers must be activated simultaneously: structure, task, technology, and people. (Tushman and Romanelli, 1985) have refined Leavitt’s model by adding other critical dimensions such as culture, strategy, power distribution, and control system. Most of the models proposed in the literature show two major dimensions associated with change management: structural change and cultural change. Following Bunganza et al. (2011) the implementation of open innovation requires important changes in organizational design or structure. Belts (2012) suggested that differences in organizational structure explain the nature of open innovation initiatives. In this perspective, he identified three types of organizational structures associated with open innovation namely centralised (all open innovation projects are controlled and managed by a centralized unit), decentralised (each business unit has its own entity in charge of open innovation projects), and hybrid (a mix between the two previous forms of organizational design). As mentioned above, the organizational culture is an important dimension when it comes to implement open innovation (Remneland‐Wikhamn and Wikhamn, 2011). For instance, the Not‐Invented‐Here (NIH) syndrome (Katz and Allen (1982) is often pointed out as an element that prevents the organization to launch open innovation strategies (Gassmann, 2006, Lichtenthaler and Ernst, 2009, Dahlander and Gann, 2010, Lichtenthaler, 2011). At this level, Remneland‐Wikhamn and Wikhamn (2011) developed an Open Innovation Climate Measure (OICM) based on the work of Patterson et al. (2005). These authors identified four dimensions in creating open innovation climate: flexibility (the organization's propensity for action and change), innovation (the organization's propensity to innovate), outward focus (the responsiveness of an organisation and its staff to the external demand), and reflexivity (the ability to respond to external changes). Therefore, it can be suggested that to ensure success of open innovation implementation projects, organisation needs to explicitly address these characteristics. As a consequence, the characteristics associated with change management can be spited in two sub categories: structural change characteristics and cultural change characteristics. Structural change characteristics emphasise role, unit, supportive system, and rewarding system, whereas cultural change characteristics consists of innovation atmosphere, flexibility, outward focus, and reflexivity (Bunganza et al., 2011, Chesbrough, 2006b, Remneland‐Wikhamn and Wikhamn, 2011).

3.2 External characteristics associated with open innovation Open innovation is often equated with external or networked innovation (Chesbrough, 2006b). Indeed, the goal of open innovation is to uncover new ideas, leveraging both internal and external knowledge (Chesbrough, 2003). With a better understanding of “what is out there”, organizations can lower risk by combining external capabilities with internal innovation resources. From this point of view, the way organizations manage their external activities ‐ i.e. how they collaborate and manage their relations with external players (Bunganza et al., 2011) ‐ paves the way for successful open innovation implementation. According to (Chiaroni et al., 2011) one of the key success factor in implementation of open innovation is the

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Naphunsakorn Waiyawuththanapoom, Thierry Isckia and Farhad Danesghar ability of the organization to “manage different networks for different purposes”. In this perspective, technology exploration is crucial since it enables companies to acquire new knowledge and technologies from external sources. In this perspective, companies may outsource R&D activities in order to capitalize on external knowledge bestowed in specialized outsourced companies that can be licensed or bought (Gassmann, 2006, Prencipe, 2000). When exploring external knowledge, companies can externally acquire intellectual property (IP), including the licensing of patents, copyrights or trademarks, to benefit from external innovation opportunities (Chesbrough, 2006a). Thus, intellectual property right management is an important dimension of open innovation since it involves exploration of external knowledge. In addition, as suggested by Lichtenthaler (2009), in outbound innovation (inside‐out) companies also need to ensure that they capture value from their technology. In Lichtenthaler’s view, environmental moderators such as patent protection, technological turbulence, transaction rate, and competitive intensity may benefit companies’ as a result of outbound open innovation. Finally, characteristics associated with external networking and IP arrangements are important dimensions that must be integrated alongside with environmental moderators in assessing open innovation readiness. In the following points, we discuss these characteristics in detail. 3.2.1 Relevant IP characteristics Creating infrastructures that encourage participation and collaboration is central within the open innovation paradigm (Chesbrough, 2003, Chesbrough, 2006b). In such context, the management of IP plays a significant role (Giannopoulou et al., 2011, Lee and Lee, 2009) which has also been identified as a major inhibitor of open‐ innovation practices (Deschamps et al., 2013). It stems from these studies that IP is a double‐edged sword: on one side, strong patent protection leads to more attractive technology transfer schemes (Grindley and Teece (1997), but on the other side, it may have a negative impact on the firm’s performance, especially in outbound open innovation (Lichtenthaler, 2009). Various arrangements can be used to manage IP. In Granstrand’s view (2011), these arrangements can be classified in two categories, given the nature of licensing agreements and inter‐organizational arrangements (Granstrand, 2011). 3.2.2 Relevant external network characteristics Establishing appropriate relationships between organization and various types of external players is crucial in open innovation. These relationships may enhance the capability to both explore and exploit knowledge inside and outside the organisation (March, 1991). There are many type of external players that can be enrolled in open innovation projects such as universities (Perkmann and Walsh, 2007), customers (von Hippel, 2005), suppliers (EmdenGrand et al., 2006), and even competitors (Mention, 2011). Following Ditttich and Duyster (2007), the type of network structure chosen by an organization is closely related to its objectives or goals. As a consequence, in order to successfully implement open innovation, the organization should be able to clearly identify both the purpose of network creation as well as the type of actors that need to be enrolled. Finally, four characteristics of external network management are proposed as being: the type of actor in the network, the objective of the network, the network creation process, and the network retention process. 3.2.3 Relevant environmental moderators Lichtenthaler (2009) stated that one of the most important factors affecting the success of outbound open innovation lies in the characteristics of environmental moderators. Among these moderators, technological turbulence, transaction rate, and competitive intensity (Lichtenthaler, 2009) have a positive impact on open innovation implementation and thus have to be considered prior to the launch of open innovation projects. Based on the above considerations the proposed open innovation readiness assessment model (OIRAM) is shown in figure 5.

4. Conclusion and future works This study provided an integrated framework synthesized from the current literatures for assessing the readiness of an organisation prior to open innovation implementation. It stems from our preliminary study that in order to assess open innovation readiness, six core characteristics, both at the internal and external levels, have to be considered carefully. With respect to the internal dimension associated with open innovation, these characteristics refer to knowledge management, strategic management, and change management. With respect to the external dimension associated with open innovation, these characteristics refer to external network management, intellectual property right management (IPRM) and environmental moderators. The proposed conceptual framework (OIRAM) is aimed at assisting managers in evaluating the

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Naphunsakorn Waiyawuththanapoom, Thierry Isckia and Farhad Danesghar overall ability of organization for successful implementation of open innovation projects as a precursor to reducing various inherent risks associated with these projects. In its current form the proposed model lacks required measurements that are necessary for operationalization of its various concepts; and for this reason it can only be used as a general guide. Therefore, development of a corresponding operational model that enables testing and verification of the proposed model in various contexts constitutes one major future work in this area. This in turn will enable development of context‐specific models, e.g., for various industries, and under a variety of organizational cultures. Another future work would be the adoption of mixed research methodologies in order to uncover additional aspects of open innovation readiness. • • • •

• • • • •

Aim Type Network Creation Network Retention

Knowledge Strategic Analysis Knowledge Capitalisation Knowledge Transfer Knowledge Exploitation Knowledge retention •

External

Internal •

• • •

Technology Turbulence Transaction Rate Competitive Intensity

• • •

Licensing Type Collaboration Type

Organisational characteristic • Size • Competency • Position • Strategic objective Strategic characteristic • Player • Type of innovation strategy

• Culture/Climate Change Structural change • Innovation • Roles • Flexibility • Unit • Outward focus • Rewarding system • Reflexivity • Supporting system

Remark: KM = Knowledge management, SM = Strategic management, CM = Change management, IPRM = Intellectual property right management, EM = Environmental moderators, and NW = Network Figure 5: The open innovation readiness assessment model (OIRAM)

Acknowledgements This research is part of a larger joint research project by the PhD Program in Knowledge Management and Innovation Management (PhD KIM) at Bangkok Univeristy in Thailand, and Telecom Ecole de Management (Institut Mines‐Telecom) in France. The authors would like to thank the above institutes for providing required financial, administrative, and in‐kind supports required for this project.

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Cultural Influences on Knowledge Sharing Behaviours Through Open System Vs. Closed System Cultures: The Impact of Organisational Culture on Knowledge Sharing Hanan Abdulla Al Mehairi Faculty of Business and Management, University of Wollongong, Dubai, United Arab Emirates hanan.almuhairi@gmail.com Abstract: In the highly dynamic business environment, organisations are required to adapt quickly in response to the changing needs of their customers. To meet those needs, many organisations have invested in knowledge management. A recent body of literature has highlighted an organisation’s culture as one of the best strategies to enhance knowledge management via knowledge sharing among their workers. This research paper will focus on the association between knowledge sharing and the dichotomy between cultures with either open or closed systems. A closed system culture believes that newcomers cannot influence the effectiveness of either the workforce or management. An open system culture is different, because it recognises the influence of newcomers on workers behaviour (Lee and Lai 2007). This dichotomy is important because some have suggested that systems encourage knowledge sharing by accommodating newcomers. The current study aimed to test the hypothesis that open cultures encouraged knowledge sharing, while closed cultures inhibited knowledge sharing, focusing on participants of Emirati nationality. To that end, a field survey was distributed to 207 professionals at more than 10 firms, including both private and the government sectors. The results of our study were not significant, but surprisingly found preliminary evidence indicating a positive relationship between closed cultures and knowledge sharing, We believe this suggests that closed system cultures either have no effect on knowledge sharing, or may promote knowledge sharing through some unknown mechanism. Further study is needed to definitely conclude how closed and open cultures affect knowledge sharing behaviour within Emirati participants. Keywords: knowledge sharing; knowledge management; organisational culture; behaviour; hofstede; organisational culture dimensions; open system culture; closed system culture

1. Introduction In the dynamic workplace of today, many organizations have recognised the significant utility of intellectual capital and knowledge. One crucial aspect of intellectual capital is making sure that knowledge is disseminated throughout an organization’s employees. To achieve that goal, organizations can hold seminars and explicitly teach skills, but all of that expense could be obviated if employees could spontaneously share knowledge amongst each other. Some studies have shown that knowledge sharing amongst people and entire workplace divisions may help to improve workplace effectiveness among poorly performing employees (Blair 2002). These results have led to the development of new concepts like knowledge mapping and knowledge broker firms (Tohidinia and Mosakhani 2010). For this reason, organisations have invested different ways to use knowledge sharing to improve their competitive advantage. To that end, organisational culture has been explored as a factor that could potentially improve knowledge sharing, which is a vital strategy (Ruppel and Harington 2000). Previous research has addressed the impact of organisational culture on knowledge sharing behaviour and knowledge sharing norms, focusing on either Western or Asian countries (Ardichvili, et al., 2006; Wang &Chang, 2007; Walczak, 2008 ; Cronin, 2001 ; Bock, G‐W et al., 2005). Other studies have addressed the impact of organisational culture on knowledge sharing within Gulf Countries and UAE such as studies by (F. Dulaimi, 2007 ; Boumarafi & Jabnoun, 2008 ; Klein el al., 2009 ; M. Al‐Adaileh & Al‐Atawi, 2011). However, many of these studies were conducted in the UAE, but not all respondents were UAE nationals, which might not be representative of the UAE culture. In the present research, we focus on a sample of both private and government professionals in the United Arab Emirates (UAE). The UAE was united in 1971 as a federation of seven emirates. The UAE consists of seven Emirates which are Abu Dhabi, Dubai, Sharjah, Ajman, Fujeirah, Ras al‐Kheimah and Umm al‐Qawain. Within 42 years, UAE has experienced significant economic changes. According to the UAE Business Intelligence Report (2009), the UAE is considered as the richest country in the Middle East on a per capita basis. With so many unique characteristics, we sought to examine organizational culture within the unique cultural milieu of Emirati culture.

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Hanan Abdulla Al Mehairi This paper is divided into three sections. The next section will introduce the constructs of knowledge sharing, while the section three focuses on knowledge sharing in relation to open or closed system cultures. The fourth section will outline the research goals and hypotheses, while the results section will explain our findings. Finally, the conclusion attempts to take the presented evidence and draw inferences regarding the ability of open systems to promote knowledge sharing in the UAE.

2. The critical role of knowledge sharing Firms and especially for‐profit companies need to understand the essential role of knowledge sharing. According to Huang, et al. (2010), knowledge sharing is absolutely essential to the knowing and learning process. Over time, employees accumulate knowledge and can either choose to share it with their colleagues, or keep that information to themselves (Blair, 2002). The number of employees making the same decision will multiply the impact of that choice, rippling throughout their environment. In turn, this decision is dependent on the amount of trust the employee feels between his or her co‐workers. Will their co‐workers exploit them? Will their co‐workers value their knowledge? These are the factors that employees consider when they are about to share their knowledge. Unfortunately, there is very little research investigating the factors they could potentially influence the amount of trust within an organization. However, one recent study has suggested that the level of trust in an organization is attributable to the organization’s culture (Al‐Alawi, et al., 2007).

3. Open system versus closed system organisational cultures 3.1 Closed organisational systems According to Hofstede et al. (2010), in a very closed culture, new entrants are not allowed into established groups or identities. It is highly difficult for a new person to join the organisation, and to participate in organisational affairs. Individuals may be considered outsiders if they come from outside the organization, or even other parts of the organization, and are restricted from accessing certain aspects of management or organisational functionality (Hofstede et al, 2010). In such an organisation, the administration will be highly distanced from the rest of the employees and will often try to avoid any relations with them. Understandably, these organisations will often embrace management, and fail to promote the development of inter‐ organisational relations. Internal members of the closed culture system feel that their input or the knowledge can determine the performance of the group or organisation, but fail to acknowledge that organisations and cultures in general, are dynamic. Based on the system’s view, it does not rely on outsiders for solutions or explanations to managerial issues. Furthermore, closed systems believe that they are sealed off from the dynamics of the external world. Therefore, the system relies on internal dynamics and processes in explaining individual, group and organisational behaviour. The employees in this culture are characteristically closed and reserved – both among insiders and outsiders (Hofstede et al. 2010). Within a closed system, the basic assumption is that the knowledge held among the members of the particular culture or the entire organisation is enough to warrant an efficient performance. As a result, an organisation guided by this system will not embrace the knowledge held by newcomers (individuals or groups) that could be considered outsiders, from an internal viewpoint.

3.2 Open organisational systems In an open systems culture, internal and external communication is highly encouraged. This stems from the high accessibility of the organisation, among the newcomers from within the system or outside (Hofstede et. al, 2010). Therefore, such a system makes it easy to import new skills, technologies and organisational practices, which expand the knowledge‐base of the organisation. An organisation guided by the open culture system is also more likely to incorporate the input of new entrants. Thus, the system assumes that any newcomer can adjust to the level of fitting into the particular organisation. In the area of adjusting to fit the organisation, an employee would need to adopt a few, if not all the organisational culture traits of the organisation in question. As a result, such an organisation will often receive knowledge from outside sources like other firms, as well as areas of competence among organisational departments or groups. It is possible for such an organisation to recognise the individual skillsets of different individuals or groups. In such an environment, newcomers who depict exceptional managerial skills are likely to get recognised. Such

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Hanan Abdulla Al Mehairi characteristics make the model friendly to new knowledge sources as well as new knowledge sharing avenues. Such avenues could include online communication, interpersonal communication, organisational meetings as well as group‐targeted training (Sahoo & Das, 2011). Through the creation of a culture of knowledge sharing, the employees of the organisation are more likely to adapt to knowledge sharing values, which lead to the development of knowledge sharing behaviours in the long term. This is due to the flexibility of relationship building in such a culture. The system is also more apt to acknowledge external influences, which have the potential to either impair or foster the development of knowledge sharing behaviours. These values profoundly influence the internal functioning of the organisation. Furthermore, the open systems believe that higher standardization of employees can be attained through more effective training. Here, employees can be motivated towards the realization of organisational goals. In some cases, jobs and schemes are structured to capture and stimulate the interests of employees (Sahoo and Das 2011, p. 48). As a result of these factors, individuals work more cooperatively (Gebert and Boerner 1999, p 356‐358) and share information with one another more readily.

4. Research approach and hypotheses (empowering knowledge sharing behaviours through open system versus closed system cultures) The leaders of an organisation play a critical role in enabling the process of knowledge sharing and exchange, eventually cementing the behaviour of knowledge sharing amongst the staff of their organisation (Yang and Chen 2007). The literature indicates that leadership commitment is one of the more important characteristics involved in developing a culture where knowledge sharing is fully integrated. To that end, we aimed to convince leaders by running an experiment examining the relationship between an open/closed culture and an employee’s willingness to share information. Because it is hypothesized that open and closed cultures exist along the same continuum, we used a measurement of closed cultures, whereby more open cultures were represented by lower scores while more closed cultures were represented by higher scores. The willingness to share information scale was created to measure an employee’s ability and desire to share information with his or her colleagues. We hypothesized that the closed system that may impair the development of knowledge sharing, partially due to the limitations imposed on the establishment of personal relationships. This undermines the development of knowledge sharing behaviour (Gebert and Boerner 1999). This leads to hypothesis 2 in (table 1); we hypothesize that a more closed culture will limit relationships and negatively impact knowledge sharing. Table 1: Proposed hypotheses HYPOTHESES

1. Within an Open System Culture

2. Within a Closed system Culture

PREDICTED OUTCOMES, DEPENDENT ON CONTINGENTS OUTCOME CONTINGENT Creates closeness between the organisation and newcomers Positive impact on knowledge sharing Opens the platform of communication and knowledge sharing with internal and external parties, where it can get newer ideas

Positive impact on knowledge sharing

Acknowledges the input of different individuals with different characteristics Distances newcomers from the organisation

Positive impact on knowledge sharing Negative impact on knowledge sharing

Limits the sharing and the reception of knowledge with both internal and external parties, therefore limits itself from new knowledge sharing approaches

Negative impact on knowledge sharing

Disregarding the input of different insiders and outsiders

Negative impact on knowledge sharing

According to Gebert and Boerner (1999), the adoption of an open system favours the development of a knowledge sharing culture. Theoretically, some of the barriers to relationship building should be eliminated, and the focus shifted to the recognition of individual potential – which offers a critical mode of knowledge sharing and transfer (Hofstede and Bond 1984). As Minyoung, Myungsun, Soo and Seokhwa (2012) argued, the development of a knowledge sharing culture can only be realized through the abolishment or loosening of the bureaucratic model guiding the leadership of an organisation. This is the case, as the commitment to shift to

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Hanan Abdulla Al Mehairi knowledge sharing must be communicated and practiced between different levels of workers. It should also be communicated in a manner that will allow for the constant knowledge exchange (McDermott and O’Dell 2001). Wang and Noe (2010) point out that the success of the field of knowledge management relies greatly on knowledge sharing. Furthermore, the different areas of knowledge sharing touch on knowledge management in a great way (Wang and Noe, 2010). These areas include organisational context, team and interpersonal characteristics, individual characteristics, cultural characteristics, and motivational factors (Zhang and Bartol 2010). Furthermore, it is evident that a closed system cannot promote the development of knowledge sharing culture (Gebert and Boerner 1999). This is the case given that a closed system does not give consideration to external aspects like cultural characteristics and motivational factors, which could be external (McDermott and O’Dell 2001). Based on all of the presented evidence, an open system is highly inclusive of both human and the external influences to the take‐up of knowledge sharing among individuals – who form the core of a knowledge sharing culture (Gebert and Boerner, 1999),. This leads to hypothesis 1 in (table1).

5. Method 5.1 Procedure and participants Our survey was distributed to Emirati government and private firms from different industries, including Dubai Police General Head Quarters and Dubai Courts, along with airlines, utility, oil and gas companies. The questionnaire was distributed electronically for most companies, but also physically for any organisations that don’t allow their employees to access the internet during work hours. We received a total of 207 responses. The participants were 40.29% female and 59.71% male. The sample was 57.97% Emirati national and 42.03% non‐Emirati. After eliminating potential outliers, a simple regression was conducted to test the relationship between the dependent and independent variables.

5.2 Independent measures: Closed system culture Participants completed 4 items that measured the extent to which their organization had a closed system culture. The items were rated on a likert scale from 1 (Strongly agree) to 5 (Strongly disagree). One sample item from the scale was, “Our organization and people are closed and secretive, even among insiders.” Factor analysis confirmed that all 4 items loaded onto a single factor that showed high reliability (Cronbach's alpha = 0.882).

5.3 Dependent measure: Intention to share knowledge Participants completed 2 items that measured the intention to share explicit knowledge, while 3 items measured the intention to share implicit knowledge. The items were answered with endpoints labelled 1 (Very unlikely) to 5 (Very likely). Factor analysis confirmed that all 5 items loaded onto a single factor that showed high reliability (Cronbach's Alpha =0.904).

6. Results We first tested our hypotheses via simple regression between the dependent (knowledge sharing) and the independent variables (degree of closed system culture). The overall model was marginally significant, R2 = 0.016, F(1, 205) = 3.428, p=0.066, indicating there may be a significant relationship between the variables (see Figure 1). Because we were specifically interested in Emirati nationals and only half of our sample was composed of Emirati nationals, we hypothesized that nationality may moderate the effect of closed system culture on the intention to share knowledge. To that end, we added nationality as a potential moderating variable to the regression. The overall model was not significant, R2 = 0.024, F(3, 203) = 1.648, p=0.179, indicating that a closed system culture does not affect the intention to share knowledge overall. However, the original relationship between closed system culture and the intention to share knowledge became significant, B = 0.164, p=0.035 (see Figure 2). Surprisingly, we found that closed system cultures positively predicted the intention to share knowledge, which was the opposite of what we hypothesized. This means that the extent to which participants were in a closed culture system was positively predictive of their intention to share knowledge with their co‐workers. We hypothesize that some organizations, such as the police force, where knowledge sharing is formally

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Hanan Abdulla Al Mehairi encouraged. Police officers are required to share their knowledge while investigating a crime. Therefore, some of those organisations could be following a closed system culture but have adapted a way to overcome a natural, cultural tendency to not share knowledge among employees.

Figure 1: Suggest that closed culture system doesn't influence the intention to share knowledge

Figure 2: Closed culture system positively influences the intention to share knowledge when considering nationality as a control variable Table 2: Analysis summary Intention to Share Knowledge p‐value B(SE) (constant) 3.845 Closed system Culture 0.164 (0.077) 0.035 Emirati ‐0.125 (0.101) 0.219 Emirati*Closed Culture 0.439 (0.362) 0.227 °p<.10, *p < .05, **p<.01, ***p < .001

7. Conclusion and discussion Our study aimed to test whether closed cultures were related to knowledge sharing in an Emirati culture. Unfortunately, we did not find conclusive evidence that closed culture ratings negatively predicted knowledge sharing intentions. In fact, our regression provides weak evidence that closed cultures positively predicted knowledge sharing intentions (B=0.164). Before any strong conclusions are made, this topic would require further study. However, several factors could explain these unexpected finding. First, previous studies have focused on Western or Asian cultures, while this study focused on Emirati culture. Given that we are studying cultures, it is possible that closed Emirati cultures are categorically different than non‐Emirati cultures. In fact, we found that, even within the UAE sample, nationality of the respondents modified the relationship between closed system cultures and the intention to share knowledge. Second, we chose to measure closed cultures along a continuum, hypothesizing that open cultures would be synonymous with low‐closed culture ratings. Although this was based on Hofstede’s work, it is possible that we would have different stronger results if we had chosen to measure open cultures on a scale independent from closed cultures. It is likely that the lack of closed cultures is not always the same as having an open culture. Third, we cannot account for unknown heterogeneity within our sample. The results of our regression could be accounted for by Simpson’s paradox, with two different (unknown) groups driving this weak, positive correlation. However, we would be unable to verify this until we are able to identify these two groups. Future experiments could attempt to identify more heterogeneity within organizational cultures in an attempt to find the true relationship between open/closed system cultures and knowledge sharing.

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Hanan Abdulla Al Mehairi Overall, this study provides weak evidence that closed system culture may lead to knowledge sharing behaviour within Emirati culture. However, there is still a large body of evidence indicating that open‐system cultures are more likely to promote knowledge sharing. Although the current study does not contradict any previous studies, it suggests that the relationship between organizational culture and knowledge sharing may be more complex than previously thought. Future studies should control for nationality when examining knowledge sharing and closed system cultures.

Acknowledgements The author would like to extend her sincere appreciation to His Highness General Sheikh Mohamed Bin Zayed Al Nahyan the Crown Prince of Abu Dhabi for sponsoring her throughout her studies in PhD program at Wollongong University in Dubai. Reid Offringa consulted on the editing of this paper and the study’s data analysis.

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Knowledge Management as a Competitive Advantage of Contemporary Companies Andrijana Bogdanovska Gjurovikj Knowledge Center, Skopje, Macedonia

andrijana,djurovic@theknowledge-center.com Abstract: By merging existing theory, discussion and research in the areas of strategic and knowledge management, the paper argues in favor of the basic assumption of all schools on strategy – competitive advantage results into a superior performance of contemporary companies. Causation, however, can only be justified when knowledge management is introduced as a moderator in the relationship. Although recognized as an important resource as early as 1950s, knowledge did not gain in strategic value until the emergence of the Resource Based View on Strategy in the 1990s. Realizing that knowledge is a dynamic capability and that today’s knowledge although protected, is not a guarantee of tomorrow’s success, contemporary organizations approached knowledge management as a system comprised of four knowledge management processes: creation, organization, dissemination (transfer and sharing) and use (applicability), open to the influence of the wider external and internal environment considered through the social and technological context of the organization. By focusing on the internal context, the paper presents an empirical model which can be tested in different environments. The internal context reflects the influence of three predominant KM platforms: structure, culture and technology. In the process, technology is recognized as a main enabler for capturing, storing and distributing codified knowledge, while organizational structure that fosters KM processes as flexible, lean, team based and customer focused. In regards to organizational culture, adaptive and flexible cultures are recognized as supportive to knowledge generation; while stable and hierarchical cultures supportive to knowledge storage in organizational routines. At the end, the effectiveness of KM as a competitive advantage is approached through identifying superior financial and non-financial performance. Keywords Knowledge management, strategic management, competitive advantage

1. Introduction The impact of organizational knowledge on the performance of companies has been present in the economic theory of the firm long before the same became of interest in the area of strategic management. Although recognized by its proponents (the economic theory of the firm) as a significant resource since its 1950s, knowledge was treated as a static (intellectual) capital, which was exploited and protected. As a result, according to Nielsen (2005) during the domination of the economic theory of the firm, knowledge management within companies was a process of collecting and transforming individual and organizational knowledge in explicit format through codifying the same in the organizational systems and working on its protection. This view dominated the best practice in the field until the 1990s, when the emergence of the resource based view of the firm (Barnet 1991, 2001, Rumelt 1991) accompanied with an increasing awareness about the significance of the core competencies of the firm (Kaplan and Northon 1990) and the importance of the dynamic capabilities of Teece et.al. (1997), strongly emphasized the need for coordination of all organizational resources and capacities in efficient and effective way. Superior knowledge emerged as the most significant strategic resource of all, while the capacity of organizations to coordinate and combine resources in effective and efficient way, the most important strategic capability. The main assumption on which further research continues identified knowledge management as the main contributor to superior performance of companies through its impact on competitive advantage. The aim of the paper is to provide a theoretical model which will enable researchers to test this assumption, in particular: 

In what way knowledge management influences the competitive advantage of companies, if any? and



What is the impact of knowledge management on the performance of companies, i.e. whether management of organizational knowledge can result in a superior performance of organizations?

2. Knowledge and knowledge management in organizational settings To be able to adequately cover the research questions, one needs well-defined concepts. As the main topic of the article, the term knowledge refers to the organizational knowledge, defined under Davenport and Prusak’s (2000, p.5) definition of knowledge, “as a fluid mix of framed experience, values, contextual information, and expert insights... It originates and is applied in the minds of knower. In organizations, it often becomes embedded not only in documents or repositories, but also in organizational routines, processes, practices, and

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Andrijana Bogdanovska Gjurovikj norms“. For the concept to be clear, Davenport and Prusak (2000) make a difference between data, information, knowledge and wisdom. According to Davenport and Prusak (2000) who follow the autopoietic epistemology of Nonaka and Takeuchi (1995), in organizational terms, the information becomes knowledge when the same is expressed and accepted as a closed expression of truth and valid interpretation of the reality by the members of the organization. When knowledge is used in the most optimal way it becomes wisdom. Nonaka and Takeuchi (1995) on the other hand view data as the input while the information is placing the data into specific context. Knowledge is interpretation of the information. The stimuli therefore, i.e. the data, may come from the outside of the organization, but the knowledge and the information are generated only inside of the organization and cannot come from outside. Compared to other epistemologies on knowledge as the cognitive epistemology of Simon, Chomsky, and Minsky, and the connectivists as Zander and Kogur (1995), the autopoietic epistemology of Nonaka and Takeuchi (1995) still provides the most comprehensive definition of the term (von Krogh et.al. 1998). According to Grant (1996), superior knowledge can enable companies to coordinate and combine resources and capabilities in new and distinctive ways, while the capability to acquire, integrate, store, share, and apply defines the term of knowledge management aimed at creating a sustainable competitive advantage. Predominately reflecting the SECI model and the Knowledge-spiral model of Nonaka and Takeuci (1995), Davenport and Prusak (2000) identify three specific knowledge management processes: (1) generation, (2) organization and (3) transfer. These three processes, however, are not enough to explore the concept and the importance of knowledge management within the business strategy of companies. By enlarging the list of KM processes with the inclusion of the Choo sense-making model, the Wiig model of knowledge sharing and the i– Boisont model, one can specifically identify four categories of knowledge management processes: (1) generation, (2) organization and storage, (3) dissemination and (4) application/use. Although categorically distinctive, empirical research proved the processes difficult to explore in organizational context based on the autopoietic epistemology of Nonaka and Takeuchi (1995), as they fail to distinguish between information and knowledge in the processes of organization and storage and transfer and sharing; and knowledge and wisdom in the processes of generation and use. As a result, Davenport and Prusak’s (2000) definition of knowledge, may be too limited for the current developments in theory. Moreover, it may as well be too limited for the actual external context in which contemporary organizations operate. The inter organizational synergies from one side, and the impact of new technology increasing the customer/public input from another, create a new context where it is difficult to argue that organizational knowledge is generated only from inside of the organization (regardless of the stimuli) as argued by the autopoietic epistemology.

3. Knowledge management processes By working out the details on each of the identified processes and how the same have been previously approached in literature, it is possible to develop a specific description for each of the processes separately.

3.1 Knowledge generation The description of the knowledge generation processes provided in literature sources on the subject emerge from the work of Nonaka and Takeuchi (1995), and the less known model of i-Boisot (1998) based on social learning. Davenport and Prusak (2000:52), define the term as a set of specific activities and initiatives which organizations undertake for increasing their stock of organizational knowledge. Some authors as Allard (2004) and to some extent Choo (1998), approach knowledge generation out of the perspective of creating new knowledge. Others as Davenport and Prusak (2000), define more components as are knowledge acquisition (KGA) from external sources (transferring property rights of knowledge from external sources and through network - synergies), knowledge creation (KGC) (developed from within the firm through fusion i.e. the famous concept of “creative abrasion” as introduced by Leonard-Barton (1995) in Wellsprings of Knowledge and knowledge rental (KGR) (leasing or renting knowledge).

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3.2 Knowledge organization and storage For knowledge to be valuable and useful, the same needs to be organized. In order to organize the knowledge the company must have a capacity to discover the knowledge as it emerges in people and practice, identify the holders and potentially store the same for further use. The process, therefore, derives from the models of Wiig (1993), i-Boisot (1998) and Nonaka and Takeuchi (1995), while Davenport and Prusak (2000) define the same in more detail. The process as well covers organization’s ability to measure its knowledge assets (Simon et al 2004; Davenport and Prusak 2000) and protects the knowledge of the organization regardless of its type (Liebskind 1996).

3.3 Knowledge dissemination Knowledge dissemination is a critical process that occurs at various levels and among various entities in the organization. It can happen between individuals, from individuals to explicit sources, from individuals to groups, between groups, across groups, and from the group to the organization. Having in mind the division of Polinay (1962) on tacit and explicit knowledge, the process covers both. Davenport and Prusak (2000) describe knowledge transfer which arises from the models of Nonaka and Takeuchi (1995), Wiig (1983) and i-Boisot (1998) and covers explicit knowledge; knowledge sharing arising from the models of Nonaka and Takeuchi (1995), i-Boisot (1998); and Choo (1998) covering tacit knowledge and a very important process not defined in other models - replication of explicit knowledge as defined by Zander and Kogut (1995).

3.4 Knowledge use/application The new and old knowledge, collected, stored and transferred, within the organization and its networks, has little organizational meaning, if it is not used by the organization, or its people. Literature on knowledge management and existing models define that knowledge can be used in three specific ways: (1) the sense of a changing environment (sensing) as identified in the model of Choo (1998) later modified by Weick (2001); (2) Decision-making based on many models, but mainly on the limited rationality of Simon (1957), a model which premises tend to be accepted by others; and (3) knowledge generation from the old knowledge through fusion, adaptations or integrations (Choo 1003, Davenport and Prusak 2000). The last part – creating new knowledge from the existing knowledge is already covered under the processes of new knowledge generation capabilities.

3.5 Knowledge management platforms An effective knowledge management model consists of knowledge management processes and incorporated specific knowledge management platforms identified in the supportive organizational structure, culture and cutting edge technology. These platforms are materializing Nonaka and Konno (1998) concept of Ba, and adjust the same into more practical forms.

3.6 Organizational structure Out of the identified platforms, organizational structure is the oldest ‘place’ which has been explored in terms of its impact on managing organizational knowledge. In contemporary organizations, it reflects the combination of Fayol’s principles of management and Weber’s premise of authority. Within the many variations of these dimensions, Nonaka and Takeuchi (1995) identify the decentralized and team based organizational structure as the one which fosters knowledge sharing processes. Empirical findings support the assertion (Lubit 2001) and identify KM supportive structure as flexible, lean and team-based (Bennet and Bennet 2004, Knapp and Yu 1999, O’Sullivan and Azeem 2007). In the majority of these studies, however, the focus is on structures that support knowledge sharing, transfer and generation. It would be difficult to assume that the same organizational structures will support knowledge transfer and organization. The variance therefore needs to be considered in an empirical research.

3.7 Organizational culture Nonaka and Konno (1998) and Nonaka and Takeuchi (1995) were the first to identify the social context of organizational environment as crucial for knowledge sharing processes. The same can as well be attributed to i-Boisot (1998) model. Unfortunately, for the existing empirical research, the majority of scholars in the past, approached organizational culture as a barrier in implementing KM initiatives in organizations, an element

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Andrijana Bogdanovska Gjurovikj which can be an obstacle for implementing KM initiatives (Soley and Pandya 2003, Kaywort and Leidner 2004). Very few approached it as a knowledge supporting platform (Kaywort and Leidner 2004). According to the research of Kaywort and Leidner (2004), based on Hofstede’s cultural dimensions, adaptive and flexible cultures support knowledge generation; however, stable and hierarchical cultures, support knowledge storage in organizational routines. Organizational cultures which are strong, open, parochocical and employeeoriented favor knowledge sharing and application. As a result, in essence, different organizational cultures will support or limit different types of KM combination of processes and with that different KM strategies (codification vs. personalization). The variance therefore needs to be considered in the empirical research.

3.8 Technology Technology is recognized as a main enabler for capturing, storing and distributing structured (codified) knowledge (Khalifa and Liu 2003, Moffett et.al. 2004, Gold et al. 2001). KM technology is recognized as the central enabler of KM processes, while the variety of knowledge management technology tools found in contemporary organizations according to Moffett et.al. (2004) can be grouped into three distinctive categories: collaborative tools, content management and business intelligence platforms. The literature research points that without the use of sufficient level of technology adequate to the requirements of organizations, accompanied by the existence of a supportive KM structure and culture, KM initiatives are destined to fail (Bechina and Ndlela 2008). As their effectiveness is difficult to measure they are usually justified through their positive impact on the competitive advantage which results into superior performance of organizations compared to their competitors. Two underlying assumptions derive from the argument. First, knowledge management affects competitive advantage and second, competitive advantage affects performance. Bothe need to be explored into more details before developing the final conceptual framework.

4. Competitive advantage, knowledge management and performance According to Porter (1985), a competitive advantage of companies cannot be seen as a separate entity from the performance of companies. Its presence results into the creation of quantified added value in relation to other competitors. Therefore, Porter’s definition although rather ambiguous in terms of what competitive advantage is, and whether the competitors come from within the industry, or from other industries as argued by the proponents of the Resource Based View (Barney 1991, Rummelt 1991), is an excellent definition of the term in relative terms as the same avoids limitations associated with the different theories of strategic management. The actual issue regarding the competitive advantage of companies is the question whether the same affects the performance of companies, an underlying assumption which has not been tested yet, and one which is essential for the strategic value of knowledge management in organizations. In this area, one cannot ignore Powell’s work (2001, 2002, 2003, 2007). He openly questions the work of researchers on competitive advantage under the assumption that the same results into a superior performance. Approaching the issue of competitive advantage from a logical and philosophical understanding, Powell (2001) argues that there is an ontological functional equality between the terms, although these two terms are not the same, nor is the competitive advantage a necessary precondition for superior performance. According to Powell (2001), the competitive advantage can influence the performance of companies, but the same is not the only condition that it does. In line with this argument, Powell (2001) introduces the existence of competitive disadvantage arguing that sometimes companies with a competitive disadvantage can have a superior performance, which further serves to minimize the assumed causality: competitive advantage  superior performance. Durand (2002) in his critique of Powell (2001) introduces the concept of capable organization arguing that Powell’s (2001) logic is not fallible, but it needs to be extended in which case it can be said that the influence of competitive advantage in capable organizations will result in a superior performance. Powell (2003), however, successfully builds his argument further, approaching competitive advantage as a relative term in absolute contexts. In the process, Powell and Arregle (2007), make a strict divide of the competitive advantage from the organizational decision-making process and competencies through the introduction of the axis of mistake. This development makes Durand (2002) arguments on the capable organization significant for the role of knowledge management in the superior performance of companies. If the competitive advantage is a relative term as argued by Powell (2003), then why would the same not cover in its scope the decision-making capabilities of companies, and is it possible that in that case these capabilities become a competitive advantage of companies which although with a ‘competitive disadvantage’, achieve superior performance. The

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Andrijana Bogdanovska Gjurovikj discussion, therefore, leads back to the arguments of Durand (2002) on the scope and actual impact of competitive advantage, expanding the same and inferring that even capable organizations in context where they have no competitive advantage, based on optimal decision making may reach superior performance. Therefore, in certain contexts, these capable organizations need not to have any other competitive advantage to end with superior performance apart their capability to comprehend the external and internal context in which they operate and make optimal decisions. This capable organization is a one with superior knowledge management processes, where the use of the knowledge created, organized, and disseminated through the organization, regardless of the origin of the initial stimuli (external or internal), and combined with its other strategic resources creates a competitive advantage and results into a superior performance. The impact on KM platforms as structure, culture and technology, influence this causation, while the measurement of the superior performance is approached in through incorporating both, financial and non-financial performance of companies â&#x20AC;&#x201C; Figure 1. The competitive advantage however, as a term is always defined visa a vi competing organizations, which is why the presented model must be measured through the use of subjective measures i.e. managerial perceptions comparing the organisation with its competitors.

5. Conclusion Based on the presented discussion on the ontological accuracy behind the underlying assumption of all schools on strategy, i.e. that competitive advantage results into superior performance of companies, the paper argues in favor of the assumption, but only when knowledge management capabilities are introduced in the equation. In the process, the provided discussion outlines the way knowledge management impacts the development of the capable organization approached through the processes of knowledge use. Other KM processes as knowledge generation, organization, and dissemination initially outlined by Davenport and Prusak (2000) support the process, while the KM platforms as structure, culture and technology hold the potential to provide a favorable internal context. The conclusions are two-fold. First, the article argues that based on the lack of theoretical and empirical research on the impact of competitive advantage on superior performance of companies, and approaching the issue from a logical and philosophical understanding, only capable organizations are in a position to create superior performance. Second, these capable organizations are characterized with superior knowledge management processes which enable them to comprehend the external and internal concept in which they operate and make optimal decisions.

References Barney, J., (2001), â&#x20AC;&#x153;Is the resource-based view a useful perspective for strategic management research? Yesâ&#x20AC;?. Academy of Management Review, Vol 26, No.1,, pp.41-56.

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Andrijana Bogdanovska Gjurovikj Barney, J.B., (1991), “Firm resources and sustained competitive advantage. Journal of Management”,Vol 17, No.11, pp.99120. Bechina, A. and Ndlela N. (2008) “Success Factors in Implementing Knowledge Based Systems.”Electronic Journal of Knowledge Management Vol. 7. No.2, pp: 211 - 218. Bennet D, Bennet A.(2004), The Rise of the Knowledge Organization. Handbook On Knowledge Management 1: Knowledge Matters [serial online]. pp:5-20. Davenport, T.H. and Prusak, L. (2000). Working knowledge: how organizations manage what they know. Harvard Business Press, Harvard. Gold, A. H., et.al. (2001). “Knowledge management: An organizational capabilities perspective”. Journal of Management Information Systems, Vol.18. No.1,pp.185-214. Grant, R. M. (1996). ‘Prospering in dynamically-competitive environments: organisational capability as knowledge integration.Organisation Science, 7, 4, 375–87. Khalifa, M. and Liu, V. (2003). Determinants of Successful Knowledge Management Programs. Electronic Journal on Knowledge Management. Vol.1. No.2, pp:103-112. Available from:http://www.computing.co.uk/computing/news/2202746/thomas-cook-signs-accenture. [Assessed:28.12.2010]. Long, D. and Fahey, L. (2000). Diagnosing cultural barriers to knowledge management, Academy of Management Executive, Vol. 14 No. 4, pp. 113-127. Lubit, R. (2001), “Tacit knowledge and knowledge management: the keys to sustainable competitive advantage.”, Organizational Dynamics, Vol. 29 No. 4, pp. 164-78. Moffett, S. et.al. (2004) “Technological utilization for knowledge management.” Knowledge & Process Management, Vol.11, No.3, pp: 175-184. Nielsen, B. B. (2005), “Strategic Knowledge management research: tracing the co-evolution of strategic management and knowledge management perspectives.”, Competitiveness Review, Vol. 15, No.1, pp:1-13. Nielsen, B. B. (2005). “Strategic Knowledge management research: tracing the co-evolution of strategic management and knowledge management perspectives.” Competitiveness Review Vol.15, No.1, pp:1-13. Nonaka I, and Takeuchi H. (1995), The Knowledge-Creating Firm. Oxford University Press, New York. Nonaka, I. (1994), “A dynamic theory of organizational knowledge creation.”, Organization Science, Vol.5, No.1, pp:14-37. Nonaka, I. and N. Konno. (1998), “The Concept of ‘Ba’: Building a Foundation for Knowledge Creation,”California Management Review, Vol.40, No.3, pp:116-132. O’Sullivan K.J, and Azeem S.W (2007) “An Analysis of Collaborative Group Structure Technological Facilitation from a Knowledge Management Perspective” The Electronic Journal of Knowledge Management Vol. 5 No.2, pp 223 -230 O’Sullivan K.J, and Azeem S.W (2007). An Analysis of Collaborative Group Structure Technological Facilitation from a Knowledge Management Perspective. The Electronic Journal of Knowledge Management . Vol.5, No.2, pp:223 – 230. Polanyi, M. (1966), The Tacit Dimension, University of Chicago Press, Chicago Porter, M. (1980). Competitive Strategy. New York, Free Press. Porter, M., (1985). Competitive Advantage: Creating and Sustaining Superior Perfromance. New York, NY: The Free Press. Soley, M. and Pandya, K. (2003). Culture as an Issue in Knowledge Sharing: A Means of Competitive Advantage. The Electronic Journal of Knowledge Management . Vol.6 No.2 pp:125 – 134. Available online at www.ejkm.com. [Assessed:05.01.2012]. Stankosky, M. and Carolyn B. (2001). A System Approach to Engineering a Knowledge Management System. In: Knowledge Management: The Catalyst for Electronic Government, edited by R. C. Barquin, A. Bennet and S. G. Remez. Vienna, Virginia: Management concepts. Teece, D. et.al. (1997),”Dynamic capabilities and strategic management”, Strategic Management Journal, Vol.18, No.7, pp. 509-533. Powell TC. (2001).”Competitive advantage: logical and philosophical considerations”. Strategic Management Journal Vol.22 No.9: pp: 875–888. Powell TC. (2002). “The philosophy of strategy”. Strategic Management Journal. Vol. 23, No.9: pp: 873–880. Powell TC. (2003). “Varieties of competitive parity.” Strategic Management Journal Vol. 24. No.1, pp: 61–86. Von Krogh, G. et.al. (1998). Knowing in Firms: Understanding, Managing and Measuring Knowledge. London:SAGE. Chomsky, N. (1957). Syntactic Structures. Mouton and Co., The Hague. Minsky, M.L. (1975). A framework for representing knowledge. In P. Winston (Ed.), The Psychology of Computer Vision, pp. 211-277. McGraw-Hill, New York. Alternative version is in [Haugeland (1997)], and reprinted in [Brachman and Levesque (1985)]. Simon, H. (1996). The Sciences of the Artificial. MIT Press, Cambridge, MA, third edition. Choo, C.W. (1998). The Knowing Organisation – How Organisations Use Information to Construct Meaning, Create Knowledge, and Make Decisions. Oxford University Press: New York Boisot, M. (1998). Knowledge Assets: Securing Competitive Advantage in the Knowledge Economy.Oxford: Oxford University Press. Prahalad, C. K. and Hamel, G. (1990) The core competence of the corporation. Harvard Business Review May/June, 79-91. Wiig, K. M. (1993). Knowledge Management Foundations: Thinking about Thinking – How People and Organizations Create, Represent and use Knowledge, Schema Press, Arlington, TX. Liebeskind JP. (1996). “Knowledge, strategy, and the Theory of the Firm.” Strategicc Management Journal. Winter Special Issue 17: pp:93-107.

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Andrijana Bogdanovska Gjurovikj Leonard-Barton, Dorothy (1995), Wellsprings of Knowledge: Building and Sustaining the Sources of Innovation, Harvard Business School Press. Simon, H.A. (1995). “A Behavioral Model of Rational Choice.” The Quarterly Journal of Economics, Vol. 69, No. 1. (Feb., 1955), pp. 99-118. Knapp, E. and Yu, D. (1999). “Understanding organizational culture: how culture helps or hinders the flow of knowledge.” Knowledge Management Review, 7, pp:16-21. Powell, TC and Arregle, J-L (2007). “Firm Performance and the Axis of Errors,” Journal of Management Research, Vol.7, No.2, pp: 59-77.

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The Importance of Knowledge Waste for Intellectual Capital Management and Enterprise Performance Helio Aisenberg Ferenhof and Paulo Mauricio Selig PPGEP, Universidade Federal de Santa Catarina, Florianópolis, Brazil helio@igci.com.br selig@deps.ufsc.br Abstract: Companies look to be ahead in its segment, or remain in the market. Therefore, it is necessary to obtain competitive advantage, going to have to continuously improve their management strategies. One of these is directly related to how innovative production processes are and how well they are managed, resulting in the launch of new portfolio of projects equal or superior to those previously produced. For this, intellectual capital and its knowledge are fundamental and its wastage management is a factor that could be determinant. Scholars sustains that the effective management of people planning must be a part of overall corporate strategy. But, how about the knowledge waste? In this context, the research highlight the problem associated with the performance of intellectual capital, may be impacted, or not by the use of knowledge in part or in whole process, the loss and / or forgetfulness, or use knowledge that does not add value for the company, its customers consequently. This work researched the databases: EBSCO; Emerald; Compendex; Scopus; ISI; and Wiley that occasioned in 506 articles which were systematically analysed, resulting in this theoretical article that presents the main references on the subject and a scientific essay regarding the importance of managing the intellectual capital knowledge waste. We concluded that managing the forms of knowledge waste, it is expected an improvement of performance and competitive advantage. Keywords: intellectual capital, intangible assets, knowledge waste, waste of knowledge, knowledge management, portfolio management

1. Introduction In the new era of knowledge‐based companies, if ones don’t manage intellectual capital (IC) and the competitors do, then they will be beaten because the latter will get better results, being more competitive (Elias & Scarbrough, 2004). Intangible resources are more likely than tangible resources to produce a competitive advantage (John & Suresh, 2011). The recognition that much of the added value created by companies is becoming more and more conditional on intangible assets other than physical capital has stimulated a vast literature resulted by researches in the area of intellectual capital, human capital and intangible assets, highlighting the importance of IC management to add value and competitive advantage (Bontis, 2001; Bontis & Fitz‐Enz, 2002; Carson et al., 2004; Edvinsson, 2000; Edvinsson & Malone, 1997; Elias & Scarbrough, 2004; Roos & Roos, 1997; Stewart & Ruckdeschel, 1998; Sveiby, 1997; Wright & Kehoe, 2008). Companies look to be ahead in its segment, or remain in the market. Therefore, it is necessary to obtain competitive advantage, going to have to continuously improve their management strategies (Ahuja & Ahuja, 2012; Bassey & Tapang, 2012; Campbell et al., 2012; Chadee & Raman, 2012). One of these is directly related to how innovative production processes are and how well they are managed (Hsu & Sabherwal, 2012), resulting in the launch of portfolios of projects that develop goods and/or services with time and cost savings and quality equal or superior to those previously produced. For this, IC and its knowledge are fundamental (Ahmed et al., 2004; Ahuja & Ahuja, 2012; Andrzej & Marian, 2009; Carla et al., 2011; Davis & Walker, 2009; DeCarolis & Deeds, 1999) and its wastage management is a factor that could be determinant (Bauch, 2004; Ferenhof, 2011; Locher, 2008; Ward, 2007). Scholars sustains that the effective management of people means that manpower planning must be a part of overall corporate strategy, for corporate goals determines the tasks that must be performed. But, how about the knowledge waste? In this context, the research highlight the problem associated with the performance of IC, may be impacted, or not by the use of knowledge in part or in whole process, the loss and / or forgetfulness, or use knowledge that does not add value for the company, its customers consequently. The purpose of this paper as such is to develop a model to measure the knowledge waste in order to improve the performance of enterprises. First it provides a background on the aspects related with IC, wastes and enterprise performance. Second it explains more deeply the concept of knowledge waste. Third the main results and gaps identified by a systematic literature review. Forth the conceptual model to mitigate knowledge wastes. Fifth the research approach to test the conceptual model. Finally the conclusions are drawn and further research recommendations are made.

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2. The context – portfolio, intangible asset, knowledge waste and enterprise performance IC can be the most powerful asset of an enterprise in promoting value and competitive advantage, being the most important organizational resource (Kaplan & Norton, 1996; Moon & Kym, 2006). IC is classified by Stewart (1997) as a composition of: Human Capital, Structural Capital and Customer Capital. The most accepted and spread classification is the one by Sveiby (1997) that breakdown IC into three categories: Human Capital, Relational Capital and Structural Capital. He defines human capital as “the capacity to act in a wide variety of situations to create both tangible and intangible assets”; structural capital as “patents, concepts, models, and computer and administrative systems”; and relational capital as “relationships with customers and suppliers”. But what is IC? Stewart (1997) defines IC as: [...] the sum of everything everybody in a company knows that gives it a competitive edge […] IC is intellectual material, knowledge, experience, intellectual property, information […] that can be put to use to create wealth. Knowledge is an intangible asset of IC, and now a day is the most important asset to be management to archive competitive advantage (Bueno et al., 2003). The Human Capital consists of tacit and explicit knowledge, and this intangible assets can me measure by individual competence. At the same time, Structural Capital comprises knowledge: explicit, systematic and internalized measure by structural competence, on the other hand Relational Capital consists of the value of the relationship of the entire corporation with the external ambient, as can be seen at Figure 1. Thus, IC is the possession of knowledge, applied experience, organizational technology, customer relationships, and professional skill that provide a competitive edge in the market (Edvinsson and Malone, 1997). IC also captures both stocks and flows of an organization’s overall knowledge base (Bontis, 1999; Bontis et al., 2002). The indicators shown here reflects the IC that already exists on the enterprises and their portfolios, but not the lost of knowledge on It.

Source: Bueno, Arrien & Rodríguez (2003). Figure 1: From components to indicators It can be seen then that the existing IC models deal with the measurement of the elements that build directly competitive advantage. But how‐to measure the knowledge waste? To understand the issues related to the knowledge waste and the impact into the competitive advantage, this study was based in the portfolio management perspective. The relationship between IC and performance in project management perspective was presented by Demartini and Paoloni (2011). They sustains that project managers’ knowledge and experience are considered important in determining project outcomes. The PMI (2013) explains Portfolio management as: the coordinated management of one or more portfolios to achieve organizational strategies and objectives. Portfolio management produces valuable information to support or alter organizational strategies and investment decisions. And a portfolio transforms the strategic objectives into actions (PMI, 2013). The portfolio then, will be our unit of analysis.

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3. Knowledge waste The meaning of knowledge waste is defined by Ferenhof (2011) as any failure in the process of knowledge conversion, better known as spiral of knowledge creation of Nonaka and Takeuchi (1997), which the waste is presented in the following ways: reinvention, lack of system discipline, underutilized people, scatter, hand‐off, wishful thinking (Ferenhof, 2011), as detailed below. Reinvention is a type of waste that happens if the organization does not reuse the designed solutions, components, projects, experiences or knowledge acquired previously. Many companies and employees do not pay attention to the fact that instead of creating a new project from scratch, uncertainly, they can increase the chances of success by reusing previous knowledge, ie, parts or whole projects and/or process already designed, tested and approved, as well as their experiences throughout their conception (Bauch, 2004; Ferenhof, 2011). Lack of system discipline covers a number of factors related to clarity of objectives outlined in the system: goals and objectives obscure; unclear rights, roles and responsibilities; obscure rules; poor discipline of schedule; insufficient willingness to cooperate and; incompetence or lack of training (Bauch, 2004; Ferenhof, 2011). Underutilized people, employees are not using their skills and expertise completely. Often are given very limited roles and responsibilities to them, when in reality, they could assume much more if the process was designed effectively, not wasting knowledge (Locher, 2008; Ferenhof, 2011). Scatter, are the actions that make knowledge become ineffective by flow disturbance, basically disrupting the interaction required for teamwork. This category has two sub‐categories: communication barriers and poor tools.

Communication barriers directly prevent knowledge flow occurrence. They include: a) physical barriers such as distance, computational incompatible formats, etc.; B) social barriers such as the corporate class systems and management behaviour that prevent the flow of communication and knowledge c) skill barriers: people who do not transform data into usable knowledge (Ward, 2007).

Poor tools refer to the fact that the tools should support for the flow of knowledge and not stifle the process, as users assume that these tools are the only solution. These developers seek to take shortcuts, copying unsuitable operating modes, causing failures by forcing the use of tools without proper analysing their relevance. Due to the insistence of using poor tools, the process ends up in a death spiral, the more one tries to improve the process are the worst failures (Ward, 2007; Ferenhof, 2011).

Hand‐off occurs when one separates knowledge, responsibility, action and feedback. It results in decisions made by people who do not have enough knowledge to make the decision effectively or do not have the opportunity to accomplish it. As subcategories we have: useless information and waiting.

Information is useless if they do not help to understand the customer or other aspects of integration. They do not: add value to the flow; innovate; provide meaningful data for decision‐making; and are usually created only to fulfil someone’s desire. (Ward, 2007).

Waits, normally occurs by establishing a standard conventional sequencing of activities, which creates a batch processing, causing: slow processes, a single path instead of multiple streams of information, a large variation of work causing the waste of scatter, spread (Ward, 2007).

Wishful Thinking means to follow the subject’s own reasoning, based on desires rather than on facts or rationality, or the decision making based on one’s own desires for reality. For Ward (2007) this means operating in the dark, blindly making decisions without consistent data. It can be divided in: testing to specifications and discarded knowledge.

Testing to specifications is a practical conventional pattern. These cannot highlight whether a good or service is ready for commercialization, it is statistically impossible to test enough to be confident that they will have zero defects (Ward, 2007).

Discarded knowledge happens for a number of reasons: teams and superior focus on the product or service launch, leaving aside the capture of knowledge; tests with the specifications do not say much so it can be used next time and, above all, few know how to turn data into usable knowledge (Ward, 2007).

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Helio Aisenberg Ferenhof and Paulo Mauricio Selig Ferenhof (2011) elucidates that by seeking to eliminate knowledge waste, it is expected that this flow deliver benefits and results to stakeholders in a more efficient and effective, with a focus on value considering the system as a whole: processes, people and technology. In Figure 2, we expose the view of the knowledge waste relating to the conversion knowledge process, called spiral of knowledge waste.

Source: Ferenhof (2011). Figure 2: Spiral of knowledge waste

4. Literature review Bibliometric and systematic review proceeding was followed. It makes viable to organize the set of publications of a specific field. The resulting bibliographic portfolio allows to identify authors, their relationships and trends; it is one of its major contributions to science (Spinak, 1996). The bibliographic research will comprise three different stages: data collection, analysis and synthesis of results. These three stages will implicate the following procedures:

Criteria for choice and database fields;

Terms used in the research;

Manage and treat the references collected;

Criteria for articles selection: in addition with the readings of title, abstract, and location of keywords in the body of the text, including selection by reading to get the articles that have addressed the terms concerning: measurement of performance or competitive advantage of intellectual capital;

Criteria for the systematic analysis: establish according to the model by Bardin (2011).

The data analysis and synthesis results were executed beginning with the creation of tables and graphics, presented below. The research resulted in 506 articles, 491 of them without duplicates, which were systematically analysed. The exact numbers of publications that was returned by each database can be seen in Figure 3.

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Source: Authors. Figure 3: Database results Only 160 of the 491 articles were aligned to our main research topic by reading the title, keywords and abstract. 24 of the 160 weren’t available the full text download. Resulting in 136 articles for full text reading analysis. After the analysis, 14 were not aligned with the theme, and were discarded from the portfolio, resulting in 122 relevant articles. Figure 4 shows the years of publications of the resulting portfolio. 19 14 20 15 10 5 0

10 1

2007 2008 2004 2005 2006 2000 2001 2002 2003 1999 1997 1977 1996 1971 1974 1976 1

2009 2010 2011 2012

Source: Authors. Figure 4: Distribution of portfolio articles per year

4.1 Meta‐Analisys conclusions The main results of articles analyzed can be summarized as:

It has significant correlations among the relational capital and competitive strategy (Acquaah, 2007).

The quality of IC components directly affects companies’ financial performance, profit growth rate and competitive position (Andrzej & Marian, 2009; Campbell et al., 2012; DeCarolis & Deeds, 1999; Hsu & Sabherwal, 2012).

Human Capital has positive influence on portfolio, program and project performance, and so into the company performance as a hole (Brown et al., 2007; Demartini & Paoloni, 2011).

Social capital can be built through delivering a project using a relationship‐based approach and how this may be achieved using the suggested CMM template and protocols (Davis & Walker, 2009).

Value creating knowledge, intangible assets, should lead to competitive advantage (Campbell et al., 2012; Carla et al., 2011; Carson et al., 2004; Davis & Walker, 2009; DeCarolis & Deeds, 1999; Elias & Scarbrough, 2004; Eric, 2010; John & Suresh, 2011; Lavie, 2007; Moon & Kym, 2006; O'Donnell et al., 2009).

Companies that fail to recognize IC as a factor are prone to failure in business and in competition (Dorweiler & Yakhou, 2005).

You need to have indicators that send signals about the effectiveness and profitability of the institution and in its assessment indicating whether she will remain competitive. In the construction of indicators is required to keep in mind the customer's perspective and competition. (Estrada Munoz et al., 2010).

IC is difficult for competitors to imitate even when employees are hired away because the knowledge is specific to the original work environment and therefore cannot add similar value in a different work environment (Hatch & Dyer, 2004).

What can be extracted is that the competitive advantage and the performance success of one enterprise are direct rated how well your IC is managed. It indicates an opportunity for knowledge management to eliminate

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Helio Aisenberg Ferenhof and Paulo Mauricio Selig or mitigate the knowledge wastes. As can be seen by the recommendations for future research from the bibliographic portfolio. Table 1: Recommendations for future research indicated by the bibliographic portfolio Author Abeysekera (2012)

Acs et al. (2007)

Ahmed et al. (2004)

Antonelli et al. (2010) Bassey and Tapang (2012)

Chadee and Raman (2012) Cormier et al. (2009) DeCarolis and Deeds (1999) Iñaki (2002)

Juma and Payne (2004)

Moon and Kym (2006)

Mura and Longo (2012)

Wright and McMahan (2011)

Gaps for research Examine how corporate governance attributes predict each human capital resource item separately. Also could examine the governance attributes in this study with firms that place less reliance on independent directors. The communication of human capital can comprise narrative, visual and numerical types of disclosure. Sort out the consequences of these results for intelligent policies to enhance new‐firm survival, encourage more successful new‐firm formation, and to overcome any important factors discouraging local growth. Empirically test the developed hypotheses, which linked IC indicators to organizational performance. Collect similar data longitudinally so that hypotheses can be tested by accounting for time lags in changes among IC metrics and organizational performance measures. A more exhaustive and robust study of human capital measurement conceived in a labour demand perspective. Researchers should address the following research questions: 1. How does one educate the users of financial statements about the usefulness of measuring human resource value and create more awareness? 2. Who should drive the measurement of human resource value? 3. Which model for the recording of human resource value is most suited to the Nigerian organizations? Extend the enquiry to a broader sample of firms from different sectors, including the manufacturing sector, to gain further insights into the effects of talent management and external knowledge on organisational performance, within a cross‐country framework. Voluntary disclosure should take into account attributes of disclosure and not only its extent. Empirical research needs to be completed to enhance our understanding of the relationship between organizational knowledge and firm performance. Examine the influence of ICs on business start‐up performance. In this study, we have intangible asset examined the relationship, not the causality, between IC elements and firm performance on a sample with limited size and characteristics. Relating IC to performance should attempt to develop new, more adequate or complete measures of IC. Although both practitioners and scholars would desire a quantifiable and readily available measurement of IC, the EVA and MVA variables do not seem to differentiate themselves enough from normal measures of performance to be used as a predictive indicator. The use of more qualitative sources seems to be more appropriate to determine such “un‐quantifiable” and intangible variables. Modify and extend our framework making it more comprehensive or more suited to specific industries. Corporate reporting and internal management systems must therefore be more holistic allowing investors and managers to evaluate the performance of the total value creation system which includes its production factors, assets, processes, and procedures. Continue to analyse the relation between IC and organizational performance, for example exploring the mediating effect of individual performance in the IC organizational performance relationship. Development the basis for a standardized internally generated intangible assets measurement tool. While the concept of human capital may be 40 years old, its treatment in organisational research is in an infant stage. Needs to study the role of human capital in firm competitive advantage.

Source: Authors. Reviewing and analysing the models of IC, was identified a gap that none of them (Bontis, 2001; Bontis & Fitz‐ Enz, 2002; Bueno et al., 2003; Edvinsson, 2000; Edvinsson & Malone, 1997; Kaplan & Norton, 1996; Roos & Roos, 1997; Stewart & Ruckdeschel, 1998; Sveiby, 1997) presented indicators directed related to manage the knowledge waste. Regarding that, we propose a conceptual model that deals with this gap.

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5. Conceptual model The conceptual model was based in three propositions: 1) in the perception that the knowledge is the most important resource of any enterprise (Choo, 1996; Davenport & Prusak, 1998; Nonaka et al., 2000); 2) is by knowledge that the IC add value to the enterprises, generating and supporting competitive advantage (Ahmed et al., 2004; Bontis, 2001; Bueno et al., 2003; Carson et al., 2004; Davis & Walker, 2009; Edvinsson & Malone, 1997; Kaplan & Norton, 1996; Roos & Roos, 1997; Stewart & Ruckdeschel, 1998; Sveiby, 1997) and; 3) the knowledge waste, wasn’t measured by any of the previous (intangible assets / intellectual capital) models. For each of the three categories of IC in Figure 1, we associated the knowledge wastes proposed by Ferenhof (2011) Figure 2. By this association (Figure 5), the listed knowledge waste may be occurring:

Human Capital: All of the six categories of knowledge waste.

Structural Capital: Reinvention, lack of system discipline, scatter, and hand‐off.

Relational Capital: Lack of system discipline, underutilized people, scatter, and hand‐off.

Key Performance Indicators (Knowledge Wastes Indicators), associated with each knowledge waste at each IC category must be created to the context that will be managed. As it’s exposed in Figure 5, Generic Model.

Intellectual Capital

Human Capital

Structural Capital

Rela onal Capital

Intangible Assets

Tacit and Explicit Knowledge

Knowledge explicited, systema zed and internalized

Value of the rela onship of the organiza on with the external ambient

Individual Competence

Structural Competence

Rela onal Competence

Reinven on, lack of system discipline, underu lized people, sca er, hand‐off, and wishful thinking.

Reinven on, lack of system discipline, sca er, and hand‐off.

Lack of system discipline, underu lized people, sca er, and hand‐off.

Knowledge Indicators

Knowledge Wastes

Knowledge Wastes Indicators

Source: Authors. Figure 5: Generic conceptual model ‐ knowledge wastes indicators path

6. Research approach to test the conceptual model For testing the conceptual model presented in the previous section, it adopts a qualitative approach because it involves understanding of an event in their natural environment; fieldwork and results in a product description (Merriam, 1998). We posit in‐depth case studies into portfolio management of intensive knowledge organizations, which will permit the researchers to investigate in detail the interactions between the phenomenon under study and the contextual factors (Yin, 2009). Case study research would hence serve the following purposes:

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Help validate and modify the constructs with clear conceptual definitions and the relationships between these constructs.

Help validate and modify the contextual organizational and other attributes, and uncover their influence in the specific context.

The researchers have identified a few organizations in Brazil and England for carrying out the case studies.

7. Final thoughts The competitive advantage and the performance success of one enterprise are direct rated how well your IC is managed. This work has sought to develop a generic model to measure and manage the knowledge wastes. We highlight the importance of managing them, by doing that it is expected an improvement of performance and competitive advantage. The adoption of the model can impact the efficiency of the entire enterprise in terms of capturing and sharing pertinent data about the knowledge wastes to the decision maker. The use of Key Performance Indicators associated with each knowledge waste at each IC category will help the decision maker bringing quality information to decide more precisely, allocating the IC resources in an effective way. It is expected that with continued use of the model to generate historical information related to the indicators of knowledge waste, tend to be more assertive the decisions. Hence, this article expects to promote future researches related with knowledge waste and IC performance.

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Paper presented at the 2010 2nd International Congress on Engineering Education: Transforming Engineering Education to Produce Quality Engineers, ICEED2010, December 8, 2010 ‐ December 9, 2010, Kuala Lumpur, Malaysia. Ferenhof, H. A. (2011). Uma sistemática de identificação de desperdícios de conhecimento visando à melhoria do processo de criação de novos serviços. . (Mestrado em Engenharia e Gestão do Conhecimento), Universidade Federal de Santa Catarina, Florianópolis. Hatch, Nile W., & Dyer, Jeffrey H. (2004). Human capital and learning as a source of sustainable competitive advantage. Strategic Management Journal, 25(12), 1155‐1178. doi: 10.1002/smj.421 Hsu, I. Chieh, & Sabherwal, Rajiv. (2012). Relationship between Intellectual Capital and Knowledge Management: An Empirical Investigation. Decision Sciences, 43(3), 489‐524. doi: 10.1111/j.1540‐5915.2012.00357.x Iñaki, Peña. (2002). Intellectual capital and business start‐up success. Journal of Intellectual Capital, 3(2), 180‐198. doi: 10.1108/14691930210424761 John, Dumay, & Suresh, Cuganesan. (2011). Making sense of intellectual capital complexity: measuring through narrative. Journal of Human Resource Costing & Accounting, 15(1), 24‐49. doi: 10.1108/14013381111125305 Juma, Norma, & Payne, G. Tyge. (2004). Intellectual capital and performance of new venture high‐tech firms. International Journal of Innovation Management, 8(3), 297‐318. Kaplan, Robert, & Norton, David P. (1996). The balanced scorecard: Harvard Business School Press. Lavie, Dovev. (2007). Alliance portfolios and firm performance: A study of value creation and appropriation in the U.S. software industry. Strategic Management Journal, 28(12), 1187‐1212. doi: 10.1002/smj.637 Locher, D. (2008). Value Stream Mapping for Lean Development: A How‐to Guide for Streamlining Time to Market. New York, NY: Productivity Press. Merriam, Sharan B. (1998). Qualitative Research and Case Study Applications in Education. Revised and Expanded from" Case Study Research in Education.": ERIC. Moon, Yun Ji, & Kym, Hyo Gun. (2006). A Model for the Value of Intellectual Capital. Canadian Journal of Administrative Sciences / Revue Canadienne des Sciences de l'Administration, 23(3), 253‐269. doi: 10.1111/j.1936‐ 4490.2006.tb00630.x Mura, Matteo, & Longo, Mariolina. (2012). Developing a tool for intellectual capital assessment: an individual‐level perspective. Expert Systems, n/a‐n/a. doi: 10.1111/j.1468‐0394.2012.00650.x Nonaka, Ikujiro, Toyama, Ryoko, & Konno, Noboru. (2000). SECI, Ba and Leadership: a Unified Model of Dynamic Knowledge Creation. Long Range Planning, 33(1), 5‐34. doi: doi: DOI: 10.1016/S0024‐6301(99)00115‐6 O'Donnell, Loretta, Kramar, Robin, & Dyball, Maria Cadiz. (2009). Human capital reporting: Should it be industry specific? Asia Pacific Journal of Human Resources, 47(3), 358‐373. doi: 10.1177/1038411108099293 PMI. (2013). 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Dissemination of Professional Routines, a Case Study in the Automotive Industry Johanna Frances1,2, Stéphane Robin2 and Jean‐Louis Ermine1 1 Télécom Ecole de Mangement, Evry, France 2 PSA Peugeot‐Citroën, Poissy, French johanna.frances@mpsa.com stephane.robin@mpsa.com jean‐louis.ermine@telecom‐em.eu Abstract: This paper aims at studying the mechanisms that contribute to the dissemination, the transmission and the creation of professional knowledge. It is based on observations led within a French large company of automobile industry. Our approach was empirical. We carried out some observations in different technical fields. What we observed is how people, individually or collectively, gather the information available in their environment, how they transform this information progressively into knowledge, how eventually they apply this knowledge and embed it in what we called professional routines. This article deals with a model of the professional routines acquisition and transmission, both from an individual point of view and from a collective point of view. The model we built is divided into five parts: Learning, Experiment, Embodying, Routinization and Mastering. Individual and collective routinization is a key for understanding the learning phenomenon and the transmission and dissemination of knowledge. Routinization can be defined as a regular and mechanical act, being more the result of a habit than of a thinking. Acting routinely is acting without thinking about the act. The routinization process is core in our daily practices. In a professional environment, routinization refers to know‐how which became “innate” as a result of repeated experiences. Routinization describes a process based on knowledge acquired from trial and error experiences. It is the result of a more or less long confrontation with the environment that allows to understand the world of this new knowledge. It helps to develop skills, hand‐tricks, tips and knowledge. In a factory, routinization is essential for the organization of the manufacturing, but can lead to blockages: difficulties to modify the working processes, the dissemination of knowledge across the company as well as to new comers. Routinization is intrinsically conservative. We described a comprehensive process that leads from one step to the other. This process is by nature non‐linear, cyclic, and retroactive. The aim of our work is to confront the model

with reality. It was done using the observations we made, and we applied it in order to better understand the dissemination of tacit knowledge in a professional context. Keywords: tacit, learning, knowledge dissemination, routines, organizational learning, automotive industry, knowledge management

1. Introduction Research in educational studies (Carré P and Chardonnier O, 2004, Carré P., 2005, Brougère G and Ulmann A.L, 2009, Brougère G. and Bézille H., 2007) showed that most learning is acquired, developed and enhanced through daily work activities (Billet S., 2006). Informal interactions with peers extensively contribute to practical learning (Boud D. and Garrick J., 1999, Marsick V.J. and Volpe M., 1999, Skule S., 2004, Clarke N., 2005) and it is estimated that about 80% of one's knowledge is tacit (Tough A., 1971) and 60% to 80% of learning at work is made informally (Marsick V.J., 2006) Tacit knowledge is knowledge that we use in our daily lives without being aware. In this aim, we have developed a model allowing identifying different phases in individual and collective learning. The tacit dimension is of interest in numerous different disciplines, specifically education sciences, cognitive sciences, psychology, sociology, anthropology and knowledge management. Since many years, the development of knowledge management as a science and applied methodology within companies’ shows how important it is considered now to cope with on‐the‐job learning. It raises many questions: how to store and preserve knowledge in the organization. What are the individual and collective learning processes? How to enhance these processes? Is the knowledge dissemination constrained by the socio‐organizational context? Knowledge management aims at studying knowledge transfer processes and at designing methods for capturing and storing tacit knowledge in the business (Ermine J‐L. and Boughzala I., 2007). In the continuity of these studies we present here a set of preliminary results obtained in two areas of PSA Peugeot Citroën: painting and “diagnosticability”. This work aims at enhancing the individual and collective knowledge appropriation processes.

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Johanna Frances, Stéphane Robin and Jean‐Louis Ermine Learning theories focused on the question: how can an individual learn and hold what he learns? According to different authors, learning can be based on conditional reflexes (Pavlov I., 1934), or be the mirror of an environment (Watson J.B., 1913). For other authors, we learn by doing, by experience. According to Skinner (Skinner B.F., 1953) for example:

We learn by doing: a repeated action that build skills and habits.

We learn by experience. However, entering a new environment does not necessarily imply learning. Everybody shall re‐invent for himself the whole set of knowledge existing within this new environment, and experiment it. As an example, the knowledge of a phenomenon cannot replace personal experience. In other words, knowing that flowers exist does not replace seeing, smelling and touching flowers.

We learn by trial and error. That’s how we enhance our knowledge of an object, its possibilities and limits.

th The debate about the individual cognitive functioning was launched at the beginning of the XX century (Edward C.Tolman (1886‐1956); Max Vertheimer (1880‐1943), Kurt Koffka (1886‐1941) and Wolfgang Köhler (1887‐1967)) and new theories appeared. For these authors, knowledge must necessarily be rebuilt by the learner. It is done through the equilibrium of cognitive structures, as an answer to environmental solicitations and constraints (Piaget., 1975). It can also be said that knowledge acquisition is driven by a process that goes from social (inter‐individual knowledge) to individual (intra‐individual knowledge). Culture and environment play a significant role in individual development (Lev Vygostky.,1896‐1934) and each individual possesses a set individual traits, both cognitive and emotional, corresponding to fundamental ways of striving‐and‐thinking that shape his/her perceptions, images, and his judgments regarding his/her personal world” (Allport G., 1937 cited by Pervin, L.A; John O.P, 2004) Thus the individual is a thinking subject (cognitive and emotional), who creates its own representations and reality conceptions using what he observed and experimented. He has the ability to build, deconstruct and rebuild on a daily basis his knowledge, always incorporating new knowledge. The knowledge can have different characters: explicit, tacit, implicit, fortuitous, formal, informal. Even if each of these notions has a proper definition, we tend to gather on one hand the knowledge of the “hidden sphere” (tacit) and on the other end the knowledge of the “visible sphere” (explicit). Explicit knowledge is knowledge converted into information, which is in essence explainable and elicitable through language, reasoning and images. It is possible to transform it physically into a material support : procedures, white papers, intranet and other modus operandi (Grundstein M., 2003). According to Polanyi this knowledge which we cannot tell is " tacit knowledge" (Polanyi M., 1962). The notion of tacit knowledge refers of knowledge which we possess but which we are not capable of putting into words. This knowledge is hidden into intuition, knacks, tricks of the trade, resourcefulness, skills, know‐how, and a kind of base of practical knowledge acquired in the execution of the action. According to Polanyi “There are things that we know but cannot tell” (Polanyi M., 1966). Tacit knowledge is everywhere. Because they aren’t much visible they are difficult to formalize, to observe and to transfer (Ermine J‐L. and Boughzala I., 2007) .

2. Methodological This paper is based on field surveys and empirical data analysis. We chose to study professional practices through an empirical and comparative approach. Two types of surveys were used: qualitative exploration surveys and prospective surveys. The first type enables to deeply analyze practices, strategies, groups’ values. The second type reveals emerging tendencies among the diversity of observed behaviors.(Alami S. and Desjeux D., 2009). In order to expand our data we used different sources:

In the first step “the activities of the observer are made public from the beginning. The observer can therefore have access to a wide range of information and even to secrets if it is known that confidence will be respected". In a second step, " the researcher tries hard to play a role and to acquire a status within the group or the institution that he is studying” (Lapassade G., 1990).

In this objective, we participated to a number of meetings (observational empirism), taking the role of a worker on painting production chain (experimentational empirism)

We had many formal and informal conversations (about 80) and collected impressions, feelings, explanations, criticisms, sometime secrets.

We used two successive approaches: the first being dedicated to description, (linked to observation) and the second dedicated to the establishment of relationships among these observations.

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Johanna Frances, Stéphane Robin and Jean‐Louis Ermine According to YIN, Comparative Case Studies are: “an empirical investigation which studies a contemporary phenomenon in its real‐life context, where the limits between the phenomenon and the context are not clearly obvious” (Yin R.K., 1984, 2008). The aim of this comparison being to reveal constants and divergences, we chose two very different professional fields within the same industrial organization: In the Painting field goes from the very expertise of material and processes to the end operations of applying the painting to the car bodies. We mainly observed factory workers on production sites. Painting a car involves many different techniques and jobs (protecting the body from rust, applying lacquers and paintings, applying decors…) In the Diagnosticability is a very transversal specialty. The positions in this field are characterized by an environment of engineering consulting with a majority of executive‐engineer population. It implies to work in close collaboration with other business fields having an impact on the development of a specific function. Diagnosticability aims at defining tools and methods for identifying defective parts of sold cars. Thus every part of the car is concerned, and suppliers may be deeply involved in the process. Employees in this field often have at the same time a direct hierarchy and a functional hierarchy. Our research is based on Grounded Theory. We actually started from field data collection in order to develop a model grounded on knowledge management theories. The model developed and presented in our paper was then put in perspective in comparison to the reality of our research field in order to verify our model (inductive approach). We are talking a qualitative approach of case study. The case study is “ a particular technique of harvesting, formatting and treating the information which tends to render an account on evaluative character and complexes phenomenon involved in a social system moved by its own dynamics” (Collerette P., 1997). Our approach is at the crossroad of inductive and deductive approaches.

3. Model foundations From our case studies and from our diverse readings we formulated a first model. We decomposed this mechanism into five essential phases in the process of acquisition of the knowledge: Learning, Experiment, Embodying, Routinization and mastery. We emphasize the fact that the analysis of this process is based on first analyses of our case studies. Each and every new data submitted to an individual goes through this process and evolves to a knowledge acquired and ready to be transferred. This process is cyclic, retroactive, and is not linear. Furthermore, the duration of the different phases is variable, depending upon people or group of people. As an example, we are able to simultaneously experiment and learn one or several knowledge’s.

Figure 1: Process of acquisition of the know

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Learning

This phase is essential. That’s where the individual gives sense to the data he learns. Data are raw facts, resulting from a perception process, and rendered visible through a symbolic system. As soon as data have acquired a meaning, they become information which is subject to bring new knowledge (Arsac J. 1970). The knowledge can define itself as a set of three imbricated components: information, sense and context. So the knowledge is of the information (a set of messages produced by a system), taking a specific sense in a specific context (Ermine J‐L. and Leblanc B., 2007) In the Painting, the apprenticeship phase is made through a two days training. First day is dedicated to dexterity and second day to norms and standards. In the Diagnosticability field, the apprenticeship phase is pursued through years of higher or specific education, where the acquisition of the knowledge is tested on numerous occasions. In this first phase, the explanation holds an important place. “Knowledgeable individuals” are in charge of spreading the theoretical pieces of knowledge that others need to know in order to be able to communicate with co‐workers and stakeholders. Still, knowledge is disseminated using formal presentations made in institutionalized places.

Experiment

The phase of experiment is the application of the knowledge by the learner. Knowledge application cannot take place without integrating the received information into reflexion patterns. This operation associates the learner in relation to this new knowledge with a guide, which may be virtual or physical, or the learner may be alone. This experiment leads our learner to reproduce or gesture, an intellectual or manual operation. The learner goes through testing, he tries out and produces errors. This process is going to allow the individual to improve one’s knowledge without distorting the nature but by integrating it. This phase is closely linked to the tacit sphere: oral transmission, gestures acquisition, reflexion building, it is in this operation that we see being born knacks, skills, abilities, the tricks of the trade. In the Painting, the phase of experimentation takes place when the learners end up alone to realize the operations linked with the task they have to perform. The number of errors they produce during this phase lead them to rectify their movements, to acquire them knacks, the abilities, and their know‐how, know what to do, know how to make. Gesture can be modified according to the body’s flexibility and limits to which it is accepted in standard work procedures. In operational jobs, the acquisition of the body movements in a workstation is rather fast. Indeed, the learner executes during seven hours a day the same gesture, the experiment is particularly dense and the acquisition of the capacities is very fast. In these type of positions, the turn‐over rate is low, as once the gesture is acquired, there is few chances that it will be further modified – except if the working procedure is to be changed. In the Diagnosticability, the phase of experimentation follows the trial and errors produced by the learner performing its task. The managers are the persons in charge of the quality, they give advice, criticisms and lines of conduct. The diverse team meetings are also a space where improvement of the knowledge and the know‐how take place. It is a place of oral transmission, as the team approaches the subjects by using a specific language which is bound to the business and their jobs. During this phase, the learner is going to perfect his/her approach, methods, language in order to be in compliance with the requirements of the job. The jobs singularity in the diagnosticability field relies on the fact that experience is a continuous process. It is a question here of implementing knowledge through the action, named the “Knowing in action" (Rix‐Lièvre G. and Lièvre, 2012). While experiencing, the individual is going to acquire at the same time tacit and explicit knowledge and will be confronted to “Incidental learning”. “ Incidental learning is defined as a byproduct of some other activity, such as task accomplishment, interpersonal interaction, sensing the organizational culture, trial‐and‐error experimentation, or even formal learning […] Incidental learning, on the other hand, almost always takes place although people are not always conscious of it” (Marsick V.J. and Watkins K.E., 1990). Incidental learning is a learning outcome resulting from an expected event which will lead the learner to perfect his/her knowledge and technique. Indeed, the learner acquired a knowledge base through experience to be autonomous in his operations through newly perfected gestures. Logic of reflexion is still uncertain, as

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Johanna Frances, Stéphane Robin and Jean‐Louis Ermine the learner still lacks of systematic knowledge. In this regard, we have highlighted in our model certain types of rare experiences which lead to a direct mastering of the new learning. It is for example the case with fire. A children who would not know anything about fire, would touch it and immediately integrate the effects of this object (cognitively and physically), even without knowing what it exactly is.

Embodying

This phase relates to the appropriation of knowledge. The individual builds a set of components around this knowledge. In other words, he/she is knowledgeable about its use, is able to apply it at any time or in any environmental setting. We differentiate knowledge from knowing. The knowledge is an action or an act of representation, of making sense and/or contextualizing an object. Knowing evokes the capacity of an individual to use in an optimal way the knowledge in order to establish and reach desired goals. It is equivalent to cognitively apprehend a situation or object, both conceptually or through organizing a rational reasoning. This phase to the embodying is the ability apply the know‐how. In the Painting, the acquisition is guided by previous experience. During this phase, the learner memorizes gestures to be reproduced. The body movements can be modified according to own acquired capacities and limitations. It is because the knacks and abilities acquired by the "knowledgeable individuals" are not always identical. For example a left‐handed person will not make the same movement on the same operation as a right‐handed person. The wrist flexibility of a given actor can allow a better operational success than the one of another. The actor knows what to do, how to do it, when, and why. In the Diagnosticability, the appropriation begins with training courses made during the studies and by internships, which act as first experiences in the field. In this case, the learner will be supervised by his manager and by the whole team. Similarly, the actor knows what to do, how to do it, when, and why. Here we enter what Nonaka named the interiorization (Nonaka I., Toyama R). The learners transform explicit knowledge into tacit knowledge. This transmission of knowledge is made very often in informal contexts. This phase is tacit and individual. It is therefore possible that an individual decides to think about characteristics related to this knowledge (meta‐knowledge).

Routinization

Routinization is an interesting phase as it allows to better understand obstacles linked to knowledge transfer and dissemination. The routinization refers back to a know‐how, which, because it was experimented multiple times, created virtuosities. Know‐how thus became an “innate” act. The individual is reproducing a gesture which he is not aware of anymore. In an organization, it's a group individual using daily the capital of the tacit, implicit and explicit knowledge which allows the production, we speak then about routines organizational. For example, workers within a same organization will adopt the same behaviors to greet, eat, and dress: in each business field they are using common procedures and processes. In the jobs by Painting or by the Diagnosticability, the phase of routinization is acquired when the individual makes an operation without him needing to reflect about movements anymore. He performs the gesture as a habit. This aspect of the routine is essential, because it plays a role in mechanisms of collective coordination. The routines can be visible in particular in the handicrafts but they belong mainly to the domain of the invisible. They are in essence non‐aware. We refer “to “routinization” in a highly flexible way, similarly to when “program” (or “routine”) is used when talking about computer programming. It refers to a repetitive activity modeling within a whole organization, to an individual skill and describes an efficiency consistent in time (without performance drops), should the performance be organizational or individual”. (Nelson R.R et Winter S. op.cit.). The concept of organizational routime is thus strictly and clearly situated in the continuity of the concept of “skill” in order words competencies and know‐how of individuals. At any time, individuals belonging to the organizations have to activate operational gestures linked to an optimum performance of their task. The routine is percevable through memory. It becomes visible through process rules, best practices procedures and socialization, in creating “rules of thumb” or routinely organizational procedures. Thus, both the organization and individuals act by habit. The recognition of the situation activates automatically the procedure, without reflecting about it. "A matching of rules to situations" (March J.G. and Simon H.A., 1993). However, it is

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Johanna Frances, Stéphane Robin and Jean‐Louis Ermine possible to modify a routine, it is not considered as immutable, but the process is complex: it requires becoming aware which implies an effort from the individual and even more from the collective. To change working routines is possible but the change has to make sens, and then be experimented and internalized to become a routine.

Mastering

It is the ability to consistently apply the know‐how. It is not only referring to written knowledge, knowing can be expressed by a language, a drawing, gestures to be reproduced. In the Painting, mastering is what differentiates the actors. While some are not able to verbally describe their job, tasks to be performed as well as contraints, others demonstrate an ability to make it explicit and to put on a teacher hat. They use language, writing, drawing or just describe the knowing. But, all are able to describe know‐how with gestures. The realization of mastering by an actor working on the painting chain translates into his promotion to the “orientator” workstation. These are sollicitated by the supervisors in order to let the job evolve, as they demonstrate abilities to concetualize and verbalize collective routines, and even to modify them. In the Diagniosticability, the mastering is the ability to make the knowing explicit. The interviewed actors during this study have demonstrated a strong ability to express both verbally and in writing as well as a strong ability to integrate new knowledge, new practices, build new reflexions, etc… Even though, all do not find a justification in the need to transfer their knowledge. We have noticed obstacles in knowledge transfer or knowledge harvesting in the work fields. We can thus question ourselves on this problem. We think that the mastering of an operation related to the own job is a general capacity. While it is true that the actors that we met are not always able to make their function or operations they perform explicit, all have found an alternative way. Some have schemas, graphs, others have used analogies and most of them have used their bodies. The hands are essentially used to help express a specific knowing, to describe an object, or express an idea. Thus, it seems that the mastering is accessible to all. We should further investigate the capacities to accumulate knowledge, capacity to reorganize our knowledge base according to a new input. In a similar way to architects, the one who is able to think, build from raw materials in taking into account an environment and context, and who is also able to modify at any time the building plan (to adapt it and find new solutions) in regard to issues that may rise along the plan; this architect is what we call an expert. Thus there would not be a single learning process but several processes, depending on the capacity of an individual to become an expert – or not.

4. Discussion These first results allow questioning us about the cognitive dimensions of the acquisition of the knowledge. Is it only question of favorable environment to make this process optimum? Shouldn’t we rather question individual capacities? Or question ourselves on the possibility of diverse situations characterized by "learning potential" (Jarvis P. 1987)? As we previously highlighted, we have identified barriers in the transfer. Are they linked to issues in information and knowledge sharing within the PSA Peugeot Citroën organization? Currently, the economic crisis has impacted the organization, which has been pushed to sell assets and accept the closure of part of its manufacturing plants. Is it legitimate to assume that barriers in transfer and sharing are linked to the crises and to the fear of employees of losing their job? Or is it rather a strategy that helps ensuring the employee who preserved his/her know‐how and specialized knowledge an evolution in terms of position or hierarchical level? This company, even though it uses cutting edge media tools (email, instant messenger, collaborative space, archive documents, wiki, audio conference, video conference, intranet, job support, job space, e‐learning, etc...), faces issues in knowledge transfer and sharing. We could not, in this short article, approach the matter of intentionality, however we suggest that it should be analyzed as a key factor of the learning potential. Is there (or not) intentionality in transferring or learning, both from the employees and the organization? In regards to the number of media available to facilitate communication and sharing within the organization, we could ask if the identified issues originate in knowledge retention. We also question if the organization has succeeded in putting in place means and methodologies enabling collectives to share and disseminate the knowledge?

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5. Conclusion We can distinguish situations where learning is tacit, implicit, or incidental and others where the workers is fully conscious of the learning even if he/she remains focused on his/her activity. Employees learning can be both incidental (tacit, implicit or fortuity) or incidental (the worker knows he learns even if he is focused on his task). If the process of routinization starts up naturally within the framework of socialization, this one spreads in a different way in a professional environment. If the process of routinization starts up naturally within the framework of socialization (Berger P. and Luckmann T., 1986). This one spreads in a different way in a professional environment Even if the study suggests a number of teachings, it remains however impossible to generalize them. In this paper, we explored our routinization model in two field studies carried out in an automotive environment. Our study shows how different the process can be, depending upon the job area considered. In the painting department, workers shall very quickly learn operational schemes, these latter being long‐lasting and very barely modified when an important change occurs or an enhancement of the working process is made.. In the "diagnosticability" field, the process is by far different. Employees issued from higher education do have a strong theoretical background of knowledge. This is a field where turn‐over is high and knowledge ever‐ changing. Employees must regularly acquire new knowledge. Our main hypothesis is that a learning process is complete one every steps are intimately validated by the individual. However, know‐how, self‐management skills or knowing what to do are not acquired in the same way by all actors. We should actually take into account the actor's cognitive abilities (individual), and the role of peers in acquiring knowledge and know‐how (collective).

References Alami S. & Desjeux D. 2009. Les Méthodes Qualitatives, Paris, Puf. Berger P. & Luckmann T. 1986. La Construction Sociale De La Réalité, Paris, Méridiens Klinchsieck. Billet S. 2006. Constituting The Workplace Curriculum. Journal Of Curriculum Studies, 38, 31‐48. Boud D. & Garrick J. 1999. Understandings Of Workplace Learning In: D. Boud, J. G. (Ed.) Understang Learning At Work. Routledge. Brougère G & Ulmann A.L 2009. Apprendre, Paris, Puf. Brougère G. & Bézille H. 2007. De L’usage De La Notion D’informel Dans Le Champ De L’éducation. Revue Française De Pédagogie, 158, 117‐160. Carré P & Chardonnier O 2004. Les Apprentissage Informels, Le Mesnil‐Sur‐L'estrée, L'harmattan. Carré P. 2005. L’apprenance, Vers Un Nouveau Rapport Au Savoir, Paris, Dunod. Clarke N. 2005. Workplace Learning Environment And Its Relationship With Learning Outcomes In Healthcare Organizations. Humand Resource Development International, 8, 185‐205. Collerette P. 1997. L’étude De Cas Au Service De La Recherche. Recherche De Soins Infirmiers, 50, 81‐88. Ermine J‐L. & Boughzala I. 2007. Management Des Connaissance En Entreprise, Paris, Hermes Science. Ermine J‐L. & Leblanc B. 2007. Une Théorie De Shannon De La Connaissance. In: S, H. (Ed.) Creating Collaborative Advantage Through Knowledge And Innovation. World Scientific Publishing. Grundstein M. 2003. De La Capitalisation Des Connaissances Au Management Des Connaissances Dans L’entreprise, Les Fondamentaux Du Knowledge Management In: Connaissances, C. G. D. (Ed.) Int: Entreprises 3 Jours Pour Faire Le Point Sur Le Knowledge Management. Lapassade G. 1990. La Méthode Ethnographique. Pratique De Formation‐Analyses, 20. March J.G. & Simon H.A. 1993. Organizations Revisited. Industrial And Corporate Change, 2, 299‐316. Marsick V.J. 2006. Informal Strategic Learning In The Workplace In: Streumer J (Ed.) Work‐Related Learning. Dordecht, The Netherlands: Springer. Marsick V.J. & Volpe M. 1999. Re: Informal Learning On The Job. Marsick V.J. & Watkins K.E. 1990. Informal And Incidental Learning In The Workplace, New York, Routledge. Nelson R.R. And Winter S. 1982. An Evolutionary Theory Of Economic Change. Cambridge, Ma, Harvard University Press. Pavlov I. 1934. Le Reflexe Conditionnel In: Kochtoiantz Kh (Ed). Œuvre Choisies.Moscou, Russie: Edition En Langue Etrangère Piaget J. 1975. L'équilibration Des Structures Cognitives. Problème Central Du Dévéloppement. Etude D'épistémologie Génétique [Online], Xxxiii. Available: http://www.fondationjeanpiaget.ch/fjp/site/textes/ve/jp75_eqstrcog_avt_propos_chap1.pdf. Pervin L.A. & John O.P. 2004. De La Recherche À La Théorie, Paris, De Boeck Polanyi M. 1962a. Personal Knowledge: Towards A Post‐Critical Philosophy Chicago, Université De Chicago Press.

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Johanna Frances, Stéphane Robin and Jean‐Louis Ermine Polanyi M. 1962b. Tacit Knowing: Its Bearing On Some Problem Problems Of Philosophy. Reviews Of Modern Physic, 34, 610‐616. Polanyi M. 1966. The Tacit Dimension, Chacago, University Of Chicago Press. Rix‐Lievre G. And Lievre P. (2012). La Dimension « Tacite » Des Connaissances Expérientielles Individuelles : Une Mise En Perspective Théorique Et Méthodologique." Management International, 16 Numéro Spécial: 21‐28. Skinner B.F. 1953. Science And Human Bahavior. Cambridge, Massachusetts, Harvard University. Available: http://www.bfskinner.org/books4sale.asp Skule S. 2004. Learning Conditions At Work: A Framework To Understand And Assess Informal Learning In The Workplace International Journal Of Training And Development, 8, 8‐17. Tough A. 1971. The Adult's Learning Projects: A Fresh Approach To Theory And Practice In Adult Learning, Toronto, University Of Toronto : Ontario Institute In Studies In Education. Watson J.B. 1913. Psychologies As The Behaviorist View It. Psychological Review, 20, 158‐177 Yin R.K. 1984, 2008. Case Study Research: Design And Methods, London, Fourth Edition., Sage Publications Inc.

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Cluster Analysis of the European Countries: The Europe 2020 Point of View Adela Anca Fucec and Corina Marinescu (Pirlogea) The Bucharest University of Economic Studies, Bucharest, Romania fucec.adela@yahoo.com corina_pirlogea@yahoo.com Abstract: The European Union devotes a lot of attention to the development of knowledge economies and the main result of this concern is the Europe 2020 strategy. From this point of view, the present paper aims to provide a clearer picture of who’s who in the European Union with respect to the development of knowledge based economies is concerned. In order to attain the above mentioned objective, first of all is presented the state of art regarding knowledge economy development, by presenting the values that a country is able to register at the 8 indicators of the Europe 2020 strategy objectives. Secondly, the paper presents the results of the quantitative research undergone with the help of Principal Components Analysis and Cluster Analysis on the principal components previously found. The two principal components previously discovered and used for this Cluster Analysis are the Shame Factor and the Environmental Concern Factor and they hold 94.43% of nonredundant information from the 8 initial strategy objective indicators. We analysed the 27 European Union countries, plus Switzerland, Norway and Iceland and the level of the Europe 2020’s indicators in 2010. This paper shows the full results of the Cluster Analysis, which gives us the panel of the European Union from the perspective of development of knowledge economies. The results show three groups of countries and allow a summary of several features for each group. Furthermore, this research also provides an additional quantitative support for a previously discovered ranking of the European countries, from the point of view of knowledge economies. The main advantage of this study is that it can raise the interest of research scientists interested in knowledge management and comparative management, for it shows the kinds of countries that have best managed so far to achieve the status of a knowledge economy. It can also be relevant for anyone interested in a professional picture of how the main countries in Europe currently look like from the new economic perspective. Keywords: Europe 2020 strategy, knowledge economy, European Union, cluster analysis

1. Introduction and background research The present paper aims to further pursue a recent reasearch of Fucec (2012) that managed to make a hierarchy of several European countries, using the objectives of the Europe 2020 strategy as criteria. We already argued how and why this strategy is important for the scientific and practical communities in Europe, and throughout the world, so what this new research pursues is a softer image of where each country stands in that hierarchy. Besides, in the previous research paper, the accent was placed on finding synthetic aggregators for the indicators of the objectives in the Europe 2020 strategy and seeking what those indicators can tell us about several countries in Europe and about Romania, in particular. Moreover, this paper aims at furthering the research in order to find out how, besides hierarchy, can we analyse the countries from the Europe 2020’s point of view. As previously mentioned (Fucec, 2012), the Europe 2020 Strategy has 5 main objectives which are expressed by 8 indicators: Employment Rate (%), Gross Expenditure on Research and Development (%), Greenhouse Gas Emissions (year‐base 1990 is considered to have the value 100), Renewable Energy (%), Primary oil consumption (tones of oil equivalent), Early Leavers from Education ( %), Tertiary Education Attainment (%), People at Risk of Poverty or Social Exclusion (%). These indicators have been analysed for the 27 EU member state, including Switzerland, Norway and Iceland, for 2010. Two basic results of this previous analysis are used as a basis for the present paper: the two new indicators found and the resultant hierarchy of the European countries. According to the author, the two new indicators are ‟the Irrelevance Factor, metaphorically called Shame Factor, with a low desirable value, and the Environmental Concern factor, with a high desirable value” (Fucec, 2012). The first principal component proved to be strongly negative correlated to the primary oil consumption in the countries, resulting in a necessity to find a name to express the opposite of what this indicator stands for. Assuming that large oil consumption is generally equivalent to development in a country, it was decided that this principal component should be named Irrelevance Factor, because it is desirable for it to have a low

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Adela Anca Fucec and Corina Marinescu (Pirlogea) value. The second principal component appeared as being strongly positive correlated to the Greenhouse Gas Emissions in a country, so the Factor was expressed as “Environmental Concern”. Based on an aggregated indicator which enveloped the two factors mentioned above, a hierarchy of the countries was made, “ranking them in terms of their evolution towards the stage of a knowledge‐based economy. The ranking is based on a quantitative and statistical base and it reffers particularly to knowledge‐ based economies” (Fucec, 2012). Moreover, the hierarchy is very relevant for ranking each country in the European landscape from the Europe2020’s point of view. For example, the first three countries in the ranking were Germany, France and United Kingdom, followed by Italy, Romania and Poland. The leading countries were not such a surprise, but it was a bit unexpected to find Romania or Italy on such a good position. However, these are the countries that are ”on the right track toward achieving the status of knowledge‐based economies, acording to the European strategy” (Fucec, 2012). The last countries in the ranking proved to be Iceland, Malta and Cyprus. In other words, these countries still have a lot of effort to undergo in order to catch up the progress of the other countries as far as the Europe 2020 Strategy is concerned. Furthermore, in order to be able to point out similarities between several countries, it would be even more helpful to rank the countries, by determining small groups of countries with high resemblance inside the group and with clear differences between groups. This is exactly where a Cluster Analysis comes in handy, according to Ruxanda (2002). Besides, using such a method may also allow us to make a foresight of the evolution of another country, which was not yet submitted to the analysis. Various cluster analysis have been used so far for territorial comparisons inside a country (Babucea, 2007) or even for corruption estimation studies (Iamandi and Voicu‐Dorobantu, 2007).

2. Research methodology The research methodology followed in order to obtain the groups of countries we seek, besides the ranking which we already have, has two major phases. Phase one is the Principal Component Analysis, procedure described by Fucec (2012) and resumed in the above introduction. Phase two is the center of the present research and it represents a Cluster Analysis, based on the principal components identified and described in the previous phase of the research. As explained by Ruxanda (2001), the Cluster Analysis, as a research instrument, refers to the process of clustering a number of variables or objects, with several features, into groups with two important characteristics: they are internally homogeneous and very heterogeneous in between. In other words, although these groups cannot be obviously remarked, they become obvious after the cluster analysis. It is important to note that we do not know a priori the number of groups or clusters in which the initial objects will be divided, nor the criteria by which they will be grouped. Anderberg (1973) says that ‟cluster analysis is a collective term covering a wide variety of techniques for delineating natural groups or clusters in data sets’’. This turns cluster analysis into a multicriteria optimization problem and a basic part of data mining procedures. Moreover, “applications of cluster analysis are found in virtually all professions’’ (Romesburg, 2004), but the technique is also widely used in scientific domains such as statistics, economics and data analysis. Due to its nature, cluster analysis involves trial and error, for it can often be necessary to preprocess the data, in order to acquire the desired properties. The analysis does not involve a single specific algorithm. It starts from one case (one form or one article), which possesses one or more properties of interest and which is represented mathematically as a vector. The case’s feature or properties (n) of interest are called attributes and all the cases are included in a crowd. The notion of cluster defines a subset of the initial crowd, which generally denotes the criteria of the classification: intraclass variability should be as small as possible (for the clusters to be homogeneous inside), and variability between clusters should be as large as possible (for clusters to be different from each other). Subsequently, the question is that of evaluating the distance between the cases, the distance between clusters and the two types of variability (intraclass and between classes). This is what justifies the variety in approaches and the lack of a single classical algorithm: various valid ways are available to make the evaluations mentioned above, for the choice remains up to the researcher: ‟clustering is in the eye of the beholder” (Estivill‐Castro, 2002), because no method is the right one.

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Adela Anca Fucec and Corina Marinescu (Pirlogea) For this research, we chose to perform a hierarchical cluster analysis, also called connectivity based clustering. In this case, we start with a number of clusters equal to the number of forms that we want to group: each cluster consists of a single form. With each step of the algorithm, the clusters with the smallest distance between them are united. The algorithm ends when we have one final cluster. The clusters sought can be found in the dendogram or horizontal hierarchical tree plot, which is a graphical representation of the clustering process. To calculate the distance between clusters and forms, we used Manhattan or City Block distances (Krause, 1987), which is less affected by errors because it calls on the module function, and which is calculated using the following formula:

, where (1)

d(X,Y) =

d(X,Y) is the Manhattan distance between X and Y, n is the number of space dimensions or features of our cases, and X and Y are vectors of the following form: X = (x1, x2, ..., xn); Y = (y1, y2, ..., yn). In our case, the forms or cases submitted to be analysed are the forementioned 30 countries and their attributes or properties of interest for research are the 8 indicators of the EU2020 strategy. As mentioned above, for higher accuracy, we turn to the results of the previous research that used PCA (Principal Component Analysis) for an informational synthesis of the 8 indicators. As mentioned earlier, this analysis obtained two indicators that retain approximately 95% of the information in the initial indicators. Therefore, the two attributes of the countries which will be used to start the cluster analysis will be the new synthetic indicators: the Shame Factor and the Environmental Concern Factor. The data has been retrieved from the Eurostat website (European Commission, 2012), referring to the year 2010, for the 27 EU member state, plus Switzerland, Norway and Iceland. The current and previous papers used Statistica 8 for data processing. As mentioned above, we performed a Hierarchical Joining Cluster Analysis, using raw data and calculating the Manhattan distance for clustering the cases (or rows).

3. Results and interpretation After performing the analysis, we found several pieces of useful information that we will use for the discussion in the following sections of the paper, including: the Distance Matrix, the Amalgamation Schedule and the Dendogram or Horizontal Hierarchical Tree Plot.

3.1 The distance matrix The Distance Matrix is the first and the simplest result of the Cluster Analysis and it is closely related to the second result, the Amalgamation Schedule. The algorithm first calculates all the Manhattan distances between the countries (and puts them in the Distance Matrix) and only after arranging the distances in an ascending scale, it shows the Amalgamation Schedule. What the Distance Matrix solely shows is how far away is each country from another one, from the point of view of this analysis. A part of the Distance Matrix is shown below, in Figure 1. It was not possible to show the whole matrix, because it has 31 rows and 31 columns, so we only selected the first part of it, in order to be able to explain what this matrix shows. For example, the Manhattan distance between Austria and Greece is 11 and between Malta and Germany is 373. It is obvious that Austria and Greece are much more similar to one another than Malta and Germany are. It is more likely that Austria and Greece will form a cluster and very unlikely that Malta and Germany will. By taking a peek at this matrix, at the whole matrix actually, we can estimate which countries are similar to one another and which countries present the greatest differences. If the whole matrix would be shown here, it would be possible to see that the smallest distance between two countries is 2, which is the distance between Austria and Norway. This is why these two countries will form the first cluster and this is how the Amalgamation Schedule begins.

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Figure 1: The Distance Matrix (partly), drawn by the authors, using Statistica 8

3.2 The Amalgamation Schedule This is the result of the cluster analysis which illustrates exactly what steps were taken in order to obtain the tree diagram or otherwise known as a dendogram. The tree diagram is the most relevant result and is discussed in detail below. A part of the Amalgamation Schedule is found in Figure 2.

Figure 2: The Amalgamation Schedule (partly), drawn by the authors, using Statistica 8 As shown in Figure 2 and intuited above, the two closest countries are Austria and Norway, because 2.215544 is the smallest Manhattan distance between any two countries submitted to the analysis. What this means is that Austria and Norway are as similar as possible from the viewpoint of the two indicators that show how the countries are doing, as far as the Europe 2020 Strategy is concerned. The next distance is 2.701538, between Latvia and Lithuania. These two countries are the second closest countries from the 30 states analysed, so, at this point we already have two clusters, composed of the countries with most resemblances between them. The third distance shown in Figure 2 is interesting: 3.058608 is the Manhattan distance between the initially composed cluster (Austria and Norway) and Finland. Now, the third cluster is formed among these three countries. As seen in the Figure, in the next two steps, Switzerland and Greece join the cluster. And so on and so far, this is how all the countries come together and give us a number of 2 clusters (or even one final cluster) in the end, from the 30 initial clusters. With each step, the number of clusters decreases, because each country will unite or annex itself to a previous constructed cluster, based on the smallest Manhattan distances between clusters. The following result of the analysis, and the most significant one, will show us how to divide

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Adela Anca Fucec and Corina Marinescu (Pirlogea) the clusters so as the distance between clusters is high enough for us to say that the clusters are different in an obvious way.

3.3 The Horizontal Hierarchical Tree Plot So far, we know what the Manhattan distances between all the countries are (from the Distance Matrix) and we also know which steps were followed so that all the countries were included in a cluster. The Horizontal Hierarchical Tree Plot, also called dendogram or, simply, Tree Diagram, is a picture of all that we have explained above and it is shown in Figure 3. By looking at the Tree Diagram, we can choose how many clusters we wish to define, based on the distance among clusters. In Figure 3 above, we believe that three clusters can be identified, as follows:

Cluster 1: United Kingdom, Italy, France, Germany, Spain;

Cluster 2: Poland, Iceland, Malta, Cyprus, Slovenia, Luxembourg, Portugal, Switzerland, Finland, Norway, Austria, Greece, Ireland, Denmark;

Cluster 3: Netherlands, Romania, Czech Republic, Lithuania, Latvia, Estonia, Hungary, Slovakia, Bulgaria, Sweden and Belgium.

As mentioned in the description of the research methodology, these results are submitted to the subjectivity of the researchers. We could have chosen to define only two clusters, because there is a striking difference between the countries included in cluster 1 and the remaining countries. But for the purpose of our analysis, we found it useful to identify the other two clusters in the remaining 25 countries.

Figure 3: The Tree Diagram, drawn by the authors, using Statistica 8 As far as the features of the clusters are concerned, it is important to take a look at the values that the countries have registered at the two indicators used for the analysis. In the case of cluster 1, thing are fairly clear: we have economically strong countries, which seem to manage to keep under control the targets of the European strategy’s objectives and have convenient values for the Shame factor and the Environmental Concern factor. Also, according to Fucec (2012), four out of this five countries (Spain excluded) are in the top four ranking positions of the countries, based on the principal components extracted form the 8 indicators of the Europe 2020 Strategy. Cluster 2 holds 14 countries, as shown above. What these countries have in common is that, except for Poland, they are the last 13 countries in the previously mentioned ranking. In other words, the values they registered for the two indicators, the Shame factor and the Environmental Concern factor, were the least desirable in

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Adela Anca Fucec and Corina Marinescu (Pirlogea) comparison to the other countries analysed. The situation with Poland is interesting, as is the situation with Spain in cluster 1. Poland is number 6 in the ranking, but now it seems to be included in the worst cluster with regards to the European strategy’s objectives. This could happen because several of the initial 8 indicators have very favourable values and other have very unfavourable ones, but since a principal component analysis was previously ran, the situation of Poland and Spain requires further research. As defining features for the countries in this cluster, we can say that they have average employment rates, between 70% and 81,1% (except for Ireland – 65%, Greece – 64%, Malta – 60,1% and Poland 64,60%), and also high values for greenhouse gas emissions, above 102 compared to year base 1990, considered of value 100. These countries are the ones who need to establish sustainable efforts in order to become knowledge economies. Finally, cluster 3 holds the remaining 11 countries. In the ranking, these countries were placed on the middle positions, from 7 to 17. Again, we have an interesting case here: Romania, number 5 in the ranking. However, this is not as surprising as the case of Poland and Spain. Romania is definitely not a country you would place in a cluster with Germany and the United Kingdom, but it’s knowledge economy perspectives are rather positive, since it is placed in the middle‐developed cluster, from the point of view of the European strategy. The countries in cluster 3 appear to have interesting features: in the knowledge economies ranking they are placed in the middle, yet, except for Sweden (78,70%), they have small employment rates, of about 65%. What this tells us is that the employment rate is not a defining feature for basing an economy on knowledge or not. The influence of other factors or indicators can be much more valuable. The greenhouse gas emissions are mostly below 71, compared to year base 1990 of value 100, with the exception of Belgium (92), and Sweden (91). In comparison to cluster 2, the countries in cluster 3 have a higher average of people at risk of poverty and a lower average for the primary oil consumption.

4. Conclusions The conclusions of this study come to support and detail the findings of previous quantitative research in this field. We found that the order of the countries in the ranking of Fucec (2012) is not random, for the countries can be divided into three groups or clusters, each of them having several features. Two out of 30 cases have shown ambiguous results, so this is an aspect that can be submitted to further investigations. Therefore, further research directions are imperative and will provide interesting information regarding what will happen with Poland and Spain. These countries present no connection between their position in the ranking and the cluster they have been assigned to. Further research is also recommended in the case of Romania, in order to provide an adequate support base for the results of this present analysis and the previous principal components analysis.

Figure 4: European Landscape of Knowledge Economies, drawn by the authors, using Statistica 8 In conclusion, in order to be able to say with a quantitative precision where 30 countries of Europe stand regarding the attainment of the Europe 2020 strategy’s objectives is a new approach, in the beginning phase, but with one first step successfully completed. Based on the ranking of the countries and on this cluster analysis, a photo of Europe’s knowledge economies looks as it is shown above, in Figure 4. The stars in the

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Adela Anca Fucec and Corina Marinescu (Pirlogea) picture show an ideal knowledge economy, as illustrated from this analysis, and the direction in which all the countries should head on.

References Anderberg, M. R. (1973) Cluster Analysis for Applications, Academic Press, Michigan. Babucea, A. G. (2007) ″Utilizarea analizei cluster in comparatii teritoriale″, Economic Annals of Constantin Brancusi University, [online], No 1/2007, pp 311‐316, available from: http://www.utgjiu.ro/revista/ec/pdf/2007‐ 01/57_Babucea%20Ana‐Gabriela.pdf. [accesed 12 February 2013]. Estivill‐Castro, V. (2002) ″Why so many clustering algorithms: a position paper”, ACM SIGKDD Explorations Newsletter, Vol 4, Issue 1, June, pp 65‐75. European Commission (2012) Europe 2020 indicators – Headline Indicators, Bruxelles. Fucec, A. A. (2012) ″Is Romania a favourable environment for the development of knowledge‐based organizations?″, Review of International Comparative Management, Vol 13, Issue 5, December, pp 768 – 777. Iamandi, I. and Voicu‐Dorobantu, R. (2007) ″Corupția – un risc pentru România în Uniunea Europeană″, Economic Journal, [online] Year X, No 24, July, pp 15‐27, available from: http://www.rejournal.eu/Portals/0/Arhiva/JE%2024/JE%2024%20Voicu‐Dorobantu%20Iamandi.pdf.[accesed 11 February 2013]. Krause, E. F. (1987) Taxicab Geometry, Addison‐Wesley Publishing Company, Dover. Romesburg, H. C. (2004) Cluster Analysis For Researchers, Lulu Press, North Carolina. Ruxanda, G. (2001) Analiza Datelor, Editura ASE, Bucharest. Ruxanda, G. (2002) ”Recunoaşterea formelor în domeniul economico‐financiar”, Studii şi Cercetări de Calcul Economic şi Cibernetică Economică, No. 2/2002, Year XXXVI.

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Literature Review: The Role of Intangible Resources in Improving Quality of Care in Hospitals: A Framework to Evaluate Technical and Functional Quality Hussain Hamed and Simon De Lusignan Department Of Health Care Management and Policy, Faculty Of Business, Economics And Law, University Of Surrey, UK h.hamed@surrey.ac.uk s.lusignan@surrey.ac.uk Abstract: Intangible resources (IR) impact an organisation’s performance and its ability to deliver high quality services. Health care quality is described as having technical and functional aspects; and many of these quality systems are underpinned by human capital (knowledge and expertise), structural capital (quality management programmes and information systems) and relational capital (patients’ perspective on quality). Despite the extensive research on IR across commercial and business sectors, little is known about the management of IR in health care systems. We describe the role of IR in health care systems by focusing on identifying types of IR that can play a significant role in improving the quality and safety of care. Hospitals face recurrent challenges in improving quality. We propose a framework for assessing how IR might improve health care quality. Knowledge, professional expertise, professional competences and skills, information systems, processes, safety culture, quality management programmes, guidelines, interpersonal skills and patients were found to have a significant impact on the quality of care. The suggested framework introduces a checklist that might be used by hospital managers to audit whether they are maximising their use and aligning their intangibles with delivering quality. Keywords: intangible resources, health care, quality, improvement

1. Introduction 1.1 Intangible resources Intangible resources (IR) are an important topic across a wide range of organisations (García‐Ayuso 2003). It has become recognised that organisational resources are not only tangible, but also intangible, and these intangible resources play an important role in creating and adding value (Carmeli and Tishler 2004). In addition, intangible resources are considered to be key factors that help organisations achieve their objectives and maintain high performance and, hence, achieve competitive advantage (Hall 1993; Carmeli and Tishler 2004). Extensive research on IR across multi‐disciplinary areas has resulted in a vast body of knowledge within the literature on intangibles (Marr 2005). This extensive literature has resulted in a wide range of different terms, definitions and classifications being used within this domain (Petty and Güthrie 2000; Kaufman and Schneider 2004; Marr and Moustaghfir 2005; Choong 2008). However, a better understanding of how to manage such resources is needed (Petty and Güthrie 2000; Lev 2003) because understanding how to deploy such resources to bring benefits to organisations is much more important (Marr and Moustaghfir 2005). It is crucial to identify the key types of IR that are relevant to different business sectors. According to Cong and Pandya (2003) greater knowledge about IR can help in allocating these resources more efficiently within business firms and, hence, improve processes, such as decision‐making, and reduce the need to rework jobs not done properly the first time. In the health care context there are limited studies that have attempted to explore the impact of IR on hospital performance (Habersam and Piper 2003; Peng et al. 2007; Zigan et al. 2008; 2009). There are no studies identifying the key types of IR and how these types of resources impact the quality of health care. The majority of definitions of IR refer to knowledge and economic value where knowledge can be a profit generator (Brennan and Connell 2000; Harrison and Sullivan 2000; Sullivan 2000) or to the nature of IR as the kind of resource that is invisible, non‐monetary and non‐physical, but still has an impact on organisations (Choong 2008). The definition proposed by Edvinsson and Malone (1997) will be adopted. They write from a strategic management perspective and define (IR) as “those that have no physical existence but are still of value to the company” (p. 22).

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Hussain Hamed and Simon De Lusignan Intangible resources have been mainly divided into: human capital, structural capital and relational capital (Stewart 1997; Sveiby 1997; Edvinsson and Malone 1997). The literature review has revealed additional classifications. According to Zigan et al. (2009) social capital is seen to be important in the establishment of intra‐organisational relationships. Similarly, Habersam and Piper (2003) mention “connectivity” as a fourth type of IR which connects the sub‐elements of human, structural and relational capital together.

1.2 Quality in hospitals On the other hand, health care systems worldwide are concerned with the quality of care provided to the population. Due to the high level of risks involved in health care settings (Rashid and Jusoff 2009), it is important to conceptualise quality in a health care setting (Taner and Antony 2006). In addition, quality is seen to be critical in a patient’s choice (Lynch and Schuler 1990). Therefore, providers continuously seek new approaches to improve health care quality.However, it is difficult to find a consensus regarding what is meant by quality. For the purpose of this paper, quality is defined based on dividing it into technical quality and functional quality (Gronroos 1984; Parasuraman et al. 1985; Lewis and Mitchel 1990; Lewis 1991). This perspective of quality reflects the aspects that health care providers (i.e. hospitals) should focus on in the process of delivering health care. In addition, it provides an insight into the link between the notion of IR and the intangible nature of the technical and functional attributes of quality in health care settings. Therefore, this paper aims to produce a framework for hospital managers linking IR to health care quality in order to identify key intangibles that, relatively, have a significant impact on improving quality of care.

2. Intangible resources in health care 2.1 Human capital According to Zigan et al. (2008, p. 62) employees' knowledge is very important for ensuring excellence in hospitals because "People's knowledge determines everything". Peng et al. (2007) also emphasise the importance of employees’ knowledge within hospitals, as health care providers are characterised as knowledge‐intensive organisations. Habersam and Piper (2003) identify that knowledge about the structure and processes of the organisation is important; they call it structural knowledge. Similarly, Zigan et al. (2009) emphasise structural knowledge as knowledge about the processes and structures and how the different departments interacted is seen as being important. In addition, education and professional experience are crucial in hospitals. University education (Habersam and Piper 2003) and the qualifications of the clinical staff (Zigan et al. 2008; 2009) are seen to have an impact on performance. Professional experience is found to be important in interacting with patients. In health care contexts, staff experience is crucial in dealing with patients because even the treatment of an old and a young patient with the same diagnosis might be totally different (Habersam and Piper 2003). Furthermore, Habersam and Piper (2003) state that hospital staff are required to be aware of competences, such as sensitivity, communication skills and friendliness, during their interactions with patients. Peng et al. (2007) found that top managers' personal relationships, their capabilities in decision‐making, staff capabilities in health care delivery, the staff capabilities in coping with crisis, the doctors' reputations, the professional competences and skills of medical staff and the managerial talent of administrative staff to be important in delivering services in hospitals. However, up‐to‐date competences are especially required in hospitals because such competences are dependent upon professional knowledge, which becomes obsolete quickly (Habersam and Piper 2003). Staff motivation is considered to be important in hospitals (Zigan et al. 2008). However, Habersam and Piper (2003) argue that high degree of motivation may not be sufficient for delivering high quality care if employees are not willing to continuously understand the needs of patients, to attend professional training and to overcome communication barriers. The importance of staff behaviour emerges from perceiving it to be the underlying ground of the employees’ performance, which the organisation's overall performance depends upon (Zigan et al. 2008).

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Hussain Hamed and Simon De Lusignan Table 1: Identified IR in hospitals Author(s)

Habersam and Piper (2003)

Human Capital

Structural Capital

Knowledge (university) education Personal professional experiences Further personal (professional) training Personal ability to put knowledge into practice Motivation Staff interpersonal skills

Certified quality management system Software for evidence‐based medicine

Top managers' personal relationships Staff's capabilities in health care delivery Staff's capabilities in coping with crisis Top managers’ capabilities in decision‐ making Doctors' reputation Professional competences and skills of medical staff Managerial talent of administrative staff

Health care service and quality Marketing Strategic management Information technology

Motivation Professional behaviour Knowledge (education and qualification status) Friendliness Knowledge Experience Attitude Loyalty Qualifications Social Capital Cooperation Mission statement

Information system

Peng et al. (2007)

Zigan et al. (2008)

Zigan et al. (2009)

Information & communication systems Routines Processes

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Relational Capital Patients Competition and co‐operation with other hospitals Relationship between internal and external resources General reputation Top management team Patients Government agencies The Bureau of National Health Insurance (BNHI) Employees Media Congress Suppliers Health care academy and associations Outsourcing partners Competitors (hospitals) Board Research institutions Communities Medical specialty associations Non‐profit organisations and foundations Local clinics Private insurance companies Patients Occupational groups Other hospitals Other hospitals..

Hussain Hamed and Simon De Lusignan

2.2 Relational capital Relational capital is very important to hospitals as patients and professionals can choose their health provider. According to Zigan et al. (2009), relational capital includes patients and other hospitals. This type of IR is important because its purpose is to exchange and share other intangible resources as well as tangible ones (Zigan et al. 2008). Peng et al. (2007) evaluate the importance of the hospitals' relationships with 20 key stakeholders, internally and externally, including patients and other hospitals (see Table 1). In addition, Habersam and Piper (2003) identify patients, competition and co‐operation with other hospitals, the relationship between internal and external resources, and general reputation to be important resources for hospital performance. Patients’ feedback can also provide important information for improving quality and safety of care (Tasa et al. 1996; Wensing and Grol 1998; Wilcock et al. 2003; Benn et al. 2009).

2.3 Structural capital Structural capital is comprised of the programmes and systems needed to organise work and typically includes quality management programmes and information systems. Generally, in health care settings structural capital has an impact on other types of capital, namely human and relational capital. Structural capital requires resources, yet contributes less in return (Peng et al. 2007). Furthermore, Cinca et al. (2003) emphasise the role of processes and work procedures for running the organisation and for achieving its goals. More recently the importance of having a strong safety culture has been emphasised (Wagner et al. 2013). According to Habersam and Piper (2003), systems such as certified quality management and software for evidence‐based medicine are used to set standards within a hospital for systematic management control activities in order to ensure continuous improvement. Similarly, Peng et al. (2007) emphasise the standardisation of operational processes in hospitals. Furthermore, a good information system is seen to be essential for appropriate collection and management of data (Zigan et al. 2008). Mbananga et al. (2002) also state that information systems are generally ignored by health workers; therefore, the users' awareness about how hospital information systems work should be developed. They argue that if information system users do not understand how such systems function, it would lead to the failure of the system. Peng et al. (2007) divide structural capital into four categories: namely health care service and quality capital; strategic management capital; marketing capital; and information technology capital (see Table 1). They find that components of health care service and quality are the most important and components of information technology are the least important.

3. Aspects of service quality in health care An improved understanding of health care quality is needed (Blumenthal 1996; Campbell et al. 2000). Due to the different quality perspectives of stakeholders, consensus on a definition of health care quality is almost impossible (Piligrimienė and Bučiūnienė 2008). According to McGlynn (1997), stakeholders define quality according to their own perspectives. From the service users’ point of view, patients define quality as “efforts of physicians to do everything possible for a patient” (Piligrimienė and Bučiūnienė 2008, p. 104). In addition, patients usually focus on some quality dimensions more than others, namely effectiveness, accessibility, interpersonal relations, continuity and tangibles. Nonetheless, without standards and specifications that assure the safe use of such services, the norms and values of users in determining the quality of services provided or the processes might be harmful in the long term (Harteloh 2000). In contrast, health care professionals focus on care attributes and outcomes. Piligrimienė and Bučiūnienė (2008, p. 105) state that health care professionals’ definition of quality “emphasises the technical excellence with which care is provided and the characteristics of interactions between provider and patient”. Despite that health care professionals usually discounted the importance of a patient‐centred perspective, they consider skills, technical competences, resources and conditions to be important factors in improving a patient’s health status, and tasks must be performed according to standards and specifications (Piligrimienė and Bučiūnienė 2008). According to Rashid and Jusoff (2009), the service quality of health care is divided into technical quality and functional quality. In a health care setting, technical quality refers to the technical aspects of service, such as clinical procedures and conformance to professional specifications (Lam 1997). It also refers to the clinical competences of the staff, which includes the clinical and operating skills of doctors, nurses and other

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Hussain Hamed and Simon De Lusignan occupational groups as well as their clinical expertise (Tomes and Ng 1995). On the other hand, functional quality refers to the environment in which care is delivered to patients, such as the “facilities, cleanliness, quality of hospital food, hospital personnel’s attitudes” (Rashid and Jusoff 2009, p. 472). In addition, functional quality considers the “behaviour of customer‐contact staff” (Kang 2006, p. 37). This perspective of quality in health care is adopted because it provides a platform for identifying intangible resources that can be included in delivering quality in hospitals.

4. The research methodology The literature of intangibles is vast; therefore, to cover the relevant literature several steps were needed. In order to identify the literature that is most relevant to this research it was important to determine the nature of the review by setting clear criteria for searching and selecting the most relevant sources. Therefore, a standardised protocol has been set and followed. The protocol included basic criteria in order to narrow the search. The search focused on literature in the English language only. For the purpose of this study, papers published between 1990 and 2012 were included. Keywords for this research have been defined based on the author’s prior experience and the literature related to IR. Therefore, IR, intangible assets, intellectual capital, health care, healthcare, hospital, and quality were used in the searching process. Three journal databases, namely Emerald, EBSCO and Wiley Interscience, where used. Four empirical studies were identified (see Table 1). These previous studies were used by the authors to conduct a focused literature review on the elements of IR that have an impact on the quality of health care.

5. Discussion 5.1 Principal findings The literature review of empirical studies has shown that specific intangibles have an impact on hospital performance. The most important intangibles identified are: knowledge, professional experience, motivation, professional competences and skills, interpersonal skills (friendliness), quality management programmes, information systems, patients and relationships with other hospitals (Table 1). These have all been recognised to have an Impact on hospital performance (Habersam and Piper 2003; Peng et al. 2007; Zigan et al. 2008; 2009). Quality can be subdivided into technical aspects and functional aspects. The service quality perspective divides quality in health care settings into technical aspects and functional aspects (Rashid and Jusoff 2009). This perspective of service quality in health care can provide a strong platform to link elements of technical and functional elements with components of IR. Technical and functional qualities are defined from different elements (see Table 2). Some of these elements are recognised to be intangible. Therefore, similarities can be found between elements of IR and technical and functional quality. For example, the competence and skills of one organisation’s staff are an important IR. The literature suggests that components of technical quality refer to the professional competence, skills and technical capabilities of the health care professional (see Figure 1). Comparing these components with the key elements of human capital listed in Table 1 indicates similarities between the elements of technical quality and the components of human capital. On the other hand, functional quality refers to the way in which service is provided to patients. Furthermore, the process of service provision to patients requires a minimum level of interaction between hospital staff and patients. Therefore, the interpersonal skills of the hospital staff are seen to be important for providing services to patients in an appropriate manner (Blumenthal 1996) and because patients evaluate quality based on the interpersonal skills of hospital staff (Lam 1997). Consequently, this links interpersonal skills to the elements of human capital in Table 1. Interpersonal skills are found to be an important IR and staff “friendliness” is an important component for a hospital’s performance. Patients and other hospitals were found to be common elements of relational capital (see Table 1). This is due to the importance of the patients’ perspectives on the quality and safety of health care, from the vantage point of them being the recipients of the services. As patients tend to evaluate quality based on functional quality, their evaluation is highly based on their interactions with hospital staff (human capital) that play a significant role in delivering quality within health care settings. In addition, other hospitals can provide a good opportunity for sharing training sessions, seminars and experience in order to continuously update the competences of the hospital staff (Habersam and Piper 2003).

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Hussain Hamed and Simon De Lusignan Table 2: Elements of service quality in health care

Palmer et al. (1991)

Tomes and Ng (1995) Lam (1997)

Piligrimienė and Bučiūnienė (2008)

Service Quality of Health Care Technical Quality Appropriateness of services provided Rashid and Required skills to provide appropriate Jusoff (2009) care. Competence of staff Clinical and operating skills & expertise of staff Technical accuracy of the medical diagnoses and procedures Compliance of professional specifications

Kenagy et al. (1999) Blumenthal (1996)

Functional Quality Facilities Cleanliness Quality of hospital food Hospital personnel’s attitudes Inter‐personal skills of health care professionals Physician–patient interaction

Skills Technical competences

In terms of structural capital, the literature review shows that quality management programmes and information systems are arguably the most important IR in health care settings (Habersam and Piper 2003; Peng et al. 2007; Zigan 2008). According to Lam (1997), technical quality refers to the compliance of professional standards. Therefore, quality management systems, particularly accreditation programmes, are seen as a key intangible resource. In addition, information technology within health care settings is also found to have a relative impact on the quality of health care (Chaudary et al. 2006).

Figure 1: Linking attributes of service quality to key intangible resources

5.2 Implications of the findings No hospital looking to improve quality should ignore the potential of its IR. According to Gronroos (2000), organisations in the service sector are highly dependent on specific intangible resources, such as employee knowledge and employee competences for creating and delivering value to their customers. In health care, IR is considered to be highly important for providing patients with a high quality care (Cañibano et al. 1999). Furthermore, the nature of health care organisations as being knowledge‐intensive requires good identification, management and measurement of IR (Peng et al. 2007). Health care organisations are considered to be responsive and productive and they can be perceived as a collection of professional specialists. Therefore, hospitals emphasise the specialised knowledge, skills and abilities of their employees to ensure that they deliver high quality of care to patients (Van Beveren 2003). Delivery of quality in health care

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Hussain Hamed and Simon De Lusignan should also take staff expertise into consideration, as the clinical experience of physicians has been found to be associated with quality of health care (Choudhry et al. 2005). Therefore, there is a need for any initiative that seeks to improve the quality of care in hospitals to include both technical and functional components because patients usually focus on the functional aspects of quality. This is due to the fact that patients are unable to evaluate the technical aspects of quality because they lack medical knowledge and expertise (Babakus and Boller 1991). Consequently, patients evaluate quality of health care based on interpersonal factors, which are seen as part of functional quality. Similarly, a study by Jun et al. (1998), based on focus group interviews about differences in health care quality perspectives, has shown that the views of patients focus on functional quality attributes. In contrast, the views of health care professionals focused on technical quality attributes. Furthermore, Blumenthal (1996) emphasises interpersonal quality because providing services and procedures through a highly skilled staff makes patients feel that they are being treated in a humane and culturally appropriate manner. However, ignoring professional competences (human capital) and relationship with patients (relational capital) might negatively affect safety culture and patient safety (structural capital). In the case of Mid‐Staffordshire NHS Foundation Trust, the systematic failure between 2005‐2009 was because that the hospital lacked valuing human and relational capitals. According to Roberts (2013) senior management was determined to achieve financial and clinical targets with disregard for patient safety. The number of “qualified and trained staff” was reduced and patient feedback was ignored. The Francis report (2013) emphasised putting the patient first and effective system for handling complaints among other major recommendation themes. The intangibles that underpin the delivery of technical quality and functional quality might be drawn together into a checklist that would allow managers to appraise the extent to which intangibles are being optimised to support the delivery of quality (see Table 3). The checklist identifies specific intangible resources that integrate with each other in order to improve the quality of care provided to patients within hospitals. This suggests that maximum intangible resources are employed to improve quality in hospitals by encouraging hospital management to effectively identify them and build an environment where knowledgeable, experienced and highly skilled physicians and staff use information systems to provide appropriate and responsive care to patients that complies with current quality standards and guidelines, and to meet the patients’ expectations by enhancing interpersonal skills. Furthermore, intangible resources are overlapping in nature. Table 3: IR Checklist for hospital managers Technical Human Capital “Resources of occupational groups (hospital staff) required for performing work”

Structural Capital “Systems and programmes required for helping hospital staff to perform their work “

Functional

Knowledge Professional expertise Professional competences and skills Capabilities in healthcare delivery Information Systems Processes Safety culture Quality Management Programmes Guidelines

Human Capital “Skills of occupational groups (hospital staff) required in interacting with patients”

Staff Interpersonal Skills

Relational Capital “Stakeholders who perceive and evaluate quality and safety of care based on the functional aspect”

Patients

5.3 Comparison with the literature Other quality descriptions would be difficult to apply to the concept of intangibles. The system‐based framework of structure, process and outcome of health care settings proposed by Donabedian (1980) suggests that care quality is based on these three elements. In this framework, structure refers to the organisational factors that define the health system under which care is provided; process involves the interactions between users and the health care structure; and outcome is the consequence of the structure and the process of the system. Campbell et al. (2000) suggest that it is important to distinguish between structure, process and

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Hussain Hamed and Simon De Lusignan outcome. Therefore, they consider outcome as a consequence of process rather than a component of care. Similarly, they perceive structure as an approach through which care (process) is delivered and received. This model perceives quality as being technical in nature as its evaluation of quality is based on the physician’s point of view. In general, health care professionals assess quality of care in terms of the “biological status” of the patient (e.g. blood pressure) and, as such, an outcome can be under their control. However, these sole metric outcomes ignore other aspects of care quality that are emphasised by other stakeholders, such as the emotional and social aspects in the process of care delivery (Piligrimienė and Bučiūnienė 2008). Furthermore, there are other aspects of technical and functional quality. Whilst the focus is on those aspects that are intangible in nature, other elements can be important in delivering quality. However, these elements are considered to be tangible and, therefore, not considered in this study. For example, patients might focus on facilities, cleanliness and hospital food (see Table 2), which can help patients evaluate hospital quality.

6. Limitations of the method The intangible resources identified in Table 3 are derived from the literature. Moreover, there is no evidence that these identified IR may have an impact on improving the quality of care within health care settings. Other IR that might have an impact on quality improvement may not have been explored yet in the literature. As this study identifies intangible resources based on a technical and functional quality perspective, there might be other intangible resources that could be identified using a different quality perspective. In addition, other components of IR might be important in improving quality in a different health care setting, as this study is limited to acute hospitals. The technical and functional quality perspective has its limitations. Its intangible elements only focus on the competences and skills of the health care staff and the interactions between health care providers and patients. With limited empirical studies exploring IR in health care, it was important to conduct the literature review based on assumption. Therefore, the perspective of technical and functional quality was adopted, assuming the intangible nature of some of the elements. This assumption was useful for identifying IR related to quality in health care.

7. Call for further research This paper attempted to identify intangible resources that are vital in the delivery of quality in acute hospitals based on technical and functional quality perspectives. The next stage of this study will focus on testing the proposed checklist in an acute hospital using the realist evaluation approach developed by Pawson and Tilley (1997). Further research can be conducted to explore the impact of IR on the quality of health care based on a different perspective or in different settings. Furthermore, prospective testing of the proposed checklist to examine its effect on different health systems is needed.

8. Conclusion The literature review has shown that the importance of IR is clearly considered within health care settings and a good awareness exists with regard to the impact of IR on a hospital’s performance. However, with the rising challenges that hospitals face in terms of improving quality of care, there was a need to address some of the quality aspects within health care settings and explore whether they can be linked to IR. The literature review on IR and quality of health care showed that links can be established by identifying the key elements of IR. An exploration of the service quality attributes of health care has shown that both technical quality and functional quality include a number of elements that are intangible in nature. Therefore, this paper proposed a framework linking IR to the service quality attributes of health care. However, there is a need to explore how the identified IR can improve the quality of health care in hospitals. Therefore, another study will be conducted to test the proposed checklist proposed in this paper within acute hospital settings. The adopted framework resulted in identifying specific IR. These are knowledge, professional expertise, professional competences and skills, information systems, processes, safety culture, quality management programmes, guidelines, interpersonal skills and patients. These are types of IR that have a relatively significant impact on improving the quality of care. The checklist of intangibles should help health care managers appraise the use of IR in improving the quality of care provided to their patients.

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Organizational Employee Seen as Environmental Knowledge Fractal Agents as a Consequence of the Certification With ISO 14001 Ionut Viorel Herghiligiu1, 2, Luminita Mihaela Lupu1, Cristina Maria Paius1, Christian Robledo2 and Abdessamad Kobi2 1 Gheorghe Asachi Technical University Of Iasi, Romania 2 istia, University Of Angers, France herghiligiuionut@gmail.com Abstract: The paper aims to analyze at the organization level ‐ organizations that has implemented an environmental management system ‐ EMS ‐ (designed in accordance with ISO 14001) and such as it can be considered at least, theoretically speaking, adaptable to the internal change that occurred ‐ the relationship established between (a) EMS, (b) the environmental knowledge management process, and (c) employees of the organization ‐ considered to be (c1) environmental knowledge fractals agents (adapted idea after Shoham S. and Hasgall A. in 2005) and (c2) as one of the most important factors which leads to successful the integration and the functionality of the EMS. The main objectives of this paper are to: 1. to present the idea that organizational employees can be seen as dynamic fractals agents, and thereby connecting the fractal philosophy with environmental management; 2. present a theoretical research model, based on the following: (a) the relationship between: a1. environmental objectives and targets of the EMS – a2.environmental knowledge management process and – a3.environmental knowledge fractals agents; (b) the relationship between: b1.internal/external responsiveness to different organizational needs (emergency situations / demands of stakeholders and so on) – b2.environmental knowledge management process and – b3.environmental knowledge fractals agents; (c) the relationship between: c1.different environmental responsibilities (environmental planning/ monitoring the environmental performance, and so on) – c2.environmental knowledge management process and – c3.environmental knowledge fractals agents; (d) the relationship between: d1. allocation of resources for specific activities at ems level – d2. environmental knowledge management process and – d3. environmental knowledge fractals agents; (e) the relationship between: e1. personal development level in environmental management issues – e2. environmental knowledge management process and – e3. environmental knowledge fractals agents; 3. test this proposed model by applying questionnaires on organizations in the department of Maine‐et‐Loire, Angers, France. Keywords: environmental knowledge fractal agents, environmental management, ISO 14001

1. Introduction Starting from the statement that “knowledge integration is the engine of economic prosperity” (Tiwana, 2002) it can be deduced that the essence of social progress is the level in which an organization integrates various types of knowledge in the different internal systems, and especially at the human resource level. “Knowledge” has become the success factor for an organization ‐ a major advantage, internally (because leads to responsible decisions at any organizational level) and externally (in the competitive environment level). At international level is promoted sustainable development of all systems, reducing pollution and environmental performance evaluation (Gomez‐Mejia, 2009; Kassinis and Vafeas, 2006; Lupu et al, 2012; Nawrocka and Parker, 2009) taking into account that worldwide accelerated economic development in recent decades generates enormous “pressure” on the environment, causing pollution ‐ negative impact on the environment (Herghiligiu and Lupu, 2012; Herghiligiu et al, 2013a). Organizations today interact together in an environment that is changing very quickly, and so in order to be effective must have knowledge about customers, market trends, about the different technologies that could be used to solve various problems and must have implemented various management systems (certified as example with ISO 14001 / ISO 9001 / ISO 18001), and so on. Taking into consideration this characteristic external environment of organizations ‐ constant change ‐ human resource is the most important asset owned by an organization (the most important resource). Therefore the employee knowledge and the ability to respond quickly and effectively to any change can make the difference between failure and success of an organization. Today, more than ever, given that the (a) modern technological resources and (b) the continuing need to adapt to the external environment, organizations constantly develop the knowledge management process. At the

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Ionut Viorel Herghiligiu et al. same time, considering that the principles of sustainable development of organizations play an increasingly more important role in the overall management, it can be said that "the key" that can contribute to the success of the organization are the employees who own environmental knowledge that meets the current needs. The environmental knowledge owned, the response time and the efficiency of the response of human resource in an organization, contributes fundamentally to the implementation and also to the functionality of an Environmental management system (EMS). Therefore the accumulation and the transfer process of relevant knowledge for the employee’s activities have become extremely important in terms of organizational management (Von Krogh et al, 2000; Shoham and Hasgall, 2005).

2. Environmental knowledge management, EMS and environmental knowledge fractal agents 2.1 Environmental knowledge management Environmental knowledge management summarizes all the methods, mechanisms, processes, structures, policies, strategies, people, data and information that have the ability to create, capture, collect, store, query, use and transfer of environmental knowledge, with the main purpose to: (a) minimize the negative impact on the environment, (b) reduce the potential environmental risk as a result of the activities of organizations (in normal and abnormal operating conditions), (c) leads to environmental benefits; and the environmental knowledge as including: the environmental data and information analyzed, judged and understood correctly by environmental decisional agents, environmental situational experiences gained over time (by the organizational employee), different environmental ideas and suggestions occurred from inside/ outside the organization, and so on (Herghiligiu and Lupu, 2012; Herghiligiu et al, 2013a; Finster et al, 2011). Environmental knowledge can be considered as to be the knowledge that include specific ideas and concepts about the environment management and about the manner of how stakeholders of an organization know to put into practice. [e.g. environmental knowledge about:

environmental legislative framework (maximum levels allowed) and

about the best methods and techniques for assessing and controlling the emission levels of pollutants;

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;

practical and theoretical knowledge about processes and methods to improve the functioning of different process;

ideas and strategies on improving processes;

knowledge about the internal audit mission (methodology);

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;

knowledge on the best techniques for developing and updating the environmental management programs, and so on]; (Herghiligiu et al, 2013b).

Environmental knowledge in an organization can be considered as generated/ held by employees as a result of: (a) experience in various environmental activities conducted over time, (b) the various emergency situations that occurred and were managed, (c) training (university studies in environmental science/ trainings in environmental issues), (d) exchange of experience in other organizations, (e) direct/ indirect transfer of environmental knowledge from senior managers to other employees, and so on.It should be noted that an important role is played also by the modern technologies owned and used in organizations to build and transfer the environmental knowledge.

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2.2 Environmental management system (EMS) at organizations level Primarily an organization should be defined as the sum of all existing systems at internal organizational management level, either standardized or non‐standardized, which work together in a synergistic manner to achieve the proposed objectives (a complex entity). Therefore an organization can be seen as a system of interconnected systems (on different levels) that utilizes the resources of the organization and which are designed, implemented, integrated, monitored, audited and adapted to be efficient in achieving the organization's objectives and targets (Figure 1). GENERAL LEVEL

THE ORGANIZATION AS A COMPLEX ENTITY

A-0 n 1 2 3 x

A0 2

DETAILED LEVEL

Legend: n - overall objectives of the organization - accomplished or designed; *at level A0: 1. Environmental Management System; 2. Quality Management System; 3. Occupational Health and Safety Management System; x - other management systems at the organization level *at level A1: 1. environmental objectives and targets management; 2. response capacity management (internal/ external); 3. environmental responsibilities management; 4. environmental knowledge management; 5. resource management for specific activities at the EMS level; 6. development of human resource management involved in environmental management; x - other issues

Figure 1. The organizations as a complex entity Regarding to the EMS of an organization is it could be consider to be a set of elements by which is ensure the exercise of management process for achieving the proposed objectives and targets (the case of management by objectives – EMS, and not only). Would approach this definition because it describes the environmental management system, closer to reality (for a general definition) the environmental management system. An EMS can be described as a methodology through which organizations operate in a structured way in order to ensure environmental protection. This defines the impact of the organization and then proposes actions to reduce them. Consequently the aim of EMS is to control and continuously reduce these effects ‐ negative impact – o the environment (environmental negative impact) (Herghiligiu et al, 2012). Following the same reasoning (the organization is considered as a system of systems), it can be said that at the existing management systems level, it can’t be found a series of interconnected processes that manages the key aspects of each system for a good functioning. Therefore in terms of environmental management system internally, it is considered as important the following processes: 1. environmental objectives and targets management; 2. response capacity management (internal/ external); 3. environmental responsibilities management; 4. environmental knowledge management; 5. resource management for specific activities at the EMS level; 6. development of human resource management involved in environmental management; (Figure 1).

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2.3 Environmental fractal agents (organization’s human resource) As can be seen from Table 1, the knowledge management process determines the efficiency and effectiveness of human resource, organizational functionality, and in the conditions of flexible systems architecture provided the basis for the ability to respond quickly and effectively to environment change. Table 1: The knowledge management process and its links at the organization level (particularities) AUTHOR

TYPE OF THE STUDY

SAMPLE STRUCTURE

CONSIDERED AS RELEVANT ISSUES

Poliotis, 2001

interview

227 employees and managers

*it is established a link between the employee's ability to generate knowledge and nature of internal processes of the organization / management style * knowledge management process requires a direct link between the manager and the intellectual capability of each employee

Davenport si Prusac, 2000

questionnaire

70 managers

*it’s a very important link between the knowledge management and the functionality of organizations *the knowledge management process support and help’s in achieving employees objectives, employees from different departments and also in finding solutions to environmental requirements ‐ through the generation of new knowledge

Shoham si Hasgall, 2005

interview

18 managers and 42 employees (6 organizations)

*it’s a link between the characteristics of complex adaptive systems and the ability of organizations to manage knowledge *knowledge management process is the basis for the complex adaptive systems ability to respond quickly and effectively to environmental changes

Managers of organizations, in generally, must develop a process of integration between the direction of actions taken by employees and the necessary resources for these activities and between organizational needs and general fact (actual orientation) of the organization (Shoham and Hasgall, 2005). This process mentioned above should have as effect an improvement in the response given by the organization to the environment change, taking into account the various changes reported by employees over time. However this evaluation‐ analysis process creates a delay in the response to changing environment (specifically: increase the time between the employee response and environmental changes which occurred). It should be noted that this process developed by the managers with decisional power is a "consequence" of the hierarchical structure of organizations. In another way of thoughts, the management systems that function on hierarchical considerations, are suitable for organizations that “produce” in a stable environment, but not for modern organizations which exist in a dynamical environment that rapidly changes to short periods of time. Traditional hierarchical management systems are easy to understand and have a low degree of redundancy, but (1) are unable to be flexible to external inputs (beneficial for the organization in certain conditions), (2) have a major difficulty to observe negative and inefficiency aspects at all hierarchical levels, and not least, (3) cannot change the pattern that was designed (structure design) (Ryu si Jung, 2003a/b; Herghiligiu et al, 2012). The employees of an organization can be considered as organizational entities [it can be considered organizational entity: the employees, the departments, or even a part of an employee's work that is orientated to achieving specific environmental goal (Herghiligiu and Lupu, 2012)], and considering that they (or a part) develop various environmental activities, it’s necessary to continually search for new methods/ techniques/ philosophies even, that can improve the overall efficiency and the effectiveness (or clearly explain certain phenomena). Implementation and integration of an Environmental management system in an organization (in general speaking and focusing the human resource) modify, or it should modify the current activity of employees

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Ionut Viorel Herghiligiu et al. through: (a) adding new environmental responsibilities in the job description, (b) the deployment of various training activities in environmental issues, (c) the development of different environmental activities, (d) specific environmental situations management based on specific procedures, (e) use of new techniques and methods (specific environmental management) for planning/ implementation/ monitoring/ verification/ analysis of organization activities, (f) change the goals and targets of the employees, and so on. Taking into account the human resource functions performed at the EMS level, and given that an employee to respond quickly and effectively to environmental changer must have: autonomy (up to a point determined by the management of the organization), flexibility, hold intellectual capital (knowledge intellectual capital), capability to perform multiple functions, configurability (to the environmental responsibilities that could be change depending on the needs of the organization and considering environmental objectives and targets), adaptability to the environment, the human resources should be explored from another angle Considering the above mentioned ideas it can be said that an employee of an organization that has been certified with ISO 14001 can be described as a fractal organizational entity that creates/ accumulate/ transfers/ utilizes/ and even transform environmental knowledge (environmental knowledge fractal agent). Even if organizations comply with the hierarchical structure typology (considering the previous ideas), implementation of an Environmental management system and certification to ISO 14001 leads to a "dynamic transformation" of a part of the human resource activity (employees' and managers). This effect is not an only a consequence of the issues presented above (of points a‐f) but rather a consequence of the principle of continuous improvement, a principle that produces medium and long term effects at the EMS level.

3. Proposed methodology for the analysis of the characteristics and links developed by an employee of an organization certified with ISO 14001 seen as environment knowledge fractal agents The research methodology approaches the manner in which is achieved the scientific knowledge and focuses on all the elements involved in the research, and its main purpose is to substantiate (step by step) correctly the knowing process (Lazarsfeld, 1965). The purpose of this paper is to provide a relevant research methodology, starting from the literature and from various experts in the field of environmental management, with the purpose to assess and to analyze the different characteristics of the employee/ managers and the relationship between organizations employees/ managers and the environmental knowledge (through a different approach – as fractal agent/ entity) as a result of the certification of organizations with ISO 14001 (Figure 2). It is proposed that this methodology (with matrix developed above ‐ Table 2) to be used as a starting point for further analysis developed by other researchers, or even by the managers of organizations interested in the subject, and it can be adapted to the particular features of the organization/ organizations. The methodology testing aimed primarily the proposed constructs testing – constructs that has as basis the matrix of aspects presented above. Therefore was constructed a questionnaire with 21 items, and using SPSS 16 was tested the level of confidence of the questionnaire applied to a sample test (17 online questionnaires was received from organizations from the department of Maine‐et‐Loire, Angers, France); the statistical analysis revealed that the proposed questionnaire is valid, observing that Cronbach Alpha=0,825 (Table 3). It is also necessary to specify that starting from this proposed matrix presented above its possible to build questions (items) with the purpose to develop a questionnaire and also it is possible to be developed questions with an open answer that could be used in a focus group or for in‐depth interviews (of employees/ managers).

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Ionut Viorel Herghiligiu et al. IDENTIFYING THE PURPOSE Increase the efficiency of environmental activities performed by employees of an organization

FOR SUB-OBJECTIVE O1 (proposed aspects)

IDENTIFYING THE MAIN OBJECTS AND SUB-OBJECTIVES

FOR SUB-OBJECTIVE O2 (proposed aspects)

1. Organization as a system 2. EMS seen as complex system 3. Environmental knowledge fractal agents 4. Environmental knowledge management process

1. Environmental objectives and targets of the EMS 2. Responsiveness (eg. emergency situations/ demands of stakeholders, and so on) 3. Environmental responsibilities 4. The allocation of resources for the specific activities of EMS 5. Development of human resources involved in environmental management

RESEARCH DESIGN MATRIX OF THE DIFFERENT CHARACTERISTICS AND LINKS DEVELOPED BY EMPLOYEES OF AN ORGANIZATION; (IF EMPLOYEES ARE ENVIRONMENTAL KNOWLEDGE FRACTAL AGENTS)

PROPOSED LINK’S: 1. link between (a): a1. environmental objectives and targets of the EMS – a2.environmental knowledge management process and – a3.environmental knowledge fractals agents; 2. link between (b): b1.internal/external responsiveness to different organizational needs (emergency situations / demands of stakeholders and so on) – b2.environmental knowledge management process and – b3.environmental knowledge fractals agents; 3. link between (c): c1.different environmental responsibilities (environmental planning/ monitoring the environmental performance, and so on) – c2.environmental knowledge management process and – c3.environmental knowledge fractals agents; 4. link between (d): d1. allocation of resources for specific activities at ems level – d2. environmental knowledge management process and – d3. environmental knowledge fractals agents; 5. link between (e): e1. level of personal development in environmental management issues – e2. environmental knowledge management process and – e3. environmental knowledge fractals agents;

O1. Identifying the "constant" aspects from the level of the links developed by employees of organizations seen as fractals agents of environmental knowledge (the considered links "actors")

O2. . Identifying the "dynamic" aspects from the level of the links developed by employees of organizations seen as fractals agents of environmental knowledge

DESIGNING A RESEARCH MODEL

PROCESSING AND ENCODING THE DATA

DETERMINATION OF SAMPLE

QUALITATIVE AND QUANTITATIVE ANALYSIS OF COLLECTED DATA

CONSTRUCTS AND TOOLS DESIGN FOR INVESTIGATIONS

VALIDATION OF THE INVESTIGATIVE TOOLS FINAL CONCLUSIONS INVESTIGATIVE TOOLS APPLICATION

Figure 2: Research methodology regarding the different characteristics and links developed by employees of an organization Table 2: Proposed aspects for the different characteristics and links developed by employees of an organization; employees seen as environmental knowledge fractal agents (Research matrix for the considered aspects) I. To analyze the link between (a): a1. environmental objectives and targets of the EMS – a2.environmental knowledge management process and – a3.environmental knowledge fractals agents; 1. Possibility of identifying the employee perceptions about the organization's environmental strategy 2. Possibility of identifying the employee perceptions about imput/ transformations/ EMS output 3. The method to determine the environmental objectives of the employees and of the organization 4. Establishing the connection between environmental responsibilities of employees/ managers and the objectives and targets

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PROPOSED ASPECT

1. Organization as a system 2. EMS seen as complex system 3. Environmental knowledge fractal agents 4. Environmental knowledge management process

Ionut Viorel Herghiligiu et al.

PROPOSED ASPECT

II. To analyze the link between (b): b1.internal/external responsiveness to different organizational needs (emergency situations / demands of stakeholders and so on) – b2.environmental knowledge management process and – b3.environmental knowledge fractals agents; 1. Organization as a system 1. Identify the methods and techniques through 2. EMS seen as complex system which the organization manages environmental 3. Environmental knowledge fractal agents changes 4. Environmental knowledge management 2. Identify the methods and techniques that process EMS manages the relationship between environmental change ‐ organization/ emergencies/ stakeholder dynamics that influence the relationship organization ‐ environment 3. Identify the process that provides solutions to various environmental problems 4. Identifying the level in which employees/ managers provide independent and immediate solutions to various environmental problems III. To analyze the link between (c): c1.different environmental responsibilities (environmental planning/ monitoring the environmental performance, and so on) – c2.environmental knowledge management process and – c3.environmental knowledge fractals agents; 1. Organization as a system 1& 2. Identification of independent processes 2. EMS seen as complex system of employees (at the employee responsibilities 3. Environmental knowledge fractal agents ‐ planning or even assessment environmental) 4. Environmental knowledge management 3. Identifying the processes of informing process employees about environmental decision making 4. Establishing the level of information (up to date) of the employees (and between employees) on environmental activities undertaken IV. To analyze the link between (d): d1. allocation of resources for specific activities at ems level – d2. environmental knowledge management process and – d3. environmental knowledge fractals agents; 1. Organization as a system 1. Identify the accessibility to necessary 2. EMS seen as complex system resources for environmental management 3. Environmental knowledge fractal agents 2. Identifying the relationship between 4. Environmental knowledge management necessary/ allocated resources for an normal process functionality EMS 3. Identify the methods of distributing resources for EMS 4. Identify the report: resource allocation/ necessity of the employees to perform environmental activities V. To analyze the link between (e): e1. level of personal development in environmental management issues – e2. environmental knowledge management process and – e3. environmental knowledge fractals agents; 1. Organization as a system 1. Identifying the use/ development/ 2. EMS seen as complex system internalization of environmental knowledge 3. Environmental knowledge fractal agents generated by employees; identifying the 4. Environmental knowledge management environmental knowledge that are creating at process the SMM level 2. Establishing the level in which the organization supports environmental trainings/ environmental specialization for the employee 3. Establishing the level of environmental expertise of the employees of the organization (type levels of environmental knowledge held by employees/ managers) PROPOSED ASPECT PROPOSED ASPECT PROPOSED ASPECT

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Ionut Viorel Herghiligiu et al. Table 3: Reliability statistics Cronbach's Alpha N of Items .825

4. Conclusions Organizations today are experiencing different situations due to various environmental changes and also because of the lack of flexibility. Therefore organizations constantly are looking new methods and new techniques through become more adaptable to the environment, more flexible in front of encountered situations. It can be said that the implementation of an Environmental management system in an organization transform employees/ managers routine and stimulates the creation of new environmental knowledge to solve various situations encountered over time. It can also be considered that the principle of continuous improvement is the driving force to create new environmental knowledge and to the EMS permanent adaptation/ actualization (even to the long term flexibility of the EMS). However until it is reconsider the architecture of the organization and the implementation and operation of EMS (through decentralization of objectives and targets – based on fractal philosophy (Herghiligiu et al, 2012)) the employees/ managers (who have/ or could have specific attributes of fractals agents who manages environmental knowledge) will not be able to respond quickly and effectively to changes occurring in the environment. Considering that managers and employees have different responsibilities and carry out various activities (including environmental responsibilities and environmental activities) it may be consider these knowledge fractal agents (including environmental knowledge) are the central factor in the ability of organizations to create and manage complex systems and processes. It can also be consider and the fact that the higher is the level of environmental knowledge created/ acquired/ transferred/ used/ and converted by the employees/ managers engaged in environmental activities increases, it increases the quality of EMS functionality, and thus the problem of environmental awareness improves substantially (and even here it can be considered that exist a point of maximum ‐ determined by x factors related to the particularities of organizations). It should be noted that (1) even if the Environmental management system could be more flexible by changing the modality of integration in an organization (transformation of the current operation) through a new operational philosophy (fractal philosophy) ‐ and thus it would be decentralized (1) the environmental decision‐making process and (b) the establishing process of environmental objectives, and (2) even if it has the prerequisites of environmental knowledge fractal agents/ workers, without an intensive environmental trainings/ without specialization of employees and managers, there are plenty of chances that this effort will not achieve the maximum of expected results.

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 Davenport TH, Prusak L., (2000) Working Knowledge: How Organizations Manage What They Know, Harvard Business School Press: Boston MA. Finster, M., Eagan, P., Hussey, D., (2011) “Linking industrial ecology with business strategy: creating value for green product design”, Journal of Industrial Ecology, Vol. 5, pp 107‐125. Gomez‐Mejia, L.R., (2009) „Environmental performance and executive compensation: an integrated agency‐institutional perspective”, Academy of Management Journal, Vol. 52, No. 1, pp 103–126. Herghiligiu I.V., Lupu M.L., Budeanu B., (2013b) “Research regarding the informational system (information and knowledge) required for an environmental manager”, Paper that will be read at 14th European Conference on Knowledge Management ‐ ECKM, Kaunas, Lithuania (under publication). Herghiligiu, I.V., Lupu, M.L., (2012) “Performance analysis methodology of environmental knowledge at organizations level”, Paper read at 13th European Conference on Knowledge Management ‐ ECKM, Cartagena, Spain, Book II, pp 1402‐1410.

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Ionut Viorel Herghiligiu et al. Herghiligiu, I.V., Lupu, M.L., Robledo, C., (2012), “Necessity of change environmental management system architecture – introduction”, Supplement of the Quality‐Access to Success Journal, Vol. 13, S5, pp. 175 – 178. Herghiligiu, I.V., Lupu, M.L., Robledo, C., Kobi, A., (2013a) “Conceptual research model of factors that influence environmental knowledge management at organizational level”, [online] Applied Mechanics and Materials on: http://www.scientific.net; (under publication). Kassinis, G., Vafeas, N., (2006) Stakeholder pressures and environmental performance, Academy of Management Journal, Vol. 49, No. 1, pp 145–159. Lazarsfeld, P, (1965) Methods de la sociologie, Mouton et Co., Paris. Lupu M. L., Trofin O., Trofin N., (2012) „Environmental performance ‐ part of management performance”, Environmental Engineering and Management Journal, Vol. 11, No. 2, pp 393‐405. Nawrocka, D., Parker, T., (2009) „Finding the connection: environmental management systems and environmental performance”, Journal of Cleaner Production, Vol. 17, pp 601–607. Politis J., (2001) “The relationship of various leadership styles to knowledge management”, Leadership & Organization Development Journal, Vol. 22, No. 8, pp 354–364. Ryu, K., Jung, M., (2003a) Fractal approach to managing intelligent enterprises: Creating Knowledge Based Organizations, in Gupta, J.N.D., Sharma, S.K. (eds.), Idea Group Publishers, pp. 312–348. Ryu, K., Jung, M., (2003b) “Agent‐based fractal architecture and modeling for developing distributed manufacturing systems”, International Journal of Production Research, Vol. 41, No. 17, pp 4233‐4255. Shoham, S., Hasgall, A., (2005) “Knowledge Workers as Fractals in a Complex Adaptive Organization”, Knowledge and Process Management, Vol.12, No. 3, pp 225–236. Tiwana, A., (2002) The knowledge management toolkit ‐ Orchestrating IT, strategy, and knowledge platforms. Upper Saddle River, NJ: Prentice Hall. Von Krogh G, Ichijo K, Nonaka I., (2000) Enabling Knowledge Creation: How to Unlock the Mystery of Tacit Knowledge and Release the Power of Innovation, Oxford University Press: New York.

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Career and Knowledge Management Practices and Occupational Self Efficacy of Elderly Employees Chandana Jayawardena1,2and Ales Gregar1 1 Faculty of Management and Economics, Tomas Bata University in Zlin, Czech Republic, 2 Faculty of Agriculture, University of Peradeniya, Sri Lanka. chandanacj@gmail.com, gregar@fame.utb.cz Abstract: Organizations are paying increased attention on Career Management practices, in their quest for organizational growth and the career development of employees. Knowledge sharing has been found an important weapon of sustaining competitive advantage and improving performance. Occupational self‐efficacy reflects the conviction of a person’s ability to fulfill his job related behavior competently. The role of career management practices adopted by organizations has a special significance to the occupational self‐efficacy with the progress of employees in their careers. Study has focused the impact of Career and Knowledge management practices adopted by Czech organizations of thirty elderly employees holding administrative positions. Major objectives of the study were to examine the effects of Career and Knowledge management practices to the occupational self‐efficacy of elderly employees. Impact of gender was also examined. Descriptive and inferential analyses of data were conducted using the SPSS package. Career and Knowledge management practices of organizations were identified and assessed. Major factors contributing to the occupational self‐efficacy of elderly employees were assessed. Findings revealed a positive relationship of Knowledge management practices to the Occupational Self Efficacy, and career development of elderly employees. Study provided insights to improve the occupational self‐efficacy, and longevity of elderly employees. Keywords: career management, knowledge management, occupational self efficacy, elderly employees

1. Introduction World is facing demographic developments. It has been predicted of elderly employees (above 50 years of age) to form a significant component of the workforce in the near future, especially, in Europe, and North America. This outcome can be attributed to a combination of shrinkage of the overall workforce, and an increase in the relative number of employees in higher age groups. This leads to many scenarios in the workplace participation and performance capacities of the elderly employees of the workforce across the world (Ilmarinen, 2006, p.550; Vaupel and Loichinger, 2006, p.1911). There are some prejudices against the productivity of elderly employees. However, it has also been found that workplace career management initiatives have been effective in improving the productivity of elderly employees. Age has been found positively related to the proactivity on the job (Veldhoven and Dorenbosch, 2008, p. 112). Effectiveness of knowledge sharing is being discussed as a facilitator of work.

1.1 Knowledge management An organization can be defined as a ‘cognitive enterprise that learns and develops knowledge’ (Argyris, and Schon, 1978). This concept has been signified by referring professionals as ‘Knowledge Employees’. Effectiveness of organizations depends on their ability of adapting to environmental events. Organizational strategic behaviours have reported a direct impact on organisational performances. The shared perspective of organisational abilities and their strategic behaviours in adapting to environmental events is described as a general knowledge structure that incorporates dominant management logic (Prahalad, and Bettis, 1986, p.486). Organisations get exposed to knowledge structures through environment. As organisations gather more exposure, learning opportunities become wider. Complex situations facilitate expertise in the related knowledge structures. Knowledge Management (KM) is the practice of capturing and developing (individual and collective) knowledge within an organization, and using it to promote innovation through transfer of it, and continuous learning (Davenport, De Long, and Beers, 1998, p.45; Quinn, Anderson, and Finkelstein, 1996, p.72). The term KM has been discussed as the process of collecting and identifying useful information (i.e. knowledge acquisition), transferring tacit knowledge to explicit knowledge (i.e. knowledge creation or transfer), storing the knowledge in the repository (i.e. organizational memory), disseminating it through the whole organization (i.e. knowledge sharing), enabling employees to easily retrieve it (i.e. knowledge retrieval) and exploiting and usefully applying knowledge (i.e. knowledge leverage) (Beckman, 1999, p.1; Gordon, 20007, p.157; Martin, 2000, p.19; Nonaka, and Konno,1998, p.42). KM can also be seen as a means of developing

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Chandana Jayawardena and Ales Gregar organizational effectiveness and competitiveness. Grizelj (2003, p.375] has viewed it as “an approach for identifying, capturing, creating, and applying knowledge with the aim of improving competitiveness through new innovative KM strategies”. ‘‘Knowledge’’ in hospitality operations can be defined (Yang, and Wan, 2004, p.595) as the knowledge related to a company’s customers, products and services, operational procedures, and key stakeholders. It could be found in employees, stored in documents/papers, or located in types of electronic devices. Where the employee turnover rate is high, establishing knowledge sharing and storing is the primary step of pursuing the KM best practices. Marriott International Hotel chain provides an example for the application of ‘‘knowledge sharing and retention’’ practices. They installed a ‘‘codification System’’ to virtually convert what all its employees knew about hotel day‐to‐day operations and standard operation procedures, in order to provide consistent customer services (Gupta, and Govindarajan, 2000, p.73) . Company also designed a reward system for those who shared, created and mobilized new knowledge relevant to their business. Enhancing the customer loyalty by paying ‘attention to details’ in customer encounters and providing personalized attention in return visits has proven vital. In a study (Ghalia, and Wang, 2000, p.390) related to the development of a knowledge based system, combining hotel managers’ judgments of future room demands with traditional forecast techniques was focused. Therein the need for more ‘soft forecasting models’ was identified. It highlights the importance of the tacit knowledge of hotel managers and employees about the future state of their companies. With the advent of KM, it has become imperative for organisations to think of more interactive (non‐linear) forecasting systems for greater effectiveness.

1.2 Career management In a highly competitive global environment it has become increasingly difficult and costly to attract employees with the desirable skills. Organisations need to convince employees that they are looked after with more opportunities, challenges and rewards than their competitors. This can particularly be challenging when the traditional rewards offered as part of the old psychological contract, such as structured career paths and job stability, are more difficult for organisations to provide due to the many challenges organisations have to confront within their competitive templates. Organisational support in employees’ career satisfaction is opportune in the context of employees’ efforts in their own career success in a more individualistic career management trend during the recent years (Baruch, 2006, p.128). Differentiating the impact that organisational and individual initiatives have on career satisfaction offers the opportunity to merge the ‘two distinct perspectives provided by (employee‐focused) vocational psychology, and (employer focused) organizational psychology’ (Lent and Brown, 2006). In the employees perception of career success (where the criterion is internal than external) ‘the change in the career context’, where individuals are expected to self‐ manage their own careers rather than rely on organisational direction’, is vital (Hall and Chandler, 2005, p. 160). A study (Barnett and Bradley, 2007, p.633) on organizational support for career development (OSCD) found a positive relationship between career management behaviours and career satisfaction. Findings suggested that individuals benefit personally from engaging in career management behaviours. It inferred that ‘employees’ proactive personality, via their career management behaviours and OSCD’ contributes significantly to the career satisfaction of an employee. Further, it recommended enhancing of employees’ perceptions of OSCD through a combination of formal programs, and providing informal support for the employee’s career development as well. Empirical evidences indicate that OSCD initiatives that promote the individual benefits associated with career management behaviours of employees, and encourage them to be engaged in these behaviours, may experience greater success in facilitating employee career satisfaction.

1.3 Occupational self efficacy Self‐efficacy has been defined as the “people’s judgments of their capabilities to organize and execute courses of action required attaining designated types of performances” (Bandura, 1986, p. 391). Individuals hold self‐ beliefs that allow them to apply self‐control over who they are, and what they want to be. Occupational Self Efficacy (OSE) reflects the conviction or the confidence of a person’s ability to fulfill his or her job related behaviour at a perfectly acceptable level to the employer. However, OSE has shown to be less stable in comparison to general self‐efficacy. This is due to the fact that OSE might be influenced by the (mostly the immediate) previous experience of an employee. Schyns and von Collani (2002, p.221) have indicated that OSE is ‘broad enough to allow comparison’ between different types of jobs or professions. This qualifies it as an effective tool for investigations in the context of work and organisations. Schyns and Von Collani (2002, p.238) observed OSE having positive correlations with job satisfaction, and organizational commitment. Rigotti et al. (2008, p.251) found positive correlations, existed among OSE, job satisfaction, and job performance in five different countries. Abele and Spurk (2009, p.59) found that OSE measured at the career entry level of a

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Chandana Jayawardena and Ales Gregar person had a positive impact on salary and status three years later. When they examined after seven years from the career entry level of employees they found a positive relationship with their salary change and career satisfaction. This indicates that the level of OSE before entering the labour market might be important for the career development of professionals. The relationship between OSE and preparedness for change can be considered in the context of organizational change processes. Self‐efficacy had positively influences adaptation to change as a positive predictor of self‐initiated change in organisations (Armenakis et.al, 2000, p.650). Self‐ efficacy is appropriate and relevant in the organizational context as it is related to performance (Judge and Bono, 2001, p.86). It has been found that people with a higher sense of self‐efficacy persist longer in the face of obstacles and set themselves more challenging goals. Researchers (Heckett and Betz, 1981, p.335; Osipow et al, 1993, p.18) have found significant differences between men and women with regard to their behavioural and response patterns in OSE. Ridgeway (1997, p.230) found a lack of involvement of women in decision‐ making, especially in critical areas, to reflect a more subtle and covert form of gender discrimination prevalent in the western society. Schyns and Sanders (2005, pp. 520) found that the relationship between self‐rated transformational leadership and self‐efficacy is lower for women. In an Indian study (Gupta, and Sawhney, 2010, p. 21) conducted among the private and public sector executives, private sector males had a higher OSE over females and public sector executives.

1.4 Scope of the Study

Socio‐Demographic Covariates Age Gender Qualifications Designation/ Status

Career Experience Job Experience

Employees Occupational Self‐Efficacy

Knowledge Management Practices

Career Management Practices

Source: Authors’ impression based on literature review Figure 1: Conceptual framework of the study The central focus of this study revolves on the impact of organisational KM and career management practices to the OSE of elderly (more than 50 years old) employees. By focusing on OSE of elderly employees the aim of the study was to extend the theory of OSE to the longevity of careers. That posited the two major research questions of this study. They were, "Is there a positive relationship between KM practices and OSE of elderly (50+) employees?", and "Is there a positive relationship between career management practices and OSE of elderly (50+) employees?" These questions were topical as the elderly population (especially the elderly work force) is increasing throughout many geographic regions.

2. Methodology 2.1 Selection of respondents Respondents of this study comprised of 30 ‘elderly employees’, all of whom were over 50 years in age. They were randomly selected from three private sector organizations situated in Zlin, Czech Republic. Respondents were briefed about the purpose of the research, and confidentiality of their responses was ensured. Survey was conducted using a questionnaire. It consisted of mostly summative statements using likert, and semantic scales. Questionnaires were administered in groups for self‐responses on the basis of anonymity.

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2.2 Measurement scales Occupational Self Efficacy of Employees: The long version of the Occupational Self‐Efficacy scale developed by Schyns and von Collani (2002) consisted of 20 items taken from four different scales. A six items scale (shortened from the original scale and successfully tested in many regions) by the same authors was selected for the study (Rigotti et.al, 2008, p.252). Items could be rated on a six‐level response scale ranging from 1 (not at all true) to 6 (completely true). High values reflect high occupational self‐efficacy. Organizational Knowledge Management: KM was measured using a scale consisting of eight statements modified from the unpublished PhD dissertation of Zheng (2005, p.134, 135). Original scale had 15 statements, and the deficiencies were minimized in the modified scale. Responses to the eight items were based on a six‐ point scale which ranged from never (1) to always (6). Career Management Scale: The career management scale (Sturges et.el, 2002, p.747) was used to measure the organizational career management practices. Responses to the ten items were provided on a six‐point scale which ranged from never (1) to always (6). Control variables: Respondents’ demographic and human capital information were collected with single item questions for gender, age, career experience, highest level of education completed, organizational tenure, and work (managerial) position (e.g. Senior, middle level, junior). Age was assessed as a continuous variable. Gender was treated as a dichotomous variable with 0 for females and 1 for males. Educational level was measured by using a country‐specific combination of school and vocational education, which was then positioned on three levels for the purpose of this study.

2.3 Data analysis SPSS computer software was used for the descriptive and inferential data analysis. Multivariate regression analyses were conducted to test the relationship among study variables. Exploratory factor analysis was conducted to ascertain the validity of the constructs of KM, and Career management practices. Cronbach alpha was used to measure the reliability of constructs. Dummy variables were created for ordinal data.

3. Results and discussion 3.1 Respondents’ characteristics Respondents consisted of 15 males and 15 females from three organizations. Age of the respondents varied from 50.67 years to 62.92 years. Mean age was 56.40 years with a standard deviation (SD) of 3.81 years. Employees had a career experience of 24.13 years with a SD of 3.27. Their job experience in the present organization varied from 5 years to 18 years. Great majority (93.3%) of them had been employed in the present organization for over 10 years. They recorded a mean value of 12.33 years employed in the present organization with a SD of 2.75. Majority (73.3%) of the respondents had completed the high school education, but only 26.7% of the respondents had obtained a university degree. Majority (63.3%) of the respondents were holding junior administrative positions in their respective organizations. Twenty percent of the respondents were occupying middle level managerial positions, whilst 16.7 % of the respondents were in senior managerial positions in their organizations.

3.2 Organizational knowledge management, and career management practices Table 1: KM and career management practices in organizations Parameter Mean SD Mode Range

Knowledge Management 31.30 3.91 31 16 (26 – 42)

Career Management 35.90 3.95 34 16 (31 – 47)

Source: Authors’ (Survey data) Respondents’ scores for KM and Career Management in their respective organizations are depicted in table 1. KM scale varied from a minimum score of 8 to a maximum of 48, and the respondents have indicated a mean

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Chandana Jayawardena and Ales Gregar score of 31.30 for the KM practices in respective organizations, with a lowest of 26 and a highest of 42 scores. Career management scale varied from a minimum score of 10 to a maximum of 60. Respondents have indicated a mean score of 35.90 for the career management initiatives offered to them by their organizations. Both these scales recorded strong internal reliabilities for a with Cronbach Alpha values of 0.917, and 0.889 respectively for KM and Career management constructs.

3.3 Occupational self‐efficacy of respondents OSE of the respondents was measured using a six item scale with a minimum score of 6 and a maximum score of 36. This indicated the self‐perception of an employee on the level of success in individual job performance and future career in the organization. Respondents’ scores for OSE varied from 21 to 29, with a mean score of 24.67 and a SD of 2.37. OSE scale reported sound internal reliability of the construct with a Cronbach Alpha value of 0.826.

3.4 Significant associations of the study Study had two main hypotheses stemming from the two major research questions. 3.4.1 Organizational KM practices and OSE of employees Hypothesis 1: Organizational KM practices are positively related to the OSE of elderly employees Occupational self‐efficacy of elderly employees = 2.297+2.160 * Gender (males) + 2.488 of organizational KM practices. The above relationship was significant at 0.05 levels. The Pearson correlation value (r) between KM and OSE was 0.723, with R2 value of 0.523, indicating that organizational KM practices have contributed to 52.3% of the variation of the OSE of elderly employees. Results also indicated that males are at an advantageous position in the above relationship. The model consisted of control variables (i.e. Age, Gender, Academic qualifications), organizational work position (designation), career experience, Organizational tenure, and the organizational KM practices. OSE of employees was the dependent variable. The model had an R value of 0.815 and R2 of 0.664, indicating the models strength (i.e. 66.4%) to predict the variability of outcome (OSE of employees). Adjusted R square of 0.536 indicated the generalizability of the model. F value of 5.182 indicated the predictive validity of the model. Durbin Watson test statistic of 1.589 assumed the tenability of independent errors. VIF and Tolerence statistics (0.988, and 1.011 respectively) assumed there was no multicolinearity. 3.4.2 Organizational career management practices and OSE of employees Hypothesis 2: Organizational career management practices are positively related to the OSE of elderly employees The analytical model consisted of control variables (i.e. Age, Gender, and Academic qualifications), organizational work position (designation), career experience, Organizational tenure, and the organizational career management practices. OSE of employees was the dependent variable. This model had an R value of 0.782 and a R2 of 0.611. F value of 4.131 indicated the predictive validity of the model. Adjusted R square of 0.463 indicated the lack of generalizability of the model. However, there was no sufficient evidence to suggest a significant contribution from organizational career management practices to the occupational self‐efficacy of the elderly employees.

4. Conclusions 4.1 Major findings of the study Study concludes that the organizational knowledge management practices contribute to the improvement of the occupational self‐efficacy of elderly employees. In an increasingly ageing global work‐force this can be seen as an important initiative that will enhance the job performance of elderly employees. This would also contribute to benefit the organizations in harnessing the best from the tacit knowledge of elderly employees, which could prove crucial in the value addition in a competitive environment. Organizational career management practices did not record a significant contribution to the OSE of elderly employees. This could also be due to the lack of understanding (of respondents) of career management initiatives in their absence in respective organizations.

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4.2 Recommendations Human Resource Management activities facilitating organizational KM practices and initiatives can be effectively utilized as a strategic tool for the positive value additions in organizations. This will also serve to benefit the society by enhancing the OSE of the senior citizens.

4.3 Limitations and further research This study had been limited to only 30 elderly employees in a semi urban city. Conducting a similar study with a larger number of respondents will improve the generalizability of the findings. It will be insightful to compare the impact of gender, educational qualifications, and the position in organizational hierarchy (designation) to employees’ OSE with larger comparable groups. Further studies consisting of younger employees (below 50 years old), and employees from different socio‐demographic backgrounds will prove insightful.

Acknowledgements This contribution was written within the framework of the TAČR (Technology Agency of the Czech Republic) grant‐maintained project TAČR OMEGA TD 010129 and with the financial support of TA ČR. Authors are thankful to the TAČR Grant for providing financial support for this publication.

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Easy, Economic, Expedient – an Effective Training Evaluation Model for SMEs Sajid Khan and Phil Ramsey School of Management, Massey University, Palmerston North, New Zealand Skhan2@massey.ac.nz p.l.ramsey@massey.ac.nz Abstract: Firm overall performance highly depends on its human capital. This potential therefore needs to be maintained and improved by various development strategies. Training in this regard is esteemed to be the most effective measurement to reach this aim. However, the effectiveness of training highly depends on training design and its evaluation. Especially SMEs hesitate to invest in training due to financial restrictions and concerns of training benefits. Therefore there is a need for a training evaluation model which not only is simple and affordable but also appreciates the fact that training is a complex web of relationships between trainer, trainee, content, work environment and organisational goals. To formulate such a model a three‐step process was adopted which facilitated filtering out the appropriate assessments. The resulting evaluation model is capable of taking into account factors lying inside as well as outside the training realm while still playing a crucial role in the effectiveness of a training program. Thus, besides assessing the effectiveness of training it even works as a navigation tool for designing future training programs. Finally, the proposed training evaluation model was applied on a training program in a small‐scale service oriented organisation and the analysis of the results showed that the model successfully attained the anticipated aims. Keywords: human capital development, training evaluation model, SME training, and training efficiency

1. Introduction Intellectual Capital (IC) shows manifold facets depending on the viewing angle. IC can be seen as collective knowledge (Kong & Thomson, 2009), intellectual material (Stewart, 1997), subset of intangible assets (Hunter, Webster & Wyatt, 2005), knowledge based resources (Ordonez de Pablos, 2005) and many other nuances are expressed in the literature (Hamza & Isa, 2010; Sujan & Abeysekera, 2007). IC is predominantly perceived as a tripartite framework (Stewart, 1997; Yaghoubi, et al., 2010). This perspective views regards IC as a composition of three primary interrelated components ‐ internal capital, external capital and human capital (Johnson, 2005; Ordonez de Pablos, 2003). Whereby internal capital, refers to all the intelligence embodied in the structure and processes of a firm (Petty & Cuganesan, 2005). Similarly, external capital encompasses all the relationships ‐ market relationships, power relationships and cooperation – a firm has with outside stakeholders (Capello & Faggian, 2005; Petty & Cugansen, 2005). Lastly human capital refers to knowledge, skill, capabilities, experience, attitude, commitment and personal characteristics of firm’s employees (Yang & Lin, 2009). Hence, IC refers to a firm’s repository of all the intelligence embedded in its organisational structure, network relationships and personnel (Choo & Bontis, 2002). Since the components of IC act and interact facilitating a firm’s decisive competitive advantage they all are considered vital for the survival and success for modern day firms (Bontis, Crossan & Hulland, 2002; Ordonez de Pablos; 2003; Hamza & Isa, 2010). However, it is worth pointing out that human capital is viewed rather more significant than its counterparts (Kong & Thomson, 2009). It is because human capital not only is generatively intangible (Athonen 2000) but rare and hard‐to‐imitate (Stiles & Kulvisaechana, 2003) and has relatively more potential than other constituents of IC in terms of influencing its counterparts (Huang & Hsueh, 2007). The attainment of adequate human capital is therefore purported being central to firm success in the existing global economy (Roslender & Fincham, 2001). It is then of utmost importance that human capital attains its potential through organisational development strategies that stimulate and improve the skills, knowledge and learning capacities of the individuals that make up an organisation (Pfeffer, 1998). Training in respect of achieving this goal is considered the most important agent since it facilitates firms enhancing their human capital value (Barton & Delbridge, 2001; Hamza & Isa, 2010). Modern day firms therefore funnel a significant amount of their budgets into training to upgrade the skills and knowledge level of their workforce which eventually translate into the improvement of firms’ bottom‐line performance (OECD, 2002; Bassi & McMurrer, 2004). Despite the fact that training is deemed essential for all types of organisations, yet small and medium‐sized enterprises (SMEs) unlike large businesses invest quite meagrely in it (Westhead & Storey, 1997). Some of the major reasons for this phenomenon are SMEs’ limited financial abilities (OECD, 2002),

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Sajid Khan and Phil Ramsey unawareness of owners regarding the benefits of training and owners' concern that costs incurred on training may surpass its returns (Huang & Hsueh, 2007). Two of these three above mentioned reasons – owner ignorance of training benefits as well as their underestimation – are to a great extent related to the effectiveness of trainings within the context of SMEs. Although there is a huge body of literature discussing a multitude of training design and evaluation models, very limited attention is given to the effectiveness of training in the context of SMEs (Huang & Hsueh, 2007). Consequently, most of the training evaluation models discussed in the literature are complex, labour intensive and time consuming (Stevenson, 2012). All this necessitates the need for a simple, less costly and workable training evaluation model for SMEs. Not only should the model be able to assess the effectiveness of an existing training program but also provide a roadmap for designing future training programs.

2. The formulation of the proposed evaluation model There is an ample literature describing and proposing various models for the evaluation of a training program (Wang & Wilcox, 2006). However, it is important to note that every evaluation model has its own assumptions, purpose, rational for inclusion and exclusion of variables and intended audience which not only differentiate it from others but confine its effectiveness to certain specific contexts (Mathison, 1994, p.469). Wang and Wilcox (2006) for that reason heralded the identification of the right evaluation approach which can effectively correspond to the specific needs of the situation to be the core challenge for those theorists and HRD practitioners who plan to evaluate the training program. This paper aims to propose a training evaluation model for SMEs. The model was originally formulated for a small scale service‐oriented organisation to assess the effectiveness of their orientation training and identify bases for improving the program. Apparently, the main objective for firms to undertake trainings is to bring positive change in the behaviour by means of improving knowledge and skill levels of their employees (Kirkpatrick, 1998). The learning of skills and knowledge, however, largely depend upon the conduciveness of a learning environment (Tam, 2000). Literature published Alvarez et al. (2004) Kraiger (2002) and Kirkpatric (1998) hold that trainees’ satisfaction with the training program is not only positively correlated with their learning of skills and knowledge but also with their behaviour at workplace. Keeping forgoing in view, the basic premises set out for the development of the proposed evaluation model were to have a simple and less resource‐intensive framework that could evaluate training in its existing shape. The model should be able to effectively assess the actual and desired accomplishments of a training program in terms of trainees’ satisfaction with the training, learning improvements and change in trainees work behaviours. Furthermore the model should also be able to determine whether the parameters, trainees, facilitators, processes and the concerned management followed the right course of action in facilitating the training to achieve its anticipated outcomes. However, if the training deviated at some points then the model should not only detect those gaps but also provide guidance for rectifying the identified issues. To formulate such a model the following three‐step process was adopted.

Step 1: Selecting the most suitable evaluation approach from various available approaches ‐ formative, summative, confirmative and meta‐evaluation (Fraser, 2008).

Step 2: Identifying a range of potential assessment for the proposed model after assessing the appropriateness of assessments proposed in some of the popular training evaluation models

Step 3: Comparing the shortlisted assessments to select the most suitable ones and aggregating selected assessments into single implementable training evaluation model.

The forgoing brief descriptions of the four broad evaluation approaches disclose that three of them ‐ formative, confirmative and meta‐evaluation are incongruent with the main objectives of the model. Formative evaluation as well as meta‐evaluation are quite complex in nature and also require a considerable amount of resources for their applications. Moreover, formative evaluation demands interference with the implementation of the training program and meta‐evaluation requires previous evaluation studies for its use. As far confirmative evaluation is concerned, although it is relatively simple and does not require meddling with neither the intervention nor it needs previous evaluation studies for its execution but it also requires substantial resources in terms of labour and time. Thus, all the three evaluation approaches fail to be used as yardsticks to critically evaluate the appropriateness of individual assessment levels of various evaluation models. Summative evaluations unlike its counterparts perfectly match the requirement of this study. Thus, it is used as a basic standard along with the other presumed aims of the model to assess the suitability of each assessment proposed in various selected training evaluation models.

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Sajid Khan and Phil Ramsey Table 1: Four main types/approaches of learning evaluation

In this step some of the popular and effective training evaluation models are randomly selected. Each stage of the selected model is assessed for its evaluation approach and intended assessment objectives. Those assessments are shortlisted for the formulation of the model that are using the relevant evaluation approach – the summative approach ‐ and whose objectives match any of the objectives set for the desired model. Table 2: Summary of all shortlisted training evaluation models

3. Measuring the trainees' perceived effectiveness of training The “Reaction” level/stage/phase proposed in Kirkpatrick’s model (1967), Philip’s model (1996), Kaufman’s model (1995), Alvarez et al. IMTEE model (2004) and CIRO model (1970) seek to measure trainees satisfaction with the training programme. They all employ the same evaluation approach – Summative, hence they all are shortlisted for the proposed model. The shortlisted assessments are also the same in terms of their procedure and assessment objectives. In other words, they all were all are basically the same assessment so the need to compare shortlisted assessments to identify the best assessment rendered itself unnecessary in the existing situation. As the assessment corresponds perfectly to one of the objectives ‐ to effectively assess the satisfaction level of the trainees with the training, set out for the proposed model so selected as the level 1 of

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Sajid Khan and Phil Ramsey the proposed model. This level seek to assess the perception of trainees about the three main dimensions of a training program ‐ training delivery, training content and training environment. Training delivery here refers to the process employed by a trainer to deliver training contents to trainees (Lather, 2009). Whereas training content implies the knowledge or intellectual property of the training program (Kirkpatrick, 1998). Lastly, training environment which means the physical environment in which training takes place (Busari, Verhagen & Muskiet, 2008).

4. Measuring the learning improvement To assess the improvement in learning “Learning” level/stage of Kirkpatrick’s model (1967), Philip’s model (1996), Kaufman’s model (1995) and Brinkerhoff’s model (2003) are shortlisted. Like reaction assessment, all the shortlisted assessments are same in terms of their approach, objectives and methodologies and can adequately serve the one of the other main objectives ‐ measures the actual state of benefits in terms of skill and knowledge, of the proposed model. Consequently, the level 2 of the proposed model intends to evaluate the improvement in the two major dimensions of learning ‐ knowledge and skills.

5. Measuring the improvements in behaviour Literature identifies two main conditions for the change in trainees’ behaviour to adequately manifest itself (Kirkpatrick, 2005). First, the trainees must be able to utilise their newly learned skill and knowledge. Second, the work environment must be conducive for the application of newly learned behaviours. As behaviour assessment takes place after the learning assessment, understandably, if the learning evaluation indicates some improvement while behaviour assessment fails to show the same level of improvement then it may be because the climate of the organisation unsupportive of the new behaviours. Consequently, the cause/s of this dilemma needs to be searched in the climate of the organisation rather than in the training environment. For the proposed model “Behaviour” level of Kirkpatrick’s model (1967), Philip’s model (1996) and Kaufman’s model (1995), the “Usage & Endurance of Learning Stage” of Brinkerhoff’s model (2003) and Behaviour Gap Model (2001) as a whole are shortlisted. However, contrary to the first two assessments, there is a huge variation between the objectives and methodologies of the shortlisted assessments while assessing trainees’ behaviours. The assessments proposed in Kirkpatrick’s model (1967), Philip’s model (1996) and Kaufman’s model (1995) do not outline any proper method for analysing the climate of an organisation so neither can comprehensively assess the change in trainees work behaviours nor can identify those factors that lie outside the training realm but negatively affect the effectiveness of a training program. The remaining assessments, on the other hand, take into consideration the climate of an organisation. Brinkerhoff’s model (2003) in its “Usage & endurance of learning stage” takes into consideration all the important events that take place from training space to actual work environment to identify when, where, how well, and how often which trainees use which part of their learning (p.130‐133). This process enables training evaluators to measure how and to which extent the effect of training lasted in terms of improving behaviours. Although this assessment is able to identify deficiencies training design, peer support at trainings. The Behaviour Gap Model (2001) views the actual work environment to be made up of myriad different variables most of which lie beyond the realms of training but can strongly affect workplace behaviours of a trainee (Ramsey, Macky & Mason, 2001). This model proposes a two‐step process for trainees’ behaviour assessment. In the first step information is collected to identify behaviour at four different dimensions ‐ organisation‐desired behaviours, trainer‐targeted behaviours, trainee‐intended behaviours and trainee workplace behaviours. In the second step a series of gaps are identified by comparing 1. Organisation‐desired behaviours to trainee workplace behaviours, 2. Organisation‐desired behaviours to trainer‐targeted behaviours, 3. Trainer‐targeted behaviour to trainee‐intended behaviours, and 4. Trainee‐intended behaviours to trainee workplace behaviours (Ramsey, Macky & Mason 2001). This assessment not only provides enough information about the actual improvement in the workplace behaviour of trainees but also ascertains the causes of deviation of the training from achieving the required behavioural objectives. Moreover, it also assists in developing effective behavioural objectives for future training programs. The forgoing description makes it quite clear that the Behaviour Gap Model can address the objectives of the desired model exceedingly far better than its counterparts. Consequently, it becomes level 3 of the model

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Figure 1: Proposed evaluation model along with assessment dimensions

6. The application of the proposed training evaluation model The research was conducted in one of the financial advisory service organisations based in Palmerston North, New Zealand. The organisation ‐ ANL (Advisory Network Limited) provides professional advice in various types of financial matters such as insurances, long‐term and short‐term financial investments and retirement plans. ANL conducted orientation training for its newly hired staff. To evaluate the effectiveness of the training program, the ex post facto study was undertaken which began in August, 2010 and ended in December of the same year. The span of 4 months provided the researchers with the opportunity to not only measure the trainees’ perception and learning but their behaviour after being working for more than 3 months in the actual work environment. The sample of this study was comprised of 12 research participants out of whom 8 were ANL staff members who received the training. The remaining participants included 2 trainers who designed and delivered the training and 2 members from the management of the company. All three level assessments of the proposed evaluation model were undertaken after the completion of the training program. For the first two levels ‐ reaction and learning levels ‐ a questionnaire was employed as sole investigation instrument. For the behavioural level, the questionnaire was complemented by interview, observation and relevant literature review. To develop valid and reliable research instruments for this research, all major dimensions of reaction, learning and behaviour levels proposed in the model were taken into consideration.

7. Instruments development Literature pertaining to HRD suggests the use of questionnaires as sole research instrument for assessing trainees perceived effectiveness of a training program (Kraiger, 2002; Alvarez et al., 2004). According to Gliem and Gliem (2003), a questionnaire with Likert scale is relatively more reliable than single‐scale or ordered‐ category item. Keeping the above‐mentioned in view, a well‐structured questionnaire with a likert scale was developed. The Likert scale of the instrument offered the responses ‐ excellent to poor, high to low and, strongly agrees to strongly disagree. The internal consistency of the instrument was measured through Cronbach’s alpha whose value arrived at 0.8343 which rendered the questionnaire to be a highly reliable research instrument. The instrument was composed of 13 items. The first three questions intended to extract the respondents’ perception about the usefulness of the training in terms of its delivery mechanism, course contents and training environment. These questions sought to obtain a quantitative reply as well as a subjective answer in the form of comments. The subjective responses were solicited to obtain data for devising recommendations. Question 4 to 9 of the questionnaire sought to measure the enhancement in the state of knowledge of the trainees. The remaining last five questions intended to find out the tangible improvement in the skill levels of the trainees. For behaviour level assessment various data extraction methods were employed to answer the following four questions.

What behaviours put trainers’ emphasis on?

What behaviours do trainees plan to adopt for application at the workplace?

What actual behaviours are rendered by the trainees at the workplace?

What are the organisation‐desired behavioural objectives?

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Sajid Khan and Phil Ramsey The first question deals with the desired/ideal behavioural objectives for the organisation so to answer it appropriately, the literature concerning prevailing best practices in selling financial services was reviewed and understood. Then, mission, policies and procedures of the concerned organisation were reviewed to know the organisation’s point of view and guidelines. Lastly, the senior management was interviewed to know their existing and upcoming performance requirements. The second question deals with trainer intention and effort toward organisation intended training objectives. Trainers were interviewed to probe in this dimension. The third question deals with the intention of the trainees regarding the application of learned behaviour at workplace. The trainees were asked through survey about their intentions in terms of implementing the learned behaviour. This survey was conducted just after the training finishes. The last question deals with the actual behaviour at workplace which is a result of training. Questionnaire along with interview were employed as research instruments to obtain the requisite information. Once these behaviours were identified, a simple subjective evaluation was undertaken to identify the series of gaps between the aimed objectives and the actual performance resulting from the training.

8. Data collection 8.1 Reaction and learning levels A total of 8 questionnaires were distributed and all of them were returned making a response rate of 100%. Questions 1, 2, 3, 4, 5, 6, 7, 8, 10, 11, 12 and 13, are positively worded, whereas, 9 and 14 are negatively worded. The responses received on negatively worded question were reversed. There was no blank response so the data acquired for these questions was easily tabulated and analysed through Statistical Analysis System (SAS) for descriptive statistical analysis. The subjective responses of the questions 1, 2 and 3 helped the researchers in devising recommendations for improving the upcoming orientation programs.

8.2 Behaviour level Establishing ANL desired behaviours Literature published by Alferd and Pamela (2008), Lambart, Ohai and Kerkhoff (2009) and Sujan, Weitz, and Kumar (1994) was thoroughly reviewed to understand the prevailing standard sales practices. Then, ANL’s mission, vision, policies and needs were keenly understood through reviewing relevant documents and interviewing the GM (General Manager) and the MO (Manager Operations) of the firm. The aforementioned assisted the researcher to identify and select potential desired behaviours. The behaviours were then discussed with the trainers and the management. Finally, ANL desired behaviours for trainees were established. Establishing trainer intended behaviours To establish trainer intended behaviours, one of the researcher participated in the orientation program as an observer. The researchers then thoroughly reviewed the training contents and interviewed the trainers. This enabled the researcher not only to fully appreciate the reasons and logic of the trainers behind the training contents but also the intentions of the trainers regarding behaviours they anticipated from trainees to be demonstrated in the actual environment. Establishing trainees intended behaviours Trainees intended behaviours were identified through information obtained from them via questionnaires and interviews. The questionnaire contained three questions asking trainees to reveal the behaviours they had learned, the behaviours they wanted to implement and the behaviours they learned but they presumed could not be implemented in the actual environment. The same questions were also asked through interview to ensure the validity of the data. These activities were carried out immediately after the training had finished. Establishing actual work behaviours To determine trainees' actual work behaviours a variety of instruments were used. In the first step, questionnaires were used asking trainees regarding each of those behaviours which the trainers anticipated of

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Sajid Khan and Phil Ramsey them. The trainees, GM and MO were then individually interviewed. Lastly, some of the trainees were also observed while delivering their job responsibilities in their actual work environment. All these activities were initiated three months after the training ended.

9. Results and discussion The analysis and findings of each evaluation level are separately given as follows

9.1 Reaction level Table 3 shows the responses of the trainees along with the summary of descriptive statistics for the reaction level. Table 3: Responses to the questions 1‐3

The individual mean scores reveal that trainees perceived the training content, which received the highest mean (M) score of 4.5, to be highly effective as compared to the training delivery methods and conduciveness of the training environment which received 4.1 and 3.8 scores respectively. The overall reaction level attained M = 4.1 with Min = 3.6 and Max = 4.6 and SD (Standard deviation) σ = 0.25. The values of M and SD clearly imply that though there is a low‐level disparity among the views of the trainees, as a whole the training is viewed as an excellent effort in terms of its relevance to their actual job, content delivery methods and conduciveness of environment for learning. The Min and Max values basically show the lowest and highest points in the data. In the existing case, the perceived effectiveness of training at its lowest point amounts to 72% and at its highest it reaches 92%.

9.2 Learning level Table 4 shows trainees' responses pertaining to learning in terms of perceived improvement in their knowledge and skill. The table also contains the summary of descriptive analysis of the learning level. Table 4: Responses to questions 4‐13

The mean value (M = 4.1) suggests that the trainees perceived training to be highly successful in adding value to their existing knowledge and skills. The value of SD (σ = 0.22) indicates that there is not much disparity among the perceptions of the trainees. The Min and Max value (Min = 3.7, Max = 4.3) suggest that the minimum improvement perceived by a trainee amounts to 74% and the maximum reaches 86%.

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9.3 Correlation between trainees satisfaction and their learning

Figure 2: Correlation between level 1 and level 2 In the existing case the correlation amounts to 0.80 at statistically significant margin with p‐value 0.0161. This implies that there exists a strong positive linear correlation between the two levels. In other words, trainees’ perception about training in terms of its content delivery methods, the relevance of training contents to their job, and conduciveness of the training environment for learning, is strongly linked with trainees' learning of the knowledge and skills that are presented during the training program.

9.4 Behaviour level Gap 1: For this gap, ANL desired behaviours were compared with trainers' intended behaviours. The comparison revealed that trainers only emphasized 40‐45% of the organisational desired behaviours. This huge gap between ideal behaviours and trainers' intended behaviours pointed toward some significant issues. First, ambiguity and vagueness on the part of the organisation with regards to the outcomes it desired to seek from the training. Second, insufficient and inadequate understanding of the trainers regarding the best sales practices and organisational requirements. Last, the lack of communication between ANL management and the trainers. All these accounted for a training content which was seriously deficient in emphasizing the requisite behaviours. To address this gap, ANL needed to take into consideration its desired behaviour and make radical changes to the existing training contents i.e. add new contents. Gap 2: To identify this gap, trainers' intended behaviours were compared with trainees' intended behaviours. The result revealed that there was a high level of consistency between these two sets of behaviours. This indicated that the strategies that were employed by the trainers to deliver training were quite effective. Consequently, training methodologies for delivering training were deemed appropriate and no considerable changes were suggested. Gap 3: Gap 3: This is the third level of analysis in which trainee intended behaviours were compared with their actual post‐training work behaviours. There was a small variation between these two sets of behaviours. This variation appeared due to two apparent contradictions between the trainees’ intentions and their actual work behaviours. The first contradiction was, the trainees intended that in the actual work environment they might not use some of the techniques. Conversely, the trainees were frequently using those techniques during their work environment because these techniques were essential for a successful completion of certain jobs. The second contradiction was, the trainees intended that they would use written scripts during tele‐selling. However, most of the trainees did not use such scripts during tele‐selling. The reason being that they gained enough experience due to which they could perform better without using scripts for tele‐selling. Beside these two contradictions, trainees were found to be using all those behaviours which are intended by them at the end of the training session. This analysis suggested that there was no major hillock that was impeding the transfer of learning from training space to the actual work environment. Gap 4: This is the last step of behavioural analysis in which ANL desired behaviours were compared with the actual post‐training behaviours of the trainees. There was a huge variation between these two sets of behaviours. As stated earlier, the desired behaviours are established after the training ended. Most of the identified behaviours are not conveyed to the trainers beforehand so they have not been focused initially during the training design phase, and later on, consequently, in the delivery phase of the training. Therefore, the training only achieved 40‐45% of the desired performance objectives. Although the training failed to achieve most of the organisation desired objectives, this failure could not be attributed to some specific stake holders or aspects of the training. Rather many factors within and outside the training environment contributed to this phenomenon. These factors included ambiguity and non‐clarity on the part of management about the organisation desired objectives, lack of communication between trainers and management, trainees’ unawareness regarding best sales practices and organisation desired objectives etc.

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10. Conclusion The evaluation model proposed in this paper is based on a system perspective which views training as a complex web of relationships between trainer, trainee, content, work environment and organisational goals (Stevenson, 2012). The broad perspective enables the model to provide a relatively deeper understanding in terms of identifying those factors that exist outside the realm of training but play a crucial role in the effectiveness of a training program. In the case at hand, the implementation of the proposed evaluation model resulted in not only pointing out but also assisting in providing bases for designing more effective training programs. The findings of this study entail some important implications for researchers, managers and trainers. This study shows that there is a strong link between training environment and participants learning. Moreover, the effectiveness of training not only is linked with effective inculcation of skills, knowledge and behaviours but is also connected with the requirements of the organisation. The researchers are therefore required to take these linkages in consideration while conducting research studies on training evaluation. For managers, this study suggests that they must be fully aware of the needs of their organisations along with the prevailing best practices. This will enable them to provide the requisite guidance to the trainings undertaken by their organisations. Finally, the trainers along with expertise in pedagogy and content knowledge also require sufficient interaction with the management to identify the actual performance objectives for which the training is undertaken. This study shows that the model successfully delivered the anticipated outcomes. However, it is important to note that the conclusion drawn here is mere suggestive because of the given limitations of the study. The study was conducted in only one organisation (Brinia & Efstathiou, 2012). Moreover, the size of the sample was rather small (Malik & Janjua, 2011). Consequently, the outcomes may not be applicable to other small and medium organisations. The second limitation is that the study lacked rigorous statistical techniques such as control group and longitudinal data (Westhead & Storey, 1997). This would have helped to report that the organisation who implemented the proposed evaluation model to their trainings have actually improved their organisation performance. This research contributes to the human capital literature by having developed a training evaluation model for SMEs. Future studies can analyse the same model on other organisations to test its effectiveness. This model can be an excellent tool for analysing the training effectiveness as well as planning future trainings.

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An Exploratory Study of Knowledge Management in the UK Local Government Planning System for Improved Efficiency and Effectiveness Nasrullah Khilji and Stephen Roberts School of Computing and Technology, University of West London, London, UK nas‐khilji@hotmail.co.uk Stephen.Roberts@uwl.ac.uk Abstract: This research paper provides some pragmatic evidence to review the linkage of knowledge management to technological and human resources for efficient and effective delivery of planning services in the UK local government. This exploratory research work commenced with the participation of five local planning authorities in the South East Midlands. Case studies have been used to explore the role of emerging technologies and consolidated ICT strategies within the planning service. The goal is to establish a better integrated knowledge based planning system. The study has allowed the examination of the contemporary state of the planning system and its transition towards smart and sustainable development. The work forms part of the fieldwork for a PhD study and has an exploratory goal which can acknowledge the complexity of this organizational environment. This investigation begins by examining the existing status of the local planning system by identifying the key supportive and preventative knowledge factors in both tacit and explicit domains. It is followed by a detailed discussion of how and when the smart and sustainable development can be initiated and where this would lead the local authorities in future. This is done by exploring the innovative communication channels, effective coordination strategies and integrated knowledge management initiatives in the five participating local planning authorities. The rationale for the fieldwork is to understand how participating local authorities are moving through the transition period towards smart and sustainable development. The main proposition is that an integrated knowledge based approach would lead the planning system towards smart and sustainable development. The local planning authorities are using emerging technologies to deliver their services with greater efficiency and effectiveness. In looking at the processes at work the relationships among major supportive and preventative knowledge factors and frequency of each category are discussed in order to identify a research framework. The outcome of this research is a hybrid socio‐ technological system for an integrated knowledge based planning system.

Keywords: knowledge management; local authorities in the UK; sustainable development; innovation; socio‐technical system; planning system; eGovernment

1. Background The local government planning system has been subject to continuous reform to enhance efficiency and effectiveness and to offer improved services for sustainable development. The planning system reform in the UK local government structure was initiated in the mid 1990s by posing fundamental questions on how to organise, manage and determine the delivery of quality public services. Since the 1990s more powers have been devolved to the Scottish, Welsh and Northern Ireland governments, so more accurately our focus is in planning in England. Nevertheless, it is suggested that the general propositions we explore are valid for each of the ‘home nations’. Among the selected participating authorities are Bedford Borough Council (BBC), Central Bedfordshire Council (CBC), Luton Borough Council (LBC), Milton Keynes Council (MKC) and Northampton Borough Council (NBC). Evaluation of the planning system in the participating local authorities has guided the researcher for the fieldwork strategy. The case study is considered a suitable and appropriate method for this kind of research. The research study has taken the form of a critical synthesis based on the field evidence, original conceptual modelling, evaluation of framework development and the emergence of a specification for the implementation of a proposed framework. This paper actually explores how an integrated knowledge based planning system can improve efficiency and effectiveness for a smart and sustainable development. The primary objective is to promote an open knowledge sharing environment for the UK local government planning system. A range of relevant primary and secondary data is collected to permit modelling, data synthesis and prototype development. The fieldwork undertaken has been applied to evaluate and validate the key research area and to test the perception that emerging technologies, whilst nominally strong in some areas, are not yet converging in fully integrated and effective ways to manage information as a basis for knowledge management, and furthermore to develop an innovative planning system which is smart, sustainable and better able to serve stakeholders’ requirements. This could be delivered through an Internet based online

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Nasrullah Khilji and Stephen Roberts planning service i.e. a planning portal. The planning system is studied to analyse the function of knowledge management for efficiency and effectiveness. The continuing rate of political, economical and social change in the local government suggests a requirement for an open knowledge sharing environment: a hybrid socio‐ technological system.

2. Studying the local planning system The researcher examined how the local planning officers share, transfer, manage and integrate knowledge. They need high levels of access so as to share necessary expertise to underpin appropriate and timely action. In this study the researcher is aiming to describe the requirements for an integrated knowledge based planning system within the five participating local Councils. The case study outcomes contribute to evaluating the local authorities’ ICT strategy to underpin smart and sustainable development. The participating authorities are examined through their planning systems demonstrating interaction of many functional areas: from initial enquiry through to final development plan approval. All these require co‐operation and collaboration between multi‐disciplinary experts, who need to communicate and exchange information. Document Management System (DMS), Customer Relationship Management (CRM), Geographical Information Systems (GIS) and Communication and Project Management tools are already in use in local authorities to deliver their services with better efficiency and effectiveness. The continuous reform in the planning system is expected to further enhance performance towards smart and sustainable development. This improvement is already speeding up the planning process from around 12 weeks to about 6 weeks elapsed time on applications. The integration of emerging technologies is not only an organisational need for information management; but it is also required to meet the need for managing, transferring and sharing of individual as well as team expertise. Knowledge processes go far beyond traditional information acquisition and processing by stressing the importance and creative potential of human expression, communication and learning for successful economic planning and meaningful personal and social existence (McInerney and Day, 2007). The emerging technology solutions, the convergence of knowledge management in eGovernment and increasing use of tacit knowledge transfer for enhanced innovation are new trends in the planning system (Van Heghe, 2011). One of the most revealing works on the vital role of knowledge in the innovation process was carried out by Nonaka and Takeuchi (1995). They presented a dynamic model for the creation of new knowledge that begins with deep tacit understanding, continues through the clarification of this blurred creative force in the form of an innovative process that ends with the absorption of new knowledge into the planning system as a whole. This twisting flow of knowledge and its creation offers thoughtful insights into the essential human aspect of innovation. This is referred to as the social interaction element: as a knowledge transfer through dialogue and conversation. It is important to appreciate that conversation does not take place within individuals but between individuals of an organisation (Nonaka and Takeuchi, 1995). In a technology enabled environment, the boundaries between products and services and across enterprises become blurred that create the context for entrepreneurship, innovation, and dissemination of knowledge (Dioguardi, 2009). The current research has examined the planning system in the participating local authorities to understand how planners share, manage and transfer their knowledge, which is usually embedded in tacit form within planning teams. It is clear that planners often cannot articulate problem solving activities as these involve ambiguities in social interaction as well as unforeseen interactions among different ICT tools. To improve and enhance the planning permission process, planners need to be able to articulate new knowledge with the help of existing expertise, technologies and operational procedures. It is also observed that they need to be able to explain the complete planning process concept to all stakeholders involved in the process. Cooper (1998) has described that sustained innovation relies heavily on articulated knowledge. Managers must be actively engaged in the emergent process of knowledge management implementation in a way that does not simply offer exhortations or ensure that the ICT infrastructure is working (Benbya, 2008).

3. Fieldwork methods Research data was collected throughout the fieldwork in three ways: literature review, environmental scanning and case studies under an exploratory research methodology with a flexible research design. The collected data was later analysed in three stages: literature analysis, identification of current trends and key propositions analysis. The researcher used three important techniques for data validation, which include: audit trail, triangulation and persistent observation in order to maintain the high quality research reliability and data

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Nasrullah Khilji and Stephen Roberts validity. It was considered necessary during this research study to collect and review both primary and secondary data from a wide range of sources including one to one discussions, interviews, online forums, email correspondence, questionnaires, printed publications, group discussions, electronic journals, books and website browsing. Representatives of local planning authorities who are sponsoring, organising and leading local and regional seminars were contacted at these local events; for example a series of joint seminars and workshops organised by South East England Development Authority (SEEDA), East England Development Authority (EEDA), South East Midland Local Enterprise Partnership (SEMLEP) and local Councils at various local venues in and around Milton Keynes. The process of recruiting the research participants began with the researcher making direct contact and then sending an expression of interest email. The initial contact was always followed up by emails, phone calls, requests for an appointment and going to seminars to meet up again. Some 48 staff members, performing their job in local authorities were approached to build contacts and to provide a firm basis for data collection and analysis. The volunteer participation rate in percentage terms from each local Council during the fieldwork is shown in the doughnut chart (Figure 1).

Figure 1: Overall participation in percentage from each participating council During the data collection phase, the field study was carried out through questionnaires, interviews, online forums and email correspondence in the five Councils. Out of 48 introductory and follow up contacts in five authorities finally 28 gave a positive response to participate in the field survey and data collection. The response success rate is 58.33%. From the start of fieldwork, to the end of data collection and through to the data analysis and evaluation phase, the researcher remains in frequent contact throughout the field study. In addition, the researcher has maintained regular contact with some eight senior planning officers among the five participating local authorities during this research study. Some 18 interviews are conducted during the field work. Number of Interviews, interviewees and local authorities’ participation in term of allocated interview codes and dates are listed in the linear graphical chart timeframe (Figure 2).

Figure 2: Field interviews’ timeframe for data collection: 2009‐2013

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Nasrullah Khilji and Stephen Roberts A mixed research methodological approach was adopted for the fieldwork. The participating authorities were given identification codes as per their specific name and particular order. Each interview was allocated a unique identification sub‐code and conducted within the specified local authority depending on the conducted interview sequence in a particular planning authority. For‐example BBC‐1.1: Bedford Borough Council, the first interview in the first participating local planning authority as illustrated in the bar chart (Figure 2). The total number of interviews conducted was 18; each interview was between 60‐90 minutes duration. Interviews were usually recorded and kept safe in a research audio folder for data retrieval, narrative, and analysis purposes. The percentage of responses from five participating authority is illustrated in the pie chart (Figure 3) with number of interviews conducted during the case study fieldwork.

Figure 3: Number and percentage of interviews conducted in participating Local Councils

4. The fieldwork narrative New knowledge should be captured and stored appropriately for others to access and learn from (Ramalingam, 2006). Knowledge management is seen as a challenging task in local government in order to meet ever rising expectations of service quality and value for money. The local government is currently going through a time of significant transformation as it comes to grips with the reduced funding available as a result of the UK government comprehensive spending review in 2010. This has resulted in an increased pressure on local government to work more efficiently and effectively within reduced budget and financial constraints. The UK local authorities expect that their ICT strategy will continuously help them to achieve their productivity through knowledge management and increasingly mobile flexible working solutions (Kirk, 2012). The researcher observed from field data that the participating local authorities accept a diminished base of physical resources and are prepared to substitute with their intellectual assets. From the field interviews and collected data, the researcher observed that local government is interested to achieve maximum benefits from emerging technologies. In order to achieve efficiency and effectiveness, ICT will enable the continuous sharing of knowledge and intellectual assets across a number of locations to provide planners with a flexible back office environment for the support staff behind the scenes and better delivery of services (Neill, 2012). Transcriptions of collected data are prepared for data analysis to assist the researcher in clustering the concepts and ideas about knowledge management in the planning system. Clustering involves searching the data for related categories with similar meaning. This analysis is known as thematic analysis and the main purpose during the beginning of the analysis is to look for themes (the key research propositions). When sets of themes are formed, more advanced analysis is employed to look for clusters and patterns among knowledge domains. During the analysis, any data that interpreted a significant meaning is extracted and given a code, which is then organised under key categories. These approaches are repeated for each interview session and the meanings are later organised into categories of main and sub codes. The participating local authorities, the key interviewees’ titles and interview codes are shown in the table (Table 1).

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Nasrullah Khilji and Stephen Roberts Table 1: Interviews, interviewees and local authorities’ interview coding Research Interviewee’s Job Title

Assistant Director (Planning and Housing)

Head of Planning Policy

Office Manager Planning Support Services

ICT Strategy and Development Manager Management Support Officer ‐ Service Development Sustainable Communities Director of Sustainable Communities

Assistant Director of Customer & Systems

CBC‐2.3

Head of ICT

CBC‐2.4

Policy Monitoring Officer Acting Technical Support Manager and Business Process Review Project Manager Development Control Planning Project Manager

10 11 12

Planning Enquiry Officer

Assistant Director of Planning

Joint Head of Development Management

Assistant Director IT and eGovernment

Principal Development Control Officer

Planning Delivery Manager

Head of Customers, ICT and Cultural Services

Local Council Code

Interview’s Sub‐ Code

S. No.

BBC‐1.1 Bedford Borough Council ‐ BBC

BBC‐1.2 BBC‐1.3 BBC‐1.4 CBC‐2.1

Central Bedfordshire Council ‐ CBC

Luton Borough Council ‐ LBC

CBC‐2.2

LBC‐3.1 LBC‐3.2 LBC‐3.3 MKC‐4.1

Milton Keynes Council ‐ MKC

MKC‐4.2 MKC‐4.3 MKC‐4.4

Northampton Borough Council ‐ NBC

NBC‐5.1 NBC‐5.2 NBC‐5.3

The field interviews are transcribed before the researcher ‘comprehended’ the information. Morse (1994), described ‘comprehending’ as an act of acquiring a full understanding of the setting and culture of the study topic before commencing a research. In this research study, ‘comprehending’ involved the researcher reading transcripts multiple times to get close to the data and to understand the issues underlying the interviewee narratives. From the field interviews and direct observation, the researcher soon realised that the pace of technological change is rapid and for this reason the local authorities of the future will be very different from how they appear today. As the economy increasingly moves towards a knowledge based economy, local authorities will need the ability to manage knowledge as a matter of competitive survival (Jashapara, 2004). Field interviews are used as templates but they are not viewed as fixed, because of the emerging and constantly changing dynamic pattern that is revealed and as the researcher’s understanding about the subject deepened as the study progressed. In this process, codes for the first interviews are continuously amended until the researcher conducted the last field interview. When the coding is finished, each case study participant is given their own primary, main and sub‐codes. Although the researcher tried to be consistent in interpretation, the researcher also allowed the coding and meaning expressed by the interviewees to be central for both narrative and data analysis. During data analysis, each research category is treated uniquely to calculate the frequency of supportive and preventative knowledge key factors. It is necessary to bring various supportive and preventative knowledge factors under tacit and explicit domains as explained in the tabular presentation (Table 2). Table 2: Main and sub codes for knowledge management tacit and explicit domains Main ‐ Codes

MC‐1: Knowledge Applications

Sub ‐ Codes SC‐1.1 ‐ ICT Infrastructure SC‐1.2 ‐ Network DMS ‐ CRM ‐ GIS ‐ ERP SC‐1.3 ‐ Storage and Retrieval of Data SC‐1.4 ‐ Microsoft Office Applications SC‐1.5 ‐ SMS and Smart Phone Apps SC‐1.6 ‐ Online Reports Provider SC‐1.7 ‐ Design and Plan Review SC‐1.8 ‐ Technical Specifications

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Category

Frequency

Explicit ‐ Supporters

20.34%

Nasrullah Khilji and Stephen Roberts Main ‐ Codes MC‐2: Knowledge Channel MC‐3: Individual knowledge MC‐4: Group Knowledge MC‐5: Knowledge Preventers

MC‐6: Knowledge Supporters

Sub ‐ Codes SC‐2.1 ‐ Face to Face Meeting SC‐2.2 ‐ Team and Group Working SC‐2.3 ‐ Creation of Knowledge Models SC‐2.4 ‐ Use of Smart Devices i.e. Wiki SC‐3.1 ‐ Sharing Individual Expertise SC‐3.2 ‐ Motivation and Willingness to share knowledge SC‐3.3 ‐ Strategy and Vision SC‐4.1 ‐ Planning Teams Coordination SC‐4.2 ‐ Team Relationships SC‐4.3 ‐ Creation of New Knowledge SC‐5.1 ‐ Non Sharing Knowledge SC‐5.2 ‐ Lack of Awareness SC‐5.3 ‐ Organisational Culture SC‐5.4 ‐ Political Uncertainty SC‐5.5 ‐ Time Factor SC‐5.6 ‐ Financial Constraint SC‐6.1 ‐ Planning Project Structure SC‐6.2 ‐ Communication Channels SC‐6.3 ‐ Category of Knowledge Teams SC‐6.4 ‐ Routine Activities Schedule SC‐6.5 ‐ Training and Development

Category Tacit ‐ Supporters

Frequency 13.56%

Tacit ‐ Supporters

12.43%

Tacit ‐ Supporters Tacit ‐ Preventers

15.82%

Explicit ‐ Supporters

25.42%

12.43%

The researcher tried to demonstrate the most significant groups of key knowledge factors as evidenced by the field studies. One way of identifying significance is by studying the interview transcription, looking at the whole context and issues in order to find things that are emphasised and stressed by the staff in each local authority. Another way of finding significance is by doing mixed data analysis to trace out the key factors with frequency of repetition. Numerical counting in this case study analysis is used to look at the frequency to assess the importance of each knowledge factor. The key assumptions are also applied in the data analysis, when the interviewee mentioned a particular term or issue, the most talked about topic is likely to be the important for data analysis. Three key research propositions are intensively investigated during the field work. These three main propositions are also extensively examined for key findings: (i) innovative communication channels (knowledge identification), (ii) effective coordination strategy (knowledge integration) and (iii) integrated knowledge management (knowledge creation). The six main codes for both tacit and explicit knowledge domains are illustrated in the diagrams with frequency of identified factors in percentage (Figure 4 and Figure 5) as explained in the table (Table 2). The prominent knowledge supporters and preventers are the dominant clusters with a high percentage of repetition. These are described in the research proposed framework (Figure 7) according to each participating local authority’s individual planning department circumstances and their ICT strategy in context of innovation, channel shift, smart and sustainable development.

Figure 4: Key supportive and preventative knowledge tacit and explicit factors’ frequency

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Nasrullah Khilji and Stephen Roberts

Figure: 5: Key knowledge factors in descending order The main clusters of key knowledge supportive and preventative factors are identified from interviews and field data in relation to the primary research objective. The crucial step of narrowing the research focus is fulfilled by selecting relevant local authorities to participate in identifying, integrating and creating knowledge to classify major research themes and sub clusters. From the ongoing planning reforms in the UK eGovernment agenda, the planning portal is chosen to study online planning for an integrated knowledge based planning system with embedded technological applications such as GIS, DMS, ERP and CRM. Moreover, the online internet based system that has already shown a reasonable extent of success is selected from a range of public eGovernment reforms in the UK local government. This selection of emerging eGovernment projects provided a suitable base to understand variation in order to identify the limits for generalising from the field study. The selection and investigation of the participating local authorities’ planning system has depended on research theoretical motives rather than statistics. In the case study, multiple data collection channels are applied including interviews, e‐mail correspondence, online forums, questionnaires, environmental scanning, field notes, literature and documentary data. The mixed data sources have provided a stronger validation of the collected field data. This research study has depended on both qualitative and quantitative evidence, using a mixed methodological approach, although the foundation of the empirical research framework is mainly created from qualitative data.

5. The emerging research framework During the initial stage of field data collection, a preliminary conceptual framework was developed to explore the key supportive and preventative knowledge elements based on fundamental research propositions i.e. coordination, motivation and training. This model provided a crucial foundation to develop the empirical research framework to review the comparison between conventional and contemporary planning systems with an emerging technological advancement for knowledge management. The field study also examined the theoretical and conceptual frameworks to obtain a better understanding, particularly to identify and integrate supportive and preventative knowledge factors.

Figure 6: PKOT‐mdel‐I, ‘process, knowledge, organisation and technology

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Nasrullah Khilji and Stephen Roberts It is clear from data analysis that cross‐functional team coordination in various planning units is the essential part of the planning process to support integration between technological and human resources. The challenging issue to understand here is about how the stakeholders in the planning process identify, manage and share their knowledge and how they package them together in cross‐functional planning activities for improved performance. It is also important here to identify the relationship between disconnected functional areas to assess and express how KM occurs between planning teams in several planning disciplines. From this point of view it is important to understand how knowledge is identified, integrated and re‐created and what information systems are required in future to promote tacit and explicit knowledge in the planning system to achieve efficiency and effectiveness. The empirical framework is developed from the case study data analysis, which is based on the key research propositions. The research framework is further modified to develop a framework with clusters of key supportive and preventative elements in order to understand an integrated knowledge based planning system. The modified version of the proposed framework is based on four fundamental elements: planning process, knowledge management, organisational culture and technological exploitation. This research framework illustrates the planning system transformation with both tacit and explicit knowledge domains influenced by supportive and preventative factors as shown in the proposed framework diagram (Figure 6). The next stage in progressing, this research framework has been to review the efforts being undertaken in participating local authorities in the South East Midlands. Some local authorities are at a transition period of their planning process reforms involving consideration of current eGovernment strategy, which is already initiated from the central government (Department of Communities and Local Government). The rationale for developing this research framework is to gather initial insights about the main factors that influence the knowledge based planning system initiation, development and implementation. The research framework aims to confirm the findings, which the researcher obtained from the literature review and from the growing empirical evidence base.

6. Planning system transformation After conducting the scheduled field interviews, the research framework was modified according to the further field data collection and evaluation of case study evidence. Some of the supportive and preventative clusters remain constant throughout the process of initiating; developing, establishing, enhancing and analysing the framework for online planning services along with other eGovernment features that support an integrated knowledge based planning system. Thematic and conceptual clusters with both supportive and preventative elements are identified for a future planning system ‘To‐Be’ state as illustrated in the framework diagram (Figure 7). The role of knowledge management in the planning system reform varies significantly depending on the configuration and arrangement of local authorities and their planning teams and how they share their expertise within organisational structure. In general, the planning system must align the available resources to facilitate an active knowledge management strategy in different planning processes. Planning officers, who are engaged in a planning process, are usually confronted with a variety of challenges during the planning application progress because a single document needs attention, verification and approval from various local government departments. In the current circumstances, future development management techniques must combine functional expertise with high technological integration capabilities for diverse planning disciplines. This combination of expertise can be created through knowledge management applications. This research study describes that KM in planning system is influenced by several factors, which are identified and categorized as supportive (positive elements) and preventative (negative elements) that directly or indirectly affect identifying, articulating, sharing, transferring, recreating and managing knowledge key factors for planning system transformation. For a planning system transformation, it is important to create an atmosphere that supports the aspiration to build group expertise from singular expertise during the planning permission process. The planning system is the key activity in the local government that requires linking ICT tools for various processes in an improved planning management information system. From the data analysis an integrated knowledge based planning system framework has been developed. The developed and proposed model is not a simple solution of

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Nasrullah Khilji and Stephen Roberts integrating different ICT tools and their applications together; rather it contains the demanding integration of tacit versus explicit knowledge between different planning functional areas. The proposed exploratory framework is the process modelling to consider an integrated knowledge based planning system. It is the planning system transformation from the conventional situation (which is an old approach in many ways) to contemporary perspective (which is a recently implemented approach), emphasising customer oriented and online internet based services. From the fieldwork, key knowledge elements are identified as driving forces for effective and efficient planning system. The key supportive factors identified as driving forces during literature review and the analysis of conceptual models are vision, strategy, leadership, public demand and financial resources. The researcher also identified major preventative factors as main preventers in an integrated knowledge based planning system such as political, social, economical, technological, environmental and legal challenges as shown in the proposed research framework illustration (Figure 7).

Figure 7: To be model: Future state for an integrated knowledge based planning system

7. Future tends An integrated knowledge based planning system can deliver planning services more efficiently and the savings instigated would increase productivity, quality and satisfaction. The increased interaction between local communities and planning officers leads to increasing trust, enhancing transparency and empowering the general public to raise their voice for sustainable development. The pressure and work burden is less on local government employees as soon as citizens start to use the electronic channels and apply planning portals to mutually save hours of manual and bureaucratic work. The outcome of this research paper is to recommend an integrated database for each local authority within their specific needs so that knowledge is properly managed and shared effortlessly. The integrated knowledge based databases can advance the transactions between the Council and citizens. These databases should be then integrated into all other ICTs applications i.e. DMS, GIS, CRM etc. so that the eGovernment services are easily available though different levels of local authorities and also through different planning service functions to offer one stop planning support services. From fieldwork evidence the future research framework (‘To Be Model’: Figure 7) is proposed for future trend towards smart and sustainable development with an integrated knowledge based planning system in the UK local government.

References Benbya, H. (2008) Knowledge management, systems implementation: lessons from the Silicon Valley, Oxford: Chandos Publishing.

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Nasrullah Khilji and Stephen Roberts Cooper, R. (1998) Product leadership: creating and launching superior new products. Reading (Mass): Perseus Books. Dioguardi, G. (2009) Network enterprises: the evolution of organizational models from guilds to assembly lines to innovation clusters. New York: Springer (Innovation, Technology, and Knowledg Management series). st Jashapara, A. (2004) Knowledge management: an integrated approach, 1 Edition, Harlow: Prentice Hall/ Financial Times. Kirk, J. (2012) Knowledge hub, local government association, connect‐collaborate‐learn‐innovate, available at https://knowledgehub.local.gov.uk/web/jamie.kirk/blog/‐ /blogs/5901159;jsessionid=4FFABD330ED12C437E1A65FC7F9C01C4 McInerney, C.R. and Day, R.E. (2007) Rethinking knowledge management: from knowledge objects to knowledge processes. Berlin: Springer (Information Science and Knowledge Management series). Morse, J.M. (1994) Emerging from the data: the cognitive process of analysis in qualitative enquiry. In Proceedings of a conference on Critical Issues in Qualitative Research Methods. Thousand Oaks, CA: Sage Neill, B. (2012) Planning and Communities Minister, Department of Communities and Local Government, Planning Portal hits the million app mark, available at http://www.communities.gov.uk/news/corporate/2092719 Nonaka, I. and Takeuchi, H. (1995) The knowledge creating company: how Japanese companies create the dynamics of innovation. New York: Oxford University Press. Ramalingam, B. (2006) Tools for knowledge and learning: a guide for development and humanitarian organizations. Research and Policy in development, Overseas Development Institute, available at http://www.odi.org.uk/resources/docs/188.pdf Van Heghe, H. (2011) Knowledge centric management: urgent recommendations and a practical and pragmatic framework to become a knowledge centric organisation. St Albans: Ecademy Press.

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Characterization of Knowledge Sharing Practices in a Project Based Organization Irene Kitimbo and Kimiz Dalkir School of Information Studies, McGill University, Montreal, Canada Irene.kitimbo@mail.mcgill.ca Kimiz.dalkir@mcgill.ca Abstract: Knowledge sharing within and across projects is important to the performance and competitiveness of project based organizations. However, the temporary nature of projects raises multiple challenges for the transfer of knowledge across project boundaries such that: best practices are not shared, mistakes are repeated, work is duplicated and many projects fail to meet expectations. Using a web‐based, self administered survey to collect data from project team members, this paper describes knowledge sharing in a small telecommunications organization. Keywords: knowledge sharing, cross project learning, project knowledge management, case study, project based learning

1. Introduction Knowledge is widely recognized as a critical resource for organizational performance; however, to gain competitive advantage, it is not enough for organizations to simply possess knowledge. Organizations need to emphasize and more effectively exploit their knowledge based resources (Davenport and Prusak 1998). According to Wellman (2009): “Today, more than ever, an organization’s competitiveness depends on what it knows, how well it uses what it knows, how fast it can adapt what it knows to the rapidly changing environment, and how quickly it can acquire new knowledge...” (p. 5). Organizational knowledge sharing is an activity through which knowledge is exchanged between and among individuals, communities and within and among teams, organizational units, and other organizations. As one knowledge‐centered activity, knowledge sharing is the fundamental means through which employees can contribute to knowledge application, innovation, and ultimately the competitive advantage of the organization (Jackson et al. 2006). Previous studies have shown that knowledge sharing is positively related to: faster completion of projects (Hansen 2002), team performance (Mesmer‐Magnus and DeChurch 2009), organizational innovation capabilities (e.g. Scarbrough 2003; Lin 2007d) and performance including sales growth and revenue from new products and services (Arthur and Huntley 2005). Knowledge sharing has become so important that most researchers now recognize that “the success of knowledge management in the organization depends on effective knowledge sharing practices” (Bhirud, Rodrigues and Desai 2005, p. 4). In spite of the recognized benefits of sharing knowledge within and across projects, in many project based organizations, lessons learned from past projects are not easily shared with others (Brady and Davies 2004). The characteristics of a project as a temporary effort make it difficult to share knowledge between individuals and groups participating in short term establishments, which do not have an inherent mechanism of learning, and that are usually focused on immediate deliverables (Lindner and Wald 2011). Project teams are dispersed when a project ends inhibiting the formation of routines and relationships of trust which facilitate knowledge sharing. Consequently, as members of the project team return to their permanent jobs or turn to new assignments, project knowledge is fragmented and collective learning is interrupted resulting in knowledge loss. When the lessons learned during one project are not available for application in other projects, solutions are re‐invented, mistakes are repeated and results are less than optimal as work is duplicated and resources wasted. The twin challenges of knowledge sharing therefore are to: 1) prevent reinvention and 2) enhance communication among peers (Ruuska and Vartianen 2005). Several contributions towards solving these challenges have been discussed in the extant literature. On the one hand, solutions to prevent re‐invention have proposed the codification of project lessons learned as a means to facilitate dissemination of project knowledge for consultation and re‐use in new projects. Codified knowledge is easily shared through technology mediated tools such as databases and intranets. On the other hand, solutions that enhance peer communications are aligned with personalization approaches. Personalization focuses on knowledge exchanges between individuals and within groups and the context of theses interactions. Here knowledge is regarded as embedded in practice, closely tied to the people who developed it and is shared by personal face‐ to‐face interaction (Bresnen et al. 2003).

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Irene Kitimbo and Kimiz Dalkir This study describes the knowledge sharing activities of project team members at a telecommunications organization located in Uganda. The rest of the paper is organized as follows: Section two discusses factors that influence knowledge sharing, Section three presents the methodology, Section four presents results and discussion and Section five is the conclusion.

2. Factors influencing knowledge sharing in organizations Nonaka (1994) defined knowledge as a justified personal belief that increases an individual’s capacity to take effective action. “Knowledge is a fluid mix of framed experience, values, contextual information, expert insight and grounded intuition that provides an environment and framework for evaluating and incorporating new experiences and information. It originates and is applied in the mind of the knower. In organizations, knowledge often becomes embedded not only in documents or repositories, but also in organizational routines, practices and norms.” (Davenport and Prusak 1998). Previous research on organizational knowledge sharing has explored factors that both promote and present barriers : In a review of knowledge sharing research, Wang and Noe (2007) group these factors in three broad categories: 1) environmental factors (e.g. organizational context‐including social structures like project teams and relationships among team members, interpersonal and team characteristics, cultural characteristics), 2) motivational factors (e.g. beliefs of knowledge ownership, perceived benefits and costs) and 3) individual characteristics (e.g. education, work experience). In each category, the effect on knowledge sharing can be either positive or negative, depending on moderating conditions. According to Barnard (2005), the factors that promote knowledge sharing include: “…, management support, embedding knowledge management in the work process, good tools and sharing mechanisms, personal contacts and making a conscious effort to share knowledge” ( p. 4). The emphasis of most studies in knowledge sharing, however, continues to be on factors that inhibit sharing. Barnard (2005) found that projects that involve specialists from different fields find it specifically difficult to share knowledge because of “the increasing mobility and turnover of personnel” (p. 3). The diversity of the project team, which often consists of members from different backgrounds, with various skills, who work together for the duration of the project and then disperse and reassemble in different teams (Ajmal and Koskinen 2008) interrupts knowledge sharing leading to organizational amnesia‐ by which organizations fail to manage their knowledge and simply forget what they previously learned (Grabher 2004b). Sometimes employees are simply not willing to share knowledge, or feel that sharing reduces their security on‐the‐job, their status or standing of their expertise in a field (Dalkir 2005). If a staff member has little prior experience with knowledge sharing, their lack of understanding of its purpose may also act as a barrier to progress (Laporte 2004). Some hindrances to knowledge sharing stem from personal characteristics e.g. the project manager’s “blindness, simple‐mindedness….paralysis, superstitious learning (style), and diffusion deficiency” (Knauseder 2004, p.7). Fedor (2003) argues that many projects fail and do not share knowledge because the team has not been effectively built or managed. If a project team is poorly supported with resources, it is unlikely that it will find the time to engage in knowledge sharing practices (Fedor 2003). Furthermore, outdated notions of what knowledge is are a primary reason for failure to share knowledge. If knowledge is thought of as a personal treasure or secrets stored in the individual’s mind, individuals holding these beliefs will not be willing to share knowledge (Disterer 2001). Trust, a fundamental mediator of knowledge sharing, also plays a major role in deciding whether knowledge is shared in teams (Castelfranchi 2004). In the absence of trust, formal knowledge‐sharing practices are insufficient to encourage individuals to share knowledge with others within the same work environment, report Andrews and Delahaye (2000). A more challenging area of studies of barriers to knowledge sharing looks at so called stovepipes, or “the inability to communicate across functional divisions and organizational boundaries (Binz‐Scharf 2003, p. 24).” In these settings, knowledge sharing can become impossible especially when it “runs against the interest of many agencies (p. 24).” To achieve effective knowledge sharing within organizations, therefore, there is need to explore solutions that address multiple factors. Barnard (2005) proposes that the, the best way to overcome resistance to knowledge sharing is by “integrating knowledge sharing activities into the normal work processes” (p. 4).

3. Knowledge sharing practices Ruuska and Vartiainen (2005) described five characteristics of knowledge sharing communities in project based organizations: 1) structure, 2) purpose and goals, 3) activities, 4) facilitation and coordination, 5) organizational support, and 6) outcomes. A community of practice is defined as “a group of people who share a craft or profession and deepen their knowledge by interacting on an ongoing basis” (Lave and Wenger 1991).Therefore communities, as formal, semi‐formal and informal social structures help to connect peers

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Irene Kitimbo and Kimiz Dalkir working on various projects with each other and enhance knowledge sharing in and within projects (Ruuska and Vartianen 2005). The purpose of this paper was to describe the knowledge sharing activities of members of a project team in a telecommunications organization. Study participants included project managers, site supervisors, team leaders and finance officers, representing a social structure akin to an informal community of practice. In an attempt to develop a valid and reliable measure for knowledge sharing behavior, Yi (2009) proposed the following dimensions of knowledge sharing: 1) written contributions, 2) personal interactions, 3) organizational communication, and 4) community interactions. This paper adopts Yi’s (2009) dimensions as a framework to describe knowledge sharing activities among project team members in a telecommunications organization. 1. Written Contributions as Knowledge Sharing: This involves employees contributing ideas, information, and expertise by posting documents to organizational database repositories (such as a knowledge transfer system) and by submitting reports to other employees and to the organization. The knowledge shared through written means is largely explicit (Yi, 2009). 2. Personal Interactions as Knowledge Sharing: This involves employees sharing knowledge through informal person‐to‐person interactions among individuals, such as chatting (Yi 2009). Oral storytelling is one example of KS through personal interactions. Stories and personal experiences are faster to transmit and easier to remember than abstract explanations. The knowledge shared through personal interactions is more tacit (Yi 2009). 3. Organizational Communications as Knowledge Sharing: This involves employees sharing knowledge through formal interactions within or across work units (Yi 2009). This form of communication commonly occurs at organizations’ regular and unscheduled meetings or among individual employees. The knowledge shared through organizational communications is more tacit (Yi 2009). 4. Communities Interactions as Knowledge Sharing: This involves employees sharing knowledge within a group of individuals who share common experiences or interest (Yi 2009). CoPs are generally made up of groups of people who develop shared objectives and mutual trust where reciprocity is the norm (Alvesson2004). The knowledge shared through communities is more tacit (Yi 2009).

4. Methodology This study is a pilot for a larger doctoral research inquiry into organizational learning in different types of organizations. The quantitative inquiry was based at a telecommunications organization, here after referred to as TeleCo for purposes of anonymity. The site was selected based on these criteria: 1), the site had to be a project based organization; 2) the site had to grant permission to the researcher to conduct a survey; 3) the site had to be involved in business that generated sufficient data to inform the study and had to be willing to provide continued access to study participants throughout the length of the study; and 4) the sites had to use English as its working language to ensure that participants understood survey questions (Creswell 2009; Neuman 2006). TeleCo is an entrepreneurial partner to telecommunication operators and technology vendors. The organization serves this market with skills, services and products. TeleCo provides complete passive infrastructure solutions for the wireless and wire‐line telecommunication industry. Core activities include the full range of services and materials supply required for telecom roll‐outs as well as post‐installation services like maintenance and energy management. Today the organization has 11 separate subsidiaries across Africa and employs more than 500 people. The questionnaire design drew heavily from previously validated instruments (e.g. Marsick and Watkins 2003; Yi 2009). The survey instrument was tested for validity and reliability by an expert panel and participants ‐two project managers, one survey research expert, one knowledge management professor and two doctoral

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Irene Kitimbo and Kimiz Dalkir students. After testing, some questions were dropped from the survey while others were merged. The final questionnaire consisted of 25 items. Data collection was performed between April and June, 2012 and consisted of a web‐based self administered questionnaire and conversations with the country director. The study population consisted of all TeleCo employees at one subsidiary (n=50), who hold supervisory roles on project teams. These included: project managers, site supervisors, team leaders and finance officers. The researcher obtained a list of all potential participants from the human resources office and sent an email inviting voluntary participation in the web‐based survey. Data was collected over a four week period. Given their busy schedule, participants were given the option to save responses and return to the survey later. After the first two weeks, one reminder was sent to potential participants who had not submitted the completed questionnaire. A total of twenty seven responses were submitted out of about fifty invitations sent for response rate of 44%. Five of these were incomplete and removed. Data analysis was based on twenty two responses. Data were automatically generated in a results table using lime survey software. Survey results were then exported to excel for further analysis to prepare descriptive statistics.

5. Results and analysis This section presents the descriptive analysis of data collected in the study. Data is presented in frequency tables with accompanying explanations. 1. Written contributions as knowledge sharing As shown in figure 1, it is a well established practice to save all project documentation to the company server upon project completion. This documentation includes project reports (68%), minutes of team meetings (41%) and templates & checklists (54%). For the organization studied, project audits were not routinely conducted and when they were done, a quick meeting between CEO, finance officer and responsible project manager was convened to address issues arising. There was no record of these meetings. Written contributions are an important tool for sharing project knowledge as they involve creation of records for future reference.

Figure 1: Written contributions 2. Personal Interactions as knowledge sharing Personal interactions make up the bulk of knowledge sharing practices used. Figure 2 shows that peer displays (68%), team meetings (59%), person to person conversations (50%), emails and phone messaging (54%), stories (45%) and job shadowing (64%) are the preferred means of knowledge sharing. This is an expected outcome since learning is socially embedded in practice.. Again while project audits (14%) were not standard practice, learning days (9%) were even rarer. For the two respondents who indicated learning days, a follow up interview would help establish the context in which they participated. Also, worth noting is that fact that the organization studied is a subsidiary of a larger regional corporation and has access to the group network. However, since only the CEO is privy to strategic meeting with other regional leaders, the rest of the respondents did not show any awareness of participation in inter‐organizational networks (4.5%).

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Figure 2: Personal interactions 3. Organizational communication This form of communication commonly occurs at organizations’ regular and unscheduled meetings or among individual employees. Since TeleCo receives as service contracts (54%), project personnel review them to make sure all project requirements are met, in addition to required standards (50%) and operational procedures (36%). For senior staff, adhering to policy and scheduling of staff are major elements in getting the job done and to a less extent, keeping track of success stories (9%) to attract new clients.

Figure 3: Organizational communication 4. Community Interactions The project team functions as an informal community, brought together to accomplish the objectives of a given project. The community of practice is in its initial stages of development (Wenger 1998) and given the project timelines, does not have time to mature before members move on to the next assignment. Interactions are informal; members engage in joint activities, create artifacts, form working relationships and interact both formally and informally. However, when a project ends, there is neither time nor resources to follow matters from completed projects.

6. Discussion and conclusions The purpose of this study was to describe the knowledge sharing practices of project team members of a Ugandan based telecommunications organization. Likening project team members’ activities to interactions in an informal community, we adopted Yi’s (2009) dimensions of knowledge sharing behavior to characterize the knowledge sharing practices of this organization. Knowledge sharing at TeleCo involved: written contributions, personal interactions, and organizational communication and community interactions. Written contributions are a widely used to share knowledge at TeleCo They are standardized, highly integrated into organizational practice and enable wide distribution of project artifacts such as reports, minutes and templates. Knowledge shared is explicit. Explicit knowledge on the other hand is widely known and easily shared. This type of

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Irene Kitimbo and Kimiz Dalkir knowledge is formalized and codified, and is sometimes referred to as know‐what (Brown and Duguid 1998). It is therefore fairly easy to identify, store, and retrieve (Wellman 2009). Explicit knowledge is found in: databases, memos, notes, documents, etc. (Botha et al. 2008) Personal interaction show the widest variety in mechanisms used to share knowledge. Ranging from informal conversations to formal team meetings and project audits, personal interactions engage team members in discussions, conversations, questioning, reflecting and dialogue that seeks for explanations of why things are done a certain way and exploration of alternatives for future experiences. Personal interactions involve active participation and are a rich source of tacit knowledge. Tacit knowledge, sometimes referred to as know‐how (Brown and Duguid 1998) refers to intuitive, hard to define knowledge that is largely experience based. Because of this, tacit knowledge is often context dependent and personal in nature. It is hard to communicate and deeply rooted in action, commitment, and involvement (Nonaka 1994). Tacit knowledge is found in: the minds of human stakeholders. It includes cultural beliefs, values, attitudes, mental models, etc. as well as skills, capabilities and expertise (Botha et al. 2008). Organizational communication consists of several written documents that are mostly used to provide direction and guidelines. This type of knowledge helps to keep order, maintain routines and supports a passive form of learning by using or learning by doing. Organizational communication involves exchange of explicit knowledge. Our findings show that personal interactions and community interactions are aligned to face‐to‐face communication and information exchanged that involves tacit knowledge. Project team members engage in activities such as: brainstorming, job shadowing, and sharing stories and anecdotes to share knowledge that is rather difficult to articulate. In addition, written contributions and organizational communication on the most part were most used as means of sharing explicit knowledge. Knowledge involving procedures and guidelines or communication with clients and customers was recorded in documents, stored in the project database and mostly shared electronically via email. The findings confirm that codification and personalization strategies are complimentary and should be used together to address the challenges to organizational knowledge sharing and create opportunities for organizational learning by sharing and re‐using past project experiences. The results of this pilot were used to refine the interview protocol for the bigger qualitative study and build the conceptual model. One of the limitations of the study design is the small sample size. However, this study was intended as a pilot for a larger doctoral study on organizational learning in project based organizations. Furthermore, the intention of this study was not to generalize but rather identify characteristics and describe the knowledge sharing processes in a given context – the organization under study. Future research will replicate with study using different data collection methods‐qualitative interviews and a bigger number of participants. The use of qualitative interview data will allow deeper and more contextual analysis of knowledge sharing in project based organizations.

References Ajmal, M. and Koskinen, U. (2008) “Knowledge transfer in project‐based organizations: an organizational culture perspective”, Project Management Journal, Vol 31, No. 1, pp 7‐15. Alvesson, M. (2004). Knowledge work and knowledge‐intensive firms, Oxford University Press, Oxford. Andrews, K. M. and Delahaye, B. L. (2000) “Influences on knowledge processes in organizational learning: the psychological filter”, Journal of Management Studies, Vol 37, No.6, pp 2322‐2380. Arthur, J. B., and Huntley, C. L. (2005). “Ramping up the organizational learning curve: assessing the impact of deliberate learning on organizational performance under gainsharing”, Academy of Management Journal, Vol 48, No. 6, pp 1159−1170. Barnard, Y. F. (2005). “Developing industrial knowledge management: Knowledge sharing over boundaries”. As cited In Jones, C. R. (2007). Exploring the practices of knowledge sharing between projects: an investigation of the dimensions of what, when and how knowledge is shared. Unpublished doctoral dissertation, Capella University. Bhirud, S., Rodrigues, L., and Desai, P. (2005). “Knowledge sharing practices in knowledge management: A case study in Indian software subsidiary”, Journal of Knowledge Management Practices, Vol 6, pp1‐13. Binz‐Scharf, M. C. (2003). Exploration and Exploitation: toward a theory of knowledge sharing in digital government projects. Unpublished doctoral dissertation, University of St. Gallen Botha A, Kourie D, and Snyman R, (2008), Coping with Continuous Change in the Business Environment, Knowledge Management and Knowledge Management Technology, Chandice Publishing Ltd, Oxford. Brady, T and Davies, A. (2004). “Building project capabilities: from exploratory to exploitative learning”, Organization Studies, Vol 25, No. 9, pp1601‐1621

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Irene Kitimbo and Kimiz Dalkir Bresnen, M., Edelman, L., Newell, S., Scarbrough, H. Swan, J. (2003). “Social practices and the management of knowledge in project environments”, International Journal of Project Management, Vol 21, pp 157‐166. Brown, J.S. and Duguid, P. (1998). “Organizing Knowledge”. California Management Review, Vol 40, No.3, pp 90‐111. Castelfranchi, C. (2004). “Trust mediation in knowledge management and sharing”. In: Jensen, C., Poslad, S., & Dimitrakos, T. (Eds.), Trust Management, Second International Conference: Vol. 2995. Proceeding Series: Lecture Notes in Computer Science (pp. 1‐15). Oxford, UK Creswell, J. W. (2009). Research design: Qualitative, quantitative, and mixed methods, Sage, Los Angeles. Dalkir, K. (2005). Knowledge Management in Theory and Practice. Amsterdam: Elsevier. Davenport , T and Prusak, L. (1998). Working knowledge: how organizations manage what they know. Harvard Business School Press, Boston, MA.. Disterer, G. (2001) “Individual and Social Barriers to Knowledge Transfer”, Proceedings of the 34th Annual Hawaii International Conference on System Sciences, IEEE Press, Los Alamitos, CA. Fedor, D. B. (2003). “The effects of knowledge management on team member's rating of project success and impact”, Decision Science, Summer, pp 1‐19. Grabher, G. (2004b). “Temporary architectures of learning: Knowledge governance in firms”, Organizational Behavior and Human Decision Processes, Vol 82, pp 150‐169. Hansen, M.T. (2002). “Knowledge network: explaining effective knowledge sharing in multiunit companies”’. Organization Science, Vol 13, No. 3, pp 232‐248. Jackson, S. E., Chuang, C. ‐H., Harden, E. E., Jiang, Y., and Joseph, J. M. (2006). Toward developing human resource management systems for knowledge‐intensive teamwork. In: J. M. Joseph (Ed.), Research in personnel and human resources management, Vol. 25. (pp. 27−70). Amsterdam: JAI. Knauseder, I. (2004). “The client's project manager: a key role for knowledge management in construction projects” Proceedings of the I‐Know '04, June 30, 1‐ 9. Laporte, B. (2004). “Knowledge sharing at the World Bank: the fad that would not go away” Knowledge Management Magazine, December, 1‐6. Lin, H. F. ( 2007d). “Knowledge sharing and firm innovation capability: an empirical study. International Journal of Manpower”, Vol 28, No.3/4, pp 315‐332. Lindner, F and Wald, A. (2011) “Success factors of knowledge management in temporary organizations”, International Journal of Project Management, Vol 29, No. 7, pp 877‐888. Marsick, Victoria J. and Karen E. Watkins.( 2003). “Demonstrating the value of an organization’s learning culture: The Dimensions of Learning Organizations Questionnaire”, Advances in Developing Human Resources, Vol 5, pp132–151. Mesmer‐Magnus, J.R. and DeChurch, L.A. (2009). “Information sharing and team performance: a meta‐analysis”, Journal of Applied Psychology, Vol 94 pp. 535‐546. th Neuman, W. Lawrence. (2006). Social Research Methods: Qualitative and Quantitative Approaches. 6 ed. Pearson, Boston, MA: Pearson.. Nonaka, I. (1994). “A dynamic theory of organizational knowledge creation”, Organization Science, Vol 5, No. 1, pp 14‐37. Ruuska, I and Vartiainen, M (2005). “Characteristics of knowledge sharing communities in project organizations”, International Journal of project Management, Vol 23, 374‐379. Scarbrough, H. (2003). “Knowledge management, HRM and the innovation process”. International Journal of Manpower. Vol 24, No.5, 501‐516. Wang, S. and Noe, Raymond, A. (2010). “Knowledge sharing: a review and directions for future research”, Human Resources Management Review, Vol 20, No.2, pp115‐131. Wellman, J. L. (2009). Organizational learning: How companies and institutions manage and apply knowledge, Palgrave Macmillan, New York, NY. Wenger, E. (1998). “Communities of practice learning as a social system”. Systems Thinker. June 98 Yi, J. (2009) “A measure of knowledge sharing behavior: scale development and validation”, Knowledge Management Research & Practice, Vol 7, pp 65‐81.

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Person‐Organisation fit as an Organisational Learning Tool in Employee Selection Jana Makraiova, Paul Woolliscroft, Dagmar Caganova and Milos Cambal Slovak University of Technology, Faculty of Materials Science and Technology, Trnava, Slovakia jana.makraiova@gmail.com paul.woolliscroft@stuba.sk dagmar.caganova@stuba.sk milos.cambal@stuba.sk Abstract: The current trends in the international business environment have been giving rise to increased cultural interconnections and interactions, reflected in the rapid global movement; both: geographical and interpersonal. It is indisputable that companies are seeking the most effective workforce to integrate within the organisational structure and to achieve the stated objectives and further expansion. Global companies nowadays are shifting from the perspective where cultural encounters are perceived inevitable and rather undesirable to such where culture is seen as an attribute to be exploited and benefited from. Thus, a clear understanding of cultural issues has become an essential prerequisite and imperative to ensure competitive advantage and success. By the same token employee satisfaction lies at the centre of a companies’ management attention with equal importance. “Matching the right people with the right organisation” can now be applied as a suitable phrase to accurately describe the early stages of a recruitment process. This is particularly true when taking the subtle nuances of culture with its manifestations into account. It is worth noting that cultures across the world have a significant effect on perceiving various elements that occur in working life and influence the individual job performance. The match between this culturally developed individual value orientation and the nature of corporate culture in a particular organisation is referred to and amongst specialists well‐known as person‐organisation fit. This paper presents and describes the theoretical background of person‐organisation fit and its various conceptualisations as portrayed in the literature. It goes even further by introducing the Schwartz value model as one of the most comprehensive for assessing person‐organisation fit. The paper initially identifies the differences between various levels of working environment related fits, starting with person‐job fit, later person‐group fit, ending with person‐vocation fit. Since each of them has a different meaning and reflects different attributes, they require also different approaches for exploring and assessing. The main focus is subsequently placed on person‐organisation fit. The main contribution of this theoretical study steams not only from offering a different view on the matter of recruitment process itself but also from underlining the limitations of every attempt to develop the usable tool for assessing the person‐organisation fit within an organisation. The main limitation arises from the ambiguity and lack of clarity in distinguishing between the culturally influenced attributes resulting in recognized, acknowledged and shared values and core beliefs on one side and what is referred to as personality attributes. The latter, unlike the former is, not only learned through the process of our upbringing but largely inherited. Therefore it is a task and at the same time a challenge for those striving for making the employee selection process as complex as possible to choose or develop the best value orientation assessing tools. Defined as the tools, which help to improve the organisational learning and ensure best possible outcome from the employee selection process. Keywords: knowledge management, organisational learning, recruitment, person‐organisation fit, Schwartz value model

1. Introduction The idea of organisational learning has been present in management literature for decades and today represents a key component of knowledge management (Holden, 2002). Despite wide spread acceptance the concept only became widely recognised in the 1990’s with the proposal of the “fifth discipline” (Senge, 1990) which focused on converting companies into learning organisations. Since then, two major developments have been highly significant in the growth of the field. The first one is the conceptual fragmentation of the field caused by researchers and scholars from disparate disciplines who tend to compete for whose model of organisational learning is the best one. The second development is that many consultants and companies have identified the commercial significance of organisational learning and much of the effort of these theorists has been devoted to identifying templates, which real organisations could attempt to emulate (Easterby‐Smith and Araujo 1999). A helpful way of making sense of organisational learning is to decide whether researchers fall into one of two basic camps. The dividing line between them is the extent to which the process of organisational learning is emphasised as a technical or a social process. Here we can again turn to Easterby‐Smith and Araujo (1999) who explain, that the technical view assumes that organisational learning is concerned with the effective

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Jana Makraiova et al. processing, interpretation of, and response to, information both inside and outside the organisation. The social perspective on organisation learning focuses on the way people make sense of their experiences at work. From this perspective, learning is something that can only appear from social interactions, normally in the natural work setting. The overall objective of this research study is to assess the application of the Schwartz’s Value Model (Schwartz, 1992) as an effective tool for organisational learning and knowledge creation. The model’s application will be tested in the context of recruitment in order to establish a clear organisational learning strategy which enables employees to be selected based on their fit to the organisational approach.

2. Cultural learning in organisations Firstly, it is necessary to understand the role and importance of cultural learning within organisations. The societal theory of organisational learning implies that the learning process comprises interpersonal encounters and own experience transformed into personal development (Argyris, 1996). When multinationalism and interculturalism in almost every organisation is an inevitable consequence of the processes of globalisation, it seems the culture with its impacts and influences on every individual in organisation must be strongly taken into consideration when it comes to the questions and issues of learning. Organisations in this perspective provide the common basis for encountering the people of many various cultural backgrounds and organisational learning must build upon the complex of different cultural values and norms. These attributes are deeply ingrained within human nature, and are only visible as a result of a thorough interpersonal comparison. Nevertheless their impact upon human perception, behaviour and decision making in everyday life is significant. It is indisputable that learning from other cultures brings numerous advantages both on the personal and professional level (Hofstede et al, 2010; Trompenaars and Hampden‐Turner, 2000). As every culture possess characteristics less or more different from other ones, blending of them could contribute to improvements and ideas in the organisation that would not be possible with the group of individuals of the same cultural background. Culture in this sense is perceived as a competitive advantage for every organisation, which is successful in adopting the tools for processes for better selection and integration of a culturally diverse workforce. These statements are clearly evident from the literature and various studies all over the world and can be support in our exploratory study in the context of recruitment. If a closer look is taken at the benefits of cultures for individuals on their personal level, it is the matter of fact that learning from them will help to develop a deeper understanding of worldview diversity and broaden one´s personality and perspective on different value systems in general. It helps to open up the notion that there is more than just one worldview and that neither of them is only right or wrong. But the learning concept does not remain on the personal level only; the process of organisational learning is coming full circle when individuals in organisation contribute to the common knowledge improvement. Organisational learning is driven by all individuals who make up the part of it and who broaden, refine and share their own knowledge. This gives an answer to one of the greatest myths of organisational learning, and that is “who question”. Prange (1999) contemplates if it is individuals or organisations that learn. Organisations that are committed to true learning practices will create a suitable and encouraging environment and provide the technical tools for enabling the knowledge to be collected and spread. Individuals are the main bearers of knowledge and after all, these two components of organisational learning: individuals and organisation as a whole reinforce one another.

3. Individual´s fits levels related to working environment When seeking to understand and predict the attitudes and action of employees with regard to their working environment, numerous researchers have adopted systematic knowledge management models of organisational learning in order to measuring the congruence between individuals and the specific level of working environment and their utilisation for practice (Clereq, Fontaine and Anseel 2008; Kristof‐Brown, Zimmerman and Johnson 2005; Kristof 1996). Within individuals´ fits there are several approaches applied in distinguishing between various levels. Kristof‐ Brown, Zimmerman and Johnson (2005) suggested and later investigated the relationships between 4 levels of fits: person‐job, person‐organisation, person‐group and person‐supervisor fit. Some authors (Kristof 1996; Judge and Ferris 1992) distinguish alike categories and it can be concluded that their combination could

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Jana Makraiova et al. represent an optimal categorisation to embrace all aspects of the working environment. Figure 1 depicts four types of individuals´ fits according to the aforementioned authors. Person-Vocation fit Person-Organisation fit Person-Group fit Person-Job fit

Person-Environment fit Figure 1: Levels of individuals´ fits (Author elaboration based on Kristof 1996; Judge and Ferris 1992) From all working environment fits the most important are person‐job (P‐J) and person‐organisation (P‐O) fits. Whereas the P‐J fit primarily focuses on assessing the suitability of an individual for the tasks that are required for successful performance of a specific job, P‐O fit focuses on looking for congruence between individuals and organisational value orientation that mainly arises from cultural background. The other two levels of fit are considered as complementary to the whole, since person‐group fit assesses the compatibility between the individual and other co‐workers from the complementary point of view, including all aspects attributed to effective team cooperation. And finally the broadest level is not bounded directly to a specific organisation as it is related to the general occupation or profession, comparing all individual aspects that are necessary for satisfactory profession fulfilment. P‐O fit is much less commonly used in organisations to be systematically measured during the hiring process with the utilisation of designed tools. Handler (2004) defined P‐O fit as “the congruence of and individual’s beliefs and values with the culture, norms, and values of an organisation”. One limitation is the fact that the elements of this fit are much more of a “soft” character. Thus, it is difficult to examine the job‐related outcomes of a match between person and organisation as it relates to abstract concepts such as “values” and “culture”. On the other side, the softer nature and less objective constructs than P‐J fit, does not mean it is of less importance.

3.1 The principle and value of P‐O fit The idea of measuring P‐O fit must be transformed into the elements required for using it as a systematic part of the hiring process within the organisations. Implementing P‐O fit into the hiring process attempts to understand the selection process beyond the standard evaluation of professional knowledge, skills and abilities and it offers the potential for a more flexible and comprehensive approach to employee selection (Westerman and Cyr 2004; Bowen, Ledford and Nathan 1991; Cable and Judge 1996; Kristof 1996). The comparison process of measuring P‐O fit yields the valuable outcome in form of a data‐based estimate of compatibility between an individual and organisation. This information can be very useful for helping organisations to make all kinds of important decisions (Handler, 2004). While the softer nature of P‐O fit dimensions makes the outcome less tangible and influence of good P‐O fit for objective aspects of job performance less visible, various studies has demonstrated many ways in which dealing with P‐O fit can have significant value for an organisation. Probably the most notable outcome of good P‐O fit is the greater congruence between value orientation of individuals and organisation, the more likely they will be to remain with that organisation.

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3.2 Measuring P‐O fit One of the possible approaches for testing and measuring the P‐O fit is based on an integrative model of P‐O fit, proposed by Kristof´s understanding of P‐O fit´s multidimensionality (see Figure 2). Supplementary fit

Value congruence • Job satisfaction

Personality congruence

Needs-supplies fit

Intention to remain in the organisation

• Organisational commitment

Work environmnent congruence

Figure 2: An integrative model of P‐O fit (Source: Westerman and Cyr 2004) This model, described in the study by Westerman and Cyr (2004) comprises supplementary fit measured by values congruence and personality congruence and needs‐supplies fit measured by work environment congruence. All types of congruence examined in this model result in having an effect on employee satisfaction and commitment to organisation with the subsequent intention to remain with the organisation. The portrayed model presupposes the direct and positive correlation between value, personality and work environment congruence on one hand and employee satisfaction and commitment on the other one. Westerman and Cyr (2004) explains that supplementary fit occurs when a person possesses the traits which are similar to the object of comparison and therefore supplementary fit is focused on measuring the similarity between fundamental characteristics of people and organisations. The most common operationalisation of this perspective is the similarity between individual and organisational values. Individuals’ behaviour and decision making is strongly influenced by values. Organisations pose a set of values derived from a combination of value preferences of leaders and founders that are used to develop a corporate culture and maintain guidelines for acceptable employee behaviour. The correspondence between the individual´s values and the organisation´s existing value systems represents one aspect of P‐O fit. The second aspect of supplementary P‐O fit (according to Westerman and Cyr) is congruence between individual´s personal traits and ideal or modal personality type in his/her environment. Lastly P‐O fit accordingly has been operationalised as a state of congruence between individual needs and environmental determinants that may help or hinder the fulfilment of these individual´s needs. Therefore it is often referred to as need‐supplies fit. It is also useful to introduce one more approach in developing P‐O fit theories. This theory is widely presented in the work by Kristof (1996) who distinguishes two ways of understanding complementary P‐O fit, as portrayed on Figure 3. Kristof named them needs‐supplies and demands‐abilities P‐O fit.

Person

Supplementary P-O fit

Organisation

Complementary P-O fits

Supplies • Experience, effort & commitment • Time • Task-related & interpersonal knowledge, skills, abilities

Demands-abilities P-O fit

Needs-supplies P-O fit

Demands • Financial, physical psychological resources • Task-related interpersonal

Demands • Experience, effort & commitment • Time • Task-related & interpersonal knowledge, skills, abilities

& &

opportunities

Supplies • Financial, physical psychological resources • Task-related interpersonal opportunities

Figure 3: Various conceptualisation of P‐O fit (Author elaboration based on Kristof 1996)

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Jana Makraiova et al. To draw conclusions regarding the theories and various conceptualisations of P‐O fits it valuable to highlight the definition based on relationships described above and presented in previous diagrams, which will be the default one for further research conception progress: P‐O fit means mutual compatibility or congruence between value orientation of individual and organisation that occurs when both sides share similar fundamental value oriented characteristics or the individual´s value orientation fits into the organisation to make a whole by increasing the cultural diversity. Whereas the similarity of values will be preferable for those organisations, which look at cultural diversity rather than disadvantage, making the whole from different parts might be a preferable view for cultural diversity driven organisations. Although this definition might seems too complicated, fundamentally it does not say more than when examining P‐O fit, the attention will be placed on how cultural value orientation of individual meets the cultural climate in the organisation.

3.3 Limitations of using P‐O fit As stated previously, the largest limitation of using P‐O fit lies in the fact that is deals with softer aspects of human nature. Therefore it is necessary to choose the correct model for measuring fit within the organisation. There are several widely applicable models that can be utilised for this purpose. Handler (2004) says when reflecting upon potential problems with using P‐O fit, while there are many great benefits of measuring congruence as a part of applicant selection process, there are also several issues that must be fully considered when thinking of using this tool. Besides accentuation of the less objective nature of P‐O fit dimensions (especially when compared to P‐J fit dimensions), Handler further mentions other potential problems. One of them is the necessity to document clear linkages between the P‐O fit measures and job performance requirements as a part of the hiring process. It is also necessary to study the impact of using P‐O fit in employee selection on tenure as this is typically one of the easiest ways to investigate the return of investment of a selection tool. This alone would make adopting P‐O fit measures an easy decision. But there are many other aspects that P‐O fit has been shown to impact, like satisfaction, commitment and intention to remain, just to name a few, and which are harder to measure. It is important for organisations, choosing to implement this selection tool, that they try and collect data regarding the impact on objective criteria other than tenure itself. Another issue with P‐O fit measures lies in the fact that the core of it is entirely dependent upon the culture of the organisation, with which an individual is compared. The problem could arise from a larger amount of groups within an organisation if they did not share the same value orientation. For this reason, it is of high importance to ensure that the cultural standard to which and individual is compared is reflective of his or her real future workplace. Failure to do this can result in a mismatch that could negate the value of a P‐O fit tool.

3.4 Shortcomings of previous P‐O fit studies Clercq, Fontaine and Anseel (2008) elaborated on an extensive report based on their study aimed at identifying the most comprehensive value model for assessing P‐O fit. They suggested that there are several shortcomings within the previous studies that prevent an optimal integration and understanding of research findings. The limitations mainly were found in the manner in which the P‐O value compatibility has been assessed. Firstly a number of studies have used measures that include a mix of values, then knowledge, skills and abilities (KSA) and personality dimensions. This conceptualisation makes it difficult to find out which constructs are responsible for any congruence effects. Second the majority of studies on P‐O fit examined overall fit across a wide variety of values, taking a cataloguing approach rather than starting from a well‐developed theoretical structure. Lastly when building up the concept for P‐O fit study, it becomes clear that there does not exist one dominant value framework. To make things even more complicated, numerous studies have used for this purpose scales made up of a mix of different value instruments or organisation‐specific value items. Clercq, Fontaine and Anseel have revealed that it is difficult to integrate research findings from various studies when there is no common framework to map specific value dimensions and more importantly no associated interrelations with job‐related outcomes. Furthermore they saw other questions yet not addressed. These questions arose from the attempts to find out which of the theoretical value models represents the most comprehensive concept and whether the dimensions identified in each model refer to similar underlying constructs and if not, how are they interrelated.

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4. Schwartz value model as a tool for assessing To address the above mentioned questions Clercq, Fontaine and Anseel proposed to use the well‐established Schwartz value model (see Figure 4) as an integrative framework that, as they supposed, might increase the understanding of the interplay between different values in determining P‐O fit and its outcomes. Schwartz and Bilsky (1987; p. 551) defines values as “concepts or beliefs, about desirable end states or behaviours, that transcend specific situations, guide selection or evaluation of behaviour and events, and are ordered by relative importance”.

Self-direction

Universalism

Stimulation Benevolence Hedonism Conformity Achievement Tradition

Power

Security

Figure 2: Schwartz theoretical model of values (Author elaboration based on Sagiv and Schwartz, 2000) According to Schwartz, values stem from a need of individuals to cope with three universal requirements of surrounding reality, specifically needs of individuals as biological organisms, requisites of social interaction and requirements for the smooth functioning and survival of groups. This specification led to a universal classification of values that categorises them into 10 value types. The values are organised into a circular structure along two orthogonal axes, as shown on the Figure 4. Within this circular structure, adjacent value types are closely related, and motivational differences between value types are continuous rather than discrete. It results in polarity of value types in opposition in the structure. Furthermore these 10 value types can be grouped along four sectors. Therefore along the first axis, the opposed dimensions are labelled as “openness to change” and “conservation”. The first one includes stimulation and self‐direction and is significant when people follow their own intellectual and emotional interests in an unpredictable and uncertain direction. The second one includes tradition, conformity, and security and refers to the preservation of status quo and certainty. It implies that this axis opposes values emphasising one´s own independency and action against submissive self‐restriction, the need for protection and stability, and maintaining traditions. Along the second axis there is “self‐enhancement” opposite “self‐ transcendence”. The first one includes power and achievement and represents the motivation to enhance people´s own personal interests, even at the expense of others. On the other side self‐transcendence includes universalism and benevolence and refers to the motivation of people to express courtesy, transcend selfish concerns, and promote the welfare of close or distant others as well as of nature in general. It implies that this axis put into the opposition values emphasising the success and dominance over others and those that emphasize acceptance of others, promotion of equality and common good. Hedonism as last, yet unmentioned value type, it treated as the one that crosses the boundaries of two sectors, therefore it is related to both, openness to change and self‐enhancement (Schwartz, 1992).

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Jana Makraiova et al. To draw a conclusion regarding the Schwartz Value Model, it can be stated that this model meets the requirements for thoroughness, comprehensiveness, and a cross‐culturally validated theoretical value structure. This was proven in an extensive study by Clercq, Fontaine and Anseel (2008), in which they tried to find the answers to several research questions. Three of them regarding the comprehensiveness of the model were found to be satisfying enough to say that: Schwartz value model that was subjected to examination has shown to possess all attributes for being declared as comprehensive and at the same time simple enough in order to serve as a P‐O fit assessment tool. The evidence for this statement emanates from the study including these partial results: categorisation into Schwartz value types provides sufficient framework for placing 92.5% of widely recognised value items from 42 value instruments or typologies with moderate to good agreement. The remaining 7.5% were considered as not classifiable into Schwartz typology.

5. Exploratory research methodology In order to establish the role of organisational learning in the context of recruitment an exploratory study was conducted to establish a clearer understanding of cultural issues and insights of respondents within the workplace. The respondents were classified into two main groups; one consisting of managers who are in charge of multicultural teams and the second comprising employees who work as part of multicultural teams. The industrial sector, in which the organisation operates, the size of a company or years of experience in the field for each respondent were not considered at this stage. The objective was to ask respondents a series of semi‐structured interviews questions regarding:

their view on diversity as an advantage or disadvantage for organisational learning,

their preference for working within multicultural or monocultural team,

their view on the importance that should be placed upon supporting cultural diversity,

their own experience with poor cultural match in a work team,

how to eliminate unwanted consequences of hiring people who do not match with organisation from the value orientation perspective,

what preventive measures could assure selecting the right people for organisation.

After collecting and evaluating the responses it was concluded that regardless of the group each respondent fell into, the attitudes towards inquired issues were significantly similar. They have stated that learning from other cultures within working settings is of great importance. It can bring everyone to new views on surrounded reality; it is a valuable source of innovation potential driven by different mindsets and concepts of thinking. Furthermore the personal enrichment from cultural encounters is not negligible as well. Respondents expressed that they welcome the opportunity to gain knowledge about other cultures, particularly the knowledge about different customs and working styles. But to provide the complete picture, it must be said that respondents from the managerial group noted that cultural diversity can be viewed as a disadvantage because of the increased need for understanding the cultural behavioural patterns and adjusting the leadership styles to each and every individual. The more diverse the team is, the more effort and initiative it takes on the side of manager to harmonise and compromise people´s requirements. The potential of mutual learning from other cultures prevails over the potential risks it can bring. All respondents see cultural diversity primarily as an advantage, but they emphasised the need for developing and using the tools that could be used to predict the value orientation of individuals, before they are hired for an organisation. The tools that would be able to assess if the individual will not only meet the professional requirements of a work position, known as KSA (knowledge, skills, abilities), but will also focus on the match between the organisation (represented in all members) and newcomer to this organisation from the cultural perspective. In the literature, this tool is referred to as a person‐organisation fit (P‐O fit), and its incorporating into the selection process of employees enables “hiring for organisation”, not only “for the job”. To draw conclusions from the preliminary research study, the author can summarise that cultural learning is “the prerequisite of a successful company that can be supported by maintaining a wide representation of appropriately selected employees”.

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6. Conclusions and future research All 42 value assessment instruments that have been encompassed into the Schwartz study can be further applied to the research design for potential practical operationalisation of any type of P‐O fit measurement tool. It is also interesting and challenging at the same time to construct an unambiguous and unbiased set of assessment items as a P‐O fit tool that would target each of the value types from the original Schwartz model. The aim of the future research is to now provide and test the methodological framework for incorporating the tool for assessing P‐O fit into the structure of hiring processes to ensure and enlarge the potential for employee satisfaction and retention, as well as to increase the potential of cultural learning in organisations. In order to fully explore the significance of P‐O fit, the next stage is to conduct an extensive survey of employees working within Slovak subsidiaries of multinational companies. The questionnaire will be divided into three key parts, firstly to gather classification data regarding the extent of diversity in the organisation, secondly, to identify congruence between the personal and organisational value orientation and thirdly, to explore the validity of the Schwartz’ Value Model as a tool for the recruitment process. Following completion of the survey and the collected data analysed and evaluated, the proposed solutions will be offered for evaluation to practitioners. In conclusion, it can be summarise that learning is at the heart of an organisation´s ability to adapt to rapidly changing environment; learning and development in organisations go hand in hand and cultural diversity driven people to contribute to development on various levels. Furthermore, organisations that are experienced in cultural learning are mainly skilled at creating, acquiring, and transferring knowledge, and at modifying its behaviour to reflect new insights.

References Argyris, C., and Schön, D. (1978) Organisational learning: A theory of action perspective, Reading, Mass: Addison Wesley. Bowen, D. E., Ledford, G. E. and Nathan, B. R. (1991) “Hiring for the Organisation, not the Job”, Academy of Management Executive, vol. 5, no. 4, pp. 35‐51. Cable, D. M. and Judge, T. A. (1996) “Person‐Organisation Fit, Job Choice Decisions, and Organisational Entry”, Organisational Behaviour and Human Decision Processes, 67(3), pp. 294‐311. Clercq, S., Fontaine, J. and Anseel, F. (2008) “In Search of a Comprehensive Value Model for Assessing Supplementary Person‐Organisation Fit”, Journal of Psychology, 06/2008; 142(3), pp. 277‐302. Easterby‐Smith, M. and, Araujo, L. (1999) “Organisational Learning: Current Debates and Opportunities” in M. Easterby‐ Smith, L. Araujo and J. Burgoyne (eds.) Organisational Learning and the Learning Organisation, Sage, London. Handler, C. (2004) “The Value of Person‐Organisation Fit”, [online], www.ere.net/2004/05/20/the‐value‐of‐person‐ organisation‐fit/. Holden, N. (2002) Cross‐cultural management: A knowledge management perspective, Pearson Education, London. Hofstede, G. Hofstede, G. J. and Minkov, M. (2010) Cultural Consequences and Organizations: Software of the Mind, McGraw‐Hill, London. Judge, T. A. and Ferris, G. R. (1992), “The Elusive Criterion of Fit in Human Resources Staffing Decisions”, Human Resource Planning, vol. 5, 1992, pp. 47‐67. Kristof, A. L. (1996) “Person‐Organisation Fit: An Integrative Review of its Conceptualisations, Measurement, and Implications”, Personnel Psychology, vol. 49, issue 1, pp. 1‐49. Kristof‐Brown, A. L., Zimmerman, R. D. and Johnson, E. C. (2005) “Consequences of Individuals´ Fit at Work: A Meta‐ analysis of Person‐job, Person‐organisation, Person‐group, and Person‐supervisor Fit”, Personnel Psychology, vol. 58, pp. 281‐342. Prange, C. (1999) “Organisational learning – desperately seeking theory?” in M. Easterby‐Smith, L. Araujo and J. Burgoyne (eds.) Organisational Learning and the Learning Organisation, Sage, London. Sagiv, L. and Schwartz, S. H. (2000) “Value Priorities and Subjective Well‐being: Direct Relations and Congruity Effects”, European Journal of Social Psychology, vol. 30, pp. 177‐198. Schwartz, S. H. (1992) “Universals in the Content and Structure of Values: Theoretical Advances and Empirical Tests in 20 Countries”, Advances in experimental social psychology, Academic Press, Orlando, vol. 25, pp. 1‐65. Schwartz, S. H. and Bilsky, W. (1987) “Toward a Universal Psychological Structure of Human Values”, Journal of Personality and Social Psychology, vol. 53, pp. 550‐562. Senge, Peter M. (1990), The Fifth Discipline, Doubleday/Currency, New York, USA. Trompenaars, F. and Hampden‐Turner, C. (2000) Riding the waves of culture, Nicholas Brealey Publishing, London. Westerman, J. W. and Cyr, L. A. (2004) “An Integrative Analysis of Person‐Organisation Fit Theories”, International Journal of Selection and Assessment, vol. 12, no. 3, September 2004, pp. 252‐261.

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Models for Describing Knowledge Sharing Practices in the Healthcare Industry Negar Monazam Tabrizi Manchester University, Manchester, UK Negar.MonazamTabrizi@manchester.ac.uk Abstract: Recently, healthcare organisations realised that if they want to gain or sustain advantages, medical knowledge needs to be not only managed but also shared among professionals and patients. Inadequate knowledge sharing in healthcare organisations can lead to medical errors. As a result, knowledge sharing in healthcare industry may no longer be a “nice to have” process but changes into a “must have” one. Acknowledgement of the importance of knowledge sharing in healthcare organisations has resulted in some valuable contributions trying to understand this phenomenon. Most of these contributions are about the nature of knowing, knowledge sharing means, and governance mechanisms. Despite the richness and depth in these three streams of research, at present there is no study integrating these various insights. Hence, there remains uncertainty about the intrinsic relationship among these three kinds of concepts. Therefore, it is worthwhile to examine firstly, the relationship among these concepts and secondly, their impact on knowledge sharing performance. This study provides a comprehensive view of knowledge sharing practices from the three mentioned perspectives. Drawing upon the descriptive process of theory building, a model for these three aspects of knowledge sharing practices is built through literature review, and the relationship among them is explored. It is proposed that both knowledge sharing means and governance mechanism impact the knowledge sharing process directly. Also, the governance mechanism has an indirect impact on the knowledge sharing process by influencing the choice and usage of the means. This study will provide organisations and policy makers with a framework to better understand knowledge sharing practices from different perspectives. It also provides a valuable insight of how to choose the appropriate knowledge sharing means and take into account the governance mechanism to enable the knowledge sharing process to be more effective. Keywords: knowledge management, knowledge sharing, knowledge sharing process, knowledge sharing means, governance mechanism, healthcare

1. Introduction In recent studies, knowledge has been recognised as a principal source of value creation (Poston and Speier, 2005). The availability of accurate and timely knowledge enables organisations to respond rapidly and with the appropriate measures to create high quality services, products, and processes (Nonaka et al., 2000). Therefore, the competitive advantage of organisations lies in their ability to effectively manage knowledge. However, individuals and organisations are faced with huge amounts of data and information which is crucial in nature but hard to manage appropriately (Carayannis, 2005). Therefore, a business philosophy namely Knowledge Management (KM) has been introduced. KM is concerned with all processes related to knowledge creation, storage, sharing, and application (Alavi and Leidner, 2001). Since the benefit of knowledge is limited if it is not shared, knowledge sharing is perceived to be the most important process of KM (Leonard‐Barton, 1995). The sharing of knowledge while largely invisible plays a critical role in achieving greater access and equity (Boisot, 1998). Knowledge sharing is especially important in industries where knowledge is a key asset like healthcare organisations. The healthcare industry is a knowledge rich community which deals with patients’ lives and wellness. Losing the opportunity of having the right knowledge at the right time can lead to medical errors (Kilo, 2005). Therefore, knowledge sharing is a must in healthcare organisations. Acknowledgement of the importance of knowledge sharing in the healthcare industry has resulted in some valuable contributions trying to understand this phenomenon. Most of these contributions are about the nature of knowing (e.g. Miller, 2012; Zigan et al. 2010), knowledge sharing means (e.g. Bradley et al., 2012; Ozdemir et al., 2011), and the governance mechanisms needed (e.g. Currie and Suhomlinova, 2006; Aron et al., 2011). Despite the richness and depth in these three streams of research, at present there is no study integrating these various insights. Therefore, there remains uncertainty about the intrinsic relationship among these concepts as knowledge sharing is a combination of process, technology, and people and cannot be considered in isolation (Awad and Ghaziri, 2007). Thus, it is worthwhile to examine firstly, the relationship among these concepts and secondly, their impact on knowledge sharing performance. This study provides a comprehensive view of knowledge sharing practices from the three mentioned perspectives. Drawing upon the descriptive process of theory building, a model for these three aspects of

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Negar Monazam Tabrizi knowledge sharing practices is built through literature review, and the relationship among them is explored. The study is organised as follows. First, the concept of knowledge sharing is discussed in general and in the healthcare industry in particular. Next, the research methodology is described. The knowledge sharing process, means, and governance mechanism are respectively discussed in the following sections. Further, the relationship among these concepts is explored and the research model of the study is proposed. Finally, we conclude with the discussion of key findings.

2. Knowledge sharing Knowledge is one of the few assets that tends to grow when it is shared (Quinn, 1996). Knowledge sharing can be defined as “team members sharing task‐relevant ideas, information, and suggestions with each other” (Sirvastava et al., 2006; p.4). The availability of shared knowledge is necessary for adapting, extending and creating new knowledge and innovation (Hislop, 2007). Despite the importance of knowledge sharing, it is not easy to implement. Due to the nature of knowledge (i.e. tacit and explicit) and people’s diverse intentions, knowledge sharing is a fragile process. Knowledge is a valuable asset which is often considered as a source of power so people might be reluctant to share their knowledge to others (Kankanhalli et al., 2005). Also, knowledge sharing is severely constrained in the absence of knowledge sharing means. Reliable knowledge and effective communication are critical factors that can be achieved by use of appropriate knowledge sharing means (Bradley et al., 2012). Therefore, managing knowledge and people effectively and using proper knowledge sharing means is key to a successful knowledge sharing practice. To study these factors deeply, it is also necessary to study knowledge sharing process. Thus, our view is that; three factors influence knowledge sharing performance, that is, process, means, and governance mechanisms. Process of knowledge sharing refers to the nature of knowing and the stages of how knowledge is shared from one party to another. Knowledge sharing means refers to what kinds of means are adopted to realise the movement of knowledge during the process of knowledge sharing. Governance mechanisms can be defined as how the event in each stage is supported or motivated during the process of knowledge sharing.

2.1 Knowledge sharing in healthcare industry Improved quality of care in medical areas is an overriding strategic goal of most healthcare organisations. Knowledge sharing is seen as a means to facilitate knowledge acquisition and knowledge distribution to reach this goal (Aron et al., 2011). However, knowledge sharing in the healthcare industry is complicated due to time pressure, shift work, mobility of knowledge, and professional boundaries, amongst of other factors. First, delivering healthcare to patients is complex and is highly dependent on available knowledge. Healthcare providers need to have access to the right knowledge at the right time, in order to be able to make decisions in a more timely and effective manner (Lin and Chang, 2008). Second, in healthcare organisations knowledge is mobile. Healthcare providers use knowledge from multiple sources and this knowledge should be shared by different actors. Patients are now managed by a team of professionals each specialising in one aspect of care. This shared care needs the ability to share knowledge easily among professionals (Singh et al., 2010). Third, one of the most important aspects about healthcare organisations is the sharing of knowledge between the different shifts. It is very important that workers share their knowledge about events and problems that occur during previous shifts. Otherwise, for instance, a nurse might have not the knowledge to complete the process from the previous shift. In this case, a patient could be placed at risk for injury (Kilo, 2005). Therefore, shift workers are strongly dependent on a shared network of knowledge and knowledge based artefacts that help them to share knowledge appropriately. Finally, another aspect that makes knowledge sharing difficult in healthcare is professional boundaries. Bate (2000) described professional boundaries as ‘endemic tribalism’ between medical professionals. Although it is very important to have the right knowledge at the right time in healthcare, professional boundaries impede knowledge sharing in this industry. To overcome these challenges, knowledge sharing in the healthcare industry requires special attention, in order to create an environment to facilitate knowledge sharing.

3. Methodology The theory building process has two stages: descriptive stage and normative stage (Carlile and Christensen, 2005). This study is based on the descriptive stage as the purpose of the study is to build a model for knowledge sharing practices. Both stages of the theory building process contain three steps namely observation, categorisation, and association. In the first step, observation, the phenomena will be observed. In this study, the phenomenon is the existing literature on knowledge sharing in healthcare and the observation step is to identify attributes of knowledge sharing practices via a literature review. In the second stage,

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Negar Monazam Tabrizi categorisation, phenomena will be classified into categorises which in this study are based on attributes of knowledge sharing practices. Finally, the association step is to define relationships between step one and two. In this study, the links among different kinds of knowledge sharing attributes is explored. In exploring the phenomenon from a broad view, this study collected articles dated from 2000‐2012 by using a systematic review. The literature mainly came from the journals in field of Healthcare, KM, and Information Technology (IT). Three top journals from each research field were chosen. Then, each journal was searched issue by issue by looking at paper titles and abstracts. For any that were relevant to knowledge sharing in healthcare, the full text would be read. It should be highlighted that in the case of KM journals, there were many articles related to knowledge sharing. Therefore, to ensure that no articles were missed and to save time, key words i.e. “knowledge sharing in healthcare AND PUB.exact ("journal of knowledge management")” were used in ProQuest. The retrieved articles were then reviewed with the same method as described earlier. The breakdown of sources that contributed to the final report is shown in Figure 1. In addition, other papers published in other outlets were used in this study. For instance, most of the widely cited papers on knowledge sharing such as Alavi and Leidner (2001), Nicolini et al. (2008), etc. were included. References of articles used in final reports which were relevant to this study were also included.

Figure 1: Summary of sources contributing to the systematic review

4. Knowledge sharing process The knowledge sharing process has been studied by several researchers. Davenport and Prusak (1998), Lin et al. (2005), and Hansen (1999) suggested a two‐stage model: sending and receiving. Szulanski (1996) proposed a model with four stages: Initiation, Implementation, Ramp‐up, and Integration. This model is developed based on the investigations of the rich empirical researches on technology transfer, social change, innovation diffusion, and implementation. Based on their model, a four stage model is developed in this study which is shown in Figure 2. The proposed model of this study differs from the previous models in several ways:

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The knowledge sharing process is also analysed from the perspective of project management, since they have some similar processes (Busby, 1999; Kamara et al., 2002). Investigating knowledge sharing in this way helps to overcome some limitations in unpicking knowledge sharing.

Our model considers another stage namely, requirements stage, as the requirements associated with the sharing of knowledge need to be specified as clearly as possible.

Our model also adds another stage called follow‐up stage. Despite the importance of follow‐up stage, this phase is often neglected. The net benefits and acquired relevant experiences and lessons which can reflect the effect of shared knowledge can be clarified in this stage.

Our model considers ramp‐up and integration stages as activities of implementation stage and labels the ramp‐up stage as knowledge absorption and utilisation.

In our model, the stages of the knowledge sharing process are subdivided into several specific sub‐stages, in order to make knowledge sharing clearer.

Figure 2: A model of knowledge sharing process Initiation stage is the beginning of the knowledge sharing. In this stage, the idea for the knowledge need is explored by the receiver or by the source (demand analysis). In addition, they need to search for suitable partner (Matching). Then, the knowledge source will decide to share his/her knowledge or not (feasibility analysis). In the requirements stage, both knowledge source and knowledge receiver need to choose appropriate knowledge sharing means and corresponding governance mechanisms. Knowledge sharing takes shape during the implementation stage. Knowledge source first needs to prepare knowledge in terms of collecting the necessary parts of knowledge and then parcelling them up in order to meet the receiver’s knowledge requirement. Then, the knowledge source tries to transfer the prepared knowledge. The knowledge receiver tries to absorb the knowledge and utilise it based on his requirements. For instance, he tries to remove the noise or disturbance in the shared knowledge to obtain the useful part of it to solve the target problem. Finally, the knowledge receiver integrates the useful part of knowledge into organisation’s knowledge base. During follow up stage, knowledge source and knowledge receiver need to evaluate the issues concerning both during the knowledge sharing process, for instance, whether the knowledge sharing means is valid, whether the selected knowledge sharing means is appropriate, etc.

5. Knowledge sharing means Knowledge sharing means has been described by Ruggles (1997) as technologies used to enable and improve the implementation of knowledge sharing. Not all knowledge sharing means are IT based, as everyday means such as face to face interactions, training, etc. can be utilised to support knowledge sharing. In this study, two different types of knowledge sharing means will be discussed: techniques and Information Communication Technologies (ICTs). For the purpose of this study, techniques are defined as non‐IT based means and ICTs are

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Negar Monazam Tabrizi defined as tools that facilitate the sharing of knowledge by electronic means. Tacit and explicit nature of knowledge is also taken into account in this study as there are particular means for each of these types of knowledge. Some of the knowledge sharing means that have been identified in the literature are summarised in Table 1: This list is not conclusive; it provides an overview of knowledge sharing means adopted by healthcare organisations to share knowledge. Table 1: Knowledge sharing means

Table 2: Knowledge sharing means continue

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Negar Monazam Tabrizi Therefore, knowledge sharing can occur through different means. Techniques are affordable as no sophisticated infrastructure is required. They are also easy to implement because of their simple and straightforward nature. However, techniques might be too slow and less effective, especially, for health care organisations that need accurate knowledge at the right time (Olsson et al., 2008). In contrast, the use of ICTs makes knowledge sharing more efficient, faster, and more convenient (Ruikar et al., 2007). These types of knowledge sharing means have the potential to greatly facilitate knowledge access, improve communication, eliminate double documentation, and as a result increase quality of healthcare services in the long run (Gerber et al., 2010). Although ICTs play a significant role in facilitating knowledge sharing, they are not easy to implement due to the requirement of IT infrastructure and IT skills. Also, they are expensive and difficult to acquire and maintain (Aron et al., 2011).

6. Knowledge sharing governance mechanism Governance mechanism refer to the governance structures and coordination mechanism to facilitate KM activities i.e. knowledge creation, knowledge sharing, etc. (Grandori, 2001). At present, many studies have investigated governance mechanisms so as to favourably impact knowledge sharing. General speaking, all these studies are seeking to find the facilitating factors to motivate relevant actors to participate in the knowledge sharing process and to make the knowledge sharing process be efficiently conducted. These factors can be categorised into different groups: organisational culture, technical support, and organisational context (Aron et al., 2011; Guah and Currie, 2004; Sensky, 2002; Currie and Suhomlinova, 2006). Those factors which try to motivate actors to participate in knowledge sharing mainly provide an incentive effect for knowledge sharing and they include organisational culture. On the other hand, those factors which try to make knowledge sharing be efficiently conducted provide supportive conditions for knowledge sharing. Organisational context and technical support are among this group. The governance mechanisms of knowledge sharing are shown in Figure 3. It is highlighted that most of the governance mechanism in hospitals are not different from those identified in other industries (Sensky, 2002; Nicolini et al., 2008).

Figure 3: Governance mechanisms of knowledge sharing; Source: Based upon literature review Technical support: There is little doubt that knowledge sharing can be improved, (especially in terms of reducing cost caused by time and distance), by the use of ICTs (Fichman et al., 2011). The key issue, however, is to choose and implement appropriate ICTs that provide a close fit between workers and their requirements. Sufficient technology skill, maintenance of ICT systems, and compatibility between ICTs and processes are also reported as a cause of strong knowledge sharing. Lack of any of these issues can lead to knowledge sharing failure even with having appropriate ICTs in place (Bradley et al., 2012; Aron et al., 2011; Guah and Currie, 2004). Organisational context: For successful knowledge sharing, it is very important that knowledge sharing be supported by the organisational context (Hislop, 2007). Leadership support, time and resources, and human resource management are reported as the main factors of organisational context in the literature. Leaders set the example for others, they have a direct impact on the organisational culture and how the organisation approaches and deals with knowledge sharing practices (Sensky, 2002). It is also important that hospitals offer enough time and resources to allow staff to share their knowledge (Currie and Suhomlinova, 2006). Furthermore, people are the core of creating organisational knowledge, because it is people who create and

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Negar Monazam Tabrizi share knowledge. Therefore, it is critical to manage those who are willing to create and share knowledge (Sensky, 2002). Organisational culture: It defines the main beliefs, norms, values, and social customs that govern the way people act and behave and therefore it influences the efforts that individuals are willing to share their knowledge (Taylor and wright, 2004). The biggest challenge for hospitals actually lies in building an environment in which professional communities can trust each other; otherwise they are unlikely to share their knowledge (Dean, 2002). Tolerance of making mistakes also plays a significant role in the knowledge sharing, especially in healthcare. Since healthcare professionals are harshly blamed for errors, they hesitate to report errors. However, if hospitals create an environment in which mistakes can be tolerated, professionals will be motivated to report errors and learn from them to improve quality services (Currie and Suhomlinova, 2006). Reward is also very important to motivate actors to participate in knowledge sharing. Knowledge sharing participants need to see benefit for themselves in the knowledge sharing process. Otherwise, they can abandon knowledge sharing processes when they face the first problem in sharing/receiving knowledge (Lin and Chang, 2008).

7. Knowledge sharing influencing factors relationship So far it is clear that knowledge sharing process could only occur when appropriate governance mechanism and means are in place. First, both knowledge source and receiver, and all necessary actors, need to align their interest in participating in knowledge sharing process. To do this, they need to be supported and motivated by organisational and cultural issues to overcome their obstacles (Sensky, 2002). Take time for example. If knowledge source/receiver does not have enough time to share/receive knowledge, knowledge sharing cannot be implemented. Therefore, governance mechanisms have direct impact on knowledge sharing process. Second, when all actors decide to participate in knowledge sharing process, they need to adopt appropriate means to share knowledge from source to receiver. Content and richness of knowledge should be handled by knowledge sharing means (Zigan et al., 2010). Thus, it is very important to choose an appropriate means. As a result, knowledge sharing means directly prompt knowledge sharing process forward. However, the challenge of taking advantage of knowledge sharing means, especially ICTs, is to integrate them with different aspects of knowledge sharing. Hospitals can encounter weak knowledge sharing process even by adopting appropriate means due to the lack of organisational, cultural and technical support (Nicolini et al., 2008). Hospitals invest in ICTs to enable knowledge sharing, since they believe knowledge is crucial for their success (Sensky, 2002). However, if these means are not supported by technical support, knowledge sharing failure can result. Also, organisational and cultural issues influence the choice and usage of the knowledge sharing means by motivating the willingness and cognition of the units of knowledge sharing, which will further impact the knowledge sharing process. Finally, based on the descriptive process of theory building, key concepts from the literature are integrated into a model to identify mechanisms and elements, when present/absent, that contribute to a strong or weak approach of knowledge sharing processes in complex environments such as hospitals (Figure 4). So far the descriptive process of theory building has not been used to develop comprehensive frameworks for different aspects of knowledge sharing and their relationship. This model provides organisations and policy makers in healthcare industry with a framework to better understand how strong knowledge sharing process can be achieved with the presence of appropriate knowledge sharing means and governance mechanisms. This model, especially, can be discussed in terms of the role of ICTs in facilitating knowledge sharing and challenges around use of ICTs for the purpose of knowledge sharing. This can help healthcare organisations to create an environment to reduce the extra time and efforts required to share and use knowledge, to increase the exchange of knowledge through mobility of knowledge and in different shifts, and to extend the culture and skills for engagement in the knowledge sharing process across professional boundaries by better use of knowledge sharing means.

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Figure 4: Proposed model for describing knowledge sharing

8. Conclusion Strong approach of knowledge sharing supports the day‐to‐day activities in knowledge intensive organisations such as the healthcare industry. This study integrates key concepts from the literature into a model to explain a strong approach of knowledge sharing. It is proposed that knowledge sharing performance is influenced by the impact of knowledge sharing means and governance mechanisms on the knowledge sharing process. An analysis of these three aspects of knowledge sharing has been carried out and their relationship among them explored. Knowledge sharing means impact knowledge sharing process directly. Governance mechanism not only directly impacts the knowledge sharing process but also indirectly influences the choice and usage of knowledge sharing means. These three kinds of interrelated influencing factors of knowledge sharing have an impact on knowledge sharing performance.

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Negar Monazam Tabrizi Currie, G. and Suhomlinova, O. (2006). The Impact of Institutional Forces upon Knowledge Sharing in the UK National Health Service: The Triumph of Professional Power and the Inconsistency of Policy. Public Administration, 84(1): 1– 30. Davenport, T.H.& Prusak, L. (1998). Working Knowledge: How Organisations Manage What They Know. Harvard Business School Press, Boston. Dean, B. (2002). Learning from Prescribing Errors. Quality and Safety in Health care, 11(3): 258–260. Fichman, R.G., Kohli, R., & Krishnan, R. (2011). The Role of Information Systems in Healthcare: Current Research and Future Trends. Information Systems Research, 22(3): 419‐428. Gerber, T., Olazabal, V., Brown, K., & Pablos‐Mendez, A. (2010). An Agenda for Action on Global E‐health. Health Affairs, 29(2): 233‐236. Grandori, A. (2001). Neither Hierarchy Nor Identity: Knowledge Governance Mechanisms and the Theory of the Firm, Journal of Management and Governance, 5(3/4): 381‐399. 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The Systemic “Learning by Sharing” Diamond: How to Implant it Concretely in Private Organization? Alexandru‐Ionuț Pohonțu1, Camille Baulant2 and Costache Rusu1 1 Gh. Asachi Technical University of Iasi‐Romania, Romania 2 Université d’Angers‐France, France ai.pohontu@gmail.com camille.baulant@univ‐angers.fr c.rusu@cetex.tuiasi.ro Abstract: Purpose – To design a research methodology in order to test the conceptual model of learning by sharing process in concrete firms. Methodology – Based on literature review, the process of learning by sharing is built on four key differentiations inside organization: knowledge, competency, cooperation and competition. Findings – The sustainability of this reasearch methodology proposal should ensure that organizations can assess by their own employee attitudes and behaviors concerning the learning and knowledge sharing as one integrated process. Practical implications – The learning by sharing process could involve three advantages for firms. Learning by sharing approach is firstly a dynamic way to increase in the long run the competitiveness of firms. Secondly, the learning by sharing process could be applied in each firm without additional budget. Finally, all employees will work in a peaceful environment and they could be more creative. Originality/value – Although knowledge management and intellectual capital are a very wide spread in present literature, the concept of learning by sharing is less approached. This paper propose to show how the diamond analysis of learning by sharing is a new approach to conciliate “supply factors” and “demand factors”, “codified knowledge” and “tacit knowledge” as well as “cooperative actions” and “competition actions” inside each firm in order to be more efficient in knowledge economy. Keywords: learning by sharing, methodology, individual and organizational benefits

1. Introduction Along the knowledge management literature, many authors argued that knowledge is the ultimate resource for competitive advantage of an organization (Sharif et al., 2005), and research in this field are important since they contribute to the mindset of organizations in order to compete effectively in knowledge economy (Stewart, 2002). Knowledge economy is based on knowledge creation through their use in business area, with the help of innovation. Taking into account that the current environment is characterized by globalism, competition and dynamism, the success of an organization depends on its ability to learn from its interactions with the environment (such as relationship with the supplier, competition, customer, and state institution), its internal dynamics (from the employee’s knowledge and capabilities). In doing so, organizations need to provide the necessary conditions to enable the creation and continuous updating of knowledge, putting them into practice, allowing employees to continuously learn and prepare to assist them in generating new ideas. The way in which knowledge transfer can be connected to organizational learning in order to acquire and maintain competitive advantage have a fundamental importance for organization. Therefore, if an organization is able to enhance learning by transferring his knowledge, should become more efficient and competitive. For a long time, the need for cooperation was shown. In the last period, in the modern organization a strong interest on human cooperation has been shown, in order to achieve synergistic effects, so that collective effort can lead to superior results of individual effort. Thus, cooperation among human resources can be considered relatively simple in accordance with current standards. In this way, it can be said that the ability for knowledge sharing is perceived as one of the greatest advantages of cooperation and collaboration of human resources. In addition, this process is seen as a key resource for the continuous improvement of the organization, innovative capacity, and building and maintaining a sustainable competitive advantage (Porter, 1990). Yet there is a dark side that should not be neglected, as Koulikov (2011) states that organizations tend to perceive knowledge sharing as a waste of time and money. Therefore, it is quite difficult to recognize the importance of knowledge sharing, and organizations must be aware that the impact of knowledge sharing between employees can be seen from the long term view.

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Alexandru‐Ionuț Pohonțu, Camille Baulant and Costache Rusu From this perspective, the focus of this article is to summarize an approach to theoretical systemic model learning by sharing and to design based on this model a research framework able to be applied in organization in order to conceive a long term strategy for propelling organizational learning as an enabler of knowledge sharing, prioritizing the relationship between organization differentiations. Moreover, knowledge sharing as well as organizational learning is often problematic, so in the proposed research model both influencing factors of these two processes are included.

2. An overarching theoretical framework – learning by sharing In order to approach the integrative model of learning by sharing, it is important to mention what it is involved in knowledge sharing and organizational learning and which relationship exists between these two processes.

2.1 Approaches on learning and knowledge sharing The value of knowledge sharing is related to the fact that organizational knowledge is difficult to imitate. Although it is already known that the greatest impact of knowledge sharing is the creation of new knowledge, this process cannot be viewed from a single perspective. Knowledge sharing depends on many factors, individual or organizational, which requires special attention when organizational strategies and practices are developed on long term view. From a conceptual point of view, there are various approaches of knowledge sharing. In essences, knowledge sharing is a process that takes place between individuals, through which knowledge is transformed into a form so that can be understood, acquired and used by other individuals. In the literature there are different approaches on knowledge sharing. These approaches are focused on two dimensions of knowledge, know what and know how (Polanyi, 1966, Bratianu, 2009). Others had a perspective on tacit or explicit characteristics of knowledge (Nonaka and Takeuchi, 1995), coded and process (Alley, 1997), information and knowledge point of view (Davenport and Prusak, 1998), and personal and social (Kolb, 1984). In terms of managerial point of view, knowledge sharing can have three roles: interpersonal, informational and decisional. Each of these roles is concentrated in the function of providing, processing and application of information and knowledge. By looking in the literature, it can be stated that the most common theoretical model that analyzes the knowledge sharing process is SECI model, proposed by Nonaka and Takeuchi (1995). In essence, the elements from the SECI model address the issues regarding the foundation of the concept of knowledge. However, this model did not yet know an empirical analysis that make an approach on organizational learning mechanism. As the literature states that knowledge is power, in this way the knowledge sharing must be seen essentially for the survival of the organization. However, knowledge sharing behavior in the organizational context is not fully understood. Thus, identifying the factors that motivates employees to share knowledge for personal or organizational benefits can be seen as a high priority for organization. While, the factors that influence behavior towards knowledge sharing can be speculated, it is important to study carefully the previous studies on knowledge sharing. Regarding learning, this process takes place individually, and once employees accumulate skills, the next critical question is how the organization should proceed to incorporate these skills at the organizational level. In other words, to become more effective for the organization, individual learning must be transformed into organizational learning. Organizational learning occurs when an organization addresses a particular problem or group of problems faced. Subsequently, the problem is solved by taking into account lessons learned and assimilated competences which represent the collective learning from past, present and future. From KM perspective, organizational learning represent a key factor which involve a continuous evaluation of organizational experiences, and converting that experiences into organizational knowledge, available to whole organization. Therefore, can be identified two kinds of organizational learning process: learning how and learning why. Hence, organizational learning is described as how the organization builds and manages knowledge and routines around the business, and how these processes develop organizational efficiency by improving the skills of workforce. Looked by another perspective, learning organization allow employees to adopt and develop their workplace competences influencing and helping them in terms of creativity, innovative thinking,

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Alexandru‐Ionuț Pohonțu, Camille Baulant and Costache Rusu causing them to become more confident and competent. As well, organization can be considered to be a learning system based on processes involved in the generation of new knowledge or modification of existing knowledge. In this regards, the first stage of this system is the transformation of learning process experiences in organizational routines. The next process is the exploration, which include experimentation, risk‐taking, discovery and innovation. The final stage is focused on exploitation and surprise refinement, choice, efficient production, selection and action implementation and enforcement (Davenport and Prusak, 1998).

2.2 The need for an integrated model based on learning and knowledge sharing In a general perspective, knowledge must be first created (learning organization) in order to be then, shared (knowledge sharing). Thus, knowledge sharing arises once the learning takes place. Normally, the extraction and creation of new knowledge involves learning, and knowledge sharing involves the acquisition and application of new knowledge. Taking into consideration this, every aspect of knowing have is correspondent activity in learning process. As a final result, the literature revealed that both knowledge sharing and organizational learning represent key factors on organizational performance and innovation. Knowledge sharing provide a basis for organizational learning, therefore, to enhance learning there is a necessity of a model of knowledge sharing meant to be interactive and collaborative and dynamic. Collaboration is a process through which individuals who see different aspects of a problem can constructively explore their differences and search for solutions that go beyond their own limited vision of what is possible (Tiwana, 2000). The only way to allow knowledge sharing is to put individuals together through collaboration. Therefore, the development of individual and group competencies through learning may be the key to a process of an efficient knowledge sharing. Organizational learning and knowledge management may seem to be complementary, on one hand knowledge are created through the management of organizational learning processes, and by other hand the results are managed through knowledge management processes (Spender, 2008). In creating and maintain the knowledge within the organization, knowledge management theory recognizes the importance of community of practice CoP (Brown and Duguid, 1991). CoP is seen as an important instrument for overcoming the behavior constraints and manifesting the emergence of new organizational culture by emphasizing on organizational learning and knowledge sharing. Conventional treatment or organizational learning tends to be correlated to individual learning. A good example of a model concentrated on knowledge sharing and learning is the experiential learning theory (Kolb, 1984) which provides a model for description of the relationship between learning, learning styles, skills and knowledge sharing. In this model learning is described as cycle of four stages, namely divergent, assimilative, and accommodative and convergence. These learning styles identify preference for different types of knowledge and for knowledge processes. Such model provides a theoretical approach on understandings how individuals describe and share their knowledge acquired through learning process. Based on this model and organizational model developed by March and Olsen (1975), Kim (2004) makes a new model by focusing on characterization of the organizational learning as a developed mental model shared within the organization. As appropriate, this approach shows individual learning as the source of organizational learning. Taking into account previous model concentrated on learning and knowledge sharing, nowadays, the next generation of knowledge management is in place, and the focus is oriented on people as unique holders of knowledge, and the exchanges between people. In this context, the new knowledge management should begin with a focus on organizational learning, and building and facilitating communities of practice. Therefore, organizational learning is a key dimension to knowledge management, which involves a continuous assessment of organizational experience, and converting that experience into knowledge and making it accessible to the organization as a whole.

2.3 Approaches on learning by sharing model Considering that the complexity and the uncertainty can be seen as external factors and mobility and the dynamism as internal factors, of organizations, the systemic Learning by sharing model (Pohontu, et al., 2012) is approached. Thus, the systemic Learning by Sharing Diamond (as can be seen on Figure 1) is designed based on theories of communities of practice ‐ CoP (Duguid, 2005) which focuses only on internal process within

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Alexandru‐Ionuț Pohonțu, Camille Baulant and Costache Rusu organizations (Duguid’s triangle: horizontal arrow which links knowledge (knowing that) and competency (knowing how) and the bottom of chart (cooperation)), and on the theory of Porter (1985, 1990), with the 4 forces involved in the competitive advantages, which focuses both on internal (how to produce knowledge and competency within organization) and external environment of organizations (how to cooperate and how to face competition). In others words, this model aims to correlate internal procedures, those how refers to knowledge sharing, with the external environment.

Source: Pohontu, et al. (2012) Figure 1: The systemic “learning by sharing” diamond In essence, through this model, the competitive advantage and the process of learning by sharing are viewed as two common processes, by their pursued objectives (constant adaptation according to market needs, in order to remain efficient) and in terms of time perspective (long‐term). Nevertheless, the greatest challenge of this model is the efficient mobilization of certain interactions within organizations before (or at the same time) to compete with the external factors, as organizations must transform knowledge (owned both by organizations and its members) in competences using cooperative relationships. Thus, the proposed model is supposed to be both open and closed. It is opened to external factors to adapt the organizations to the external knowledge and to the competition of the other organizations. It is closed to internal factor of the organization because in order to develop good routines and practices, it is necessary to create confidence among employees and trust inside the organizations. On other hand, as mentioned before, due the continuous movement of individuals within the organization, learning and knowledge sharing it are very dynamic. The purpose of learning and knowledge sharing leads to competencies development, namely acquiring or increasing (maintaining) competitive advantage. Therefore, the proposed model aims to achieve a synergy between two processes: learning and knowledge sharing. Yet, the most important characteristics of this model is the fact that it promotes learning by knowledge sharing taking in consideration the four differentiations of an organization, namely cooperation differentiations, knowledge differentiations, competency differentiations, and competition differentiations. On the vertical side of the systemic “learning by sharing” diamond is presented competition differentiations among with cooperation differentiations. Competition differentiations are based on co‐opetition, more specifically a dynamic mix of cooperation and competition. In essence, co‐opetition describes both competition and cooperation. From another perspective, in knowledge economy, competition among organizations has to move into co‐opetition relationships, because the knowledge is now becoming more collective than ever. This concept describes the fact that in current business environment, in order to be competitive, organizations must, in knowledge economy, collaborate with other organizations from same field. Thus, competitors can come together to enjoy a common edge through a temporary or long term partnership agreement on knowledge sharing. On the other side, the cooperation differentiations reefers to co‐learning, more specifically to the process of mutual validation and mutual creation of knowledge acquired. Unlike individual learning,

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Alexandru‐Ionuț Pohonțu, Camille Baulant and Costache Rusu people involved in co‐learning harness the resources and the competencies of each other. The next differentiation included is the knowledge differentiation. By definition, knowledge generation represent a dynamic process of interaction between endowed partners. Organization could be considered as a knowledge system in a strong sense, therefore the organizational knowledge cannot be surveyed as a whole; it is not self‐ contained; it is inherently continually reconfiguring and based on interactive generation of knowledge. As was mentioned before, knowledge differentiation comprises the theoretical aspect of the act of knowing, by the aspect of knowing that. Thus, this process can be viewed as the first step in competencies development and co‐innovation, due the fact that includes more explicit knowledge, and less tacit knowledge. On the other side, competency differentiations refer to the practical sense of act of knowing, namely knowing how. In contrast with knowledge strength, competency strength is constructed mostly on tacit knowledge. Due the fact that knowing how makes knowing that actionable, competency differentiations can be viewed as the second step in competencies development and, therefore, the result of co‐management. In this regard, co‐ management can be considered as a knowledge partnership, as well as a problem solving process (rather than a static arrangement), and comprises both knowledge generation and joint learning. Joint learning approach is closed to the concept of cooperative learning. In essence, the learning by sharing process has three advantages upon the traditional approaches (binary approaches). Firstly, learning by sharing approach is a dynamic way to increase in the long run the competitiveness of firm and nations. Secondly, the learning by sharing process could be useful and applied in each firm without any money. The workers must just cooperate and be opened to suggestions of other workers. Thirdly, and it is very important for the long run dynamic, all the workers will work in a peaceful environment (without short run and unfair competition). All the workers will work with pleasure and they could have more and more satisfaction to keep working, so they will work better.

3. An overarching research methodology – proposed learning by sharing research framework In the context of present socio‐economic reality this article propose to addresses in a integrative manner the development of a framework in order to find out how knowledge sharing can be promoted through learning in order to enhance individual and organizational benefits, by taking into consideration influencing factors of both knowledge sharing and organizational learning.

3.1 Previous empirical approaches on knowledge sharing and organizational learning According to literature there are a lot of empirical studies that present an approach to factors that have influence on knowledge sharing. Yet, these factors are divergent, and their relationships to the knowledge sharing behavior are different in some cases. In addition, the theories used to make an explication to knowledge sharing are diverse. Taking into consideration this facts an extraction of the most influencing factors on knowledge sharing could be a great challenge. To summarize, a table of four dimensions (cost, extrinsic benefits, intrinsic benefits and contextual factors) made by Hung and Chuang (2009) is covering up a total of 10 factors according to the framework proposed by Kankanhalli et al. (2005). These four dimensions and factors are presented in the table below. Table 1: Dimension of influencing factors of knowledge sharing (Adapted from Hung and Chuang, 2009) COST Loss of knowledge power Codification effort

EXTRINSIC BENEFITS Organizational reward Reputation

INTRINSIC BENEFITS Knowledge self‐efficacy Enjoyment in helping others Reciprocity

CONTEXTUAL FACTORS Trust Pro‐sharing norms Identification

Authors such as Hutchings and Michailova (2004) have suggested that knowledge sharing is influenced in a high percentage by the individuals cultural values Therefore, the results on knowledge sharing differ on from variety of national or ethnic groups (Ardichvili et al., 2006). Also, on knowledge sharing, personal ethics play an important role. Given that knowledge sharing is controlled by the individuals, knowledge sharing could be considered as an ethical behavior. In this regard, Wang (2004) found a significant positive relationship between

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Alexandru‐Ionuț Pohonțu, Camille Baulant and Costache Rusu ethics and intentions to knowledge sharing willingness. Moreover, Wang concluded that individuals, who feel threatened by colleagues, could reduce their intention to knowledge sharing willingness. Organizations vary in all aspects. Thus, identifying the most influencing factors of organizational learning is very valuable. Fixing these factors would allow individuals within organizations to benefit from some aspect that would encourage a context for organizational learning. In this sense, Lohman (2005) stated that factors such as initiative, positive personality, commitment, trust, need for learning are motivators for organizational learning. In contrast, an unfavorable organizational culture, lack of availability, lack of time and lack of college’s proximity have a high influence on diminishing organizational learning. At the same time, Albert (2005) stated that the top management support and practices that promotes learning are motivators for organizational learning and for change. Besides the previously mentioned influence factors, others influencers involved in co‐opetition was added in order to be presented a set of dimensions through which the openness to learning through learning by sharing is evaluated.

3.2 Proposed research framework Taking into account the conceptual model of learning by sharing, the following research model is proposed with the purpose of investigating the relationship between four differentiations. In order to design this framework, the approach of Rajagopalan et al. (1993) is used by being classified in threefold: determinants, processes and outcomes.

Figure 2: Proposed learning by sharing research framework Determinants are those factors that are included in the mechanism to promote organizational learning and knowledge sharing. These determinants are categorized in two, namely organizational and individual factors. Moreover, in this model was chosen and grouped four dimensions that are involved in co‐opetition influences, namely trust, commitment, mutual benefits and organizational competitiveness. Regarding the learning by sharing variable, it refers to the way in which employees can share their experiences, expertise and contextual information’s with others. This variable is comprised of two‐dimensions, one related to knowledge sharing behavior, and another related to organizational learning behavior. The reason for this is the fact that knowledge sharing is the basis of the learning process, and less analysis, understanding and the use of experiences in order to determinate the openness in front of further learning.

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Alexandru‐Ionuț Pohonțu, Camille Baulant and Costache Rusu Outcomes variables, are, as well, categorized in two, namely individual and organizational benefits. In terms of individual benefits, they refer to intangible benefits such as accumulation of new knowledge, improving the relationship with the colleagues, satisfaction of helping others, workload reduction and tangible reward system. As for the organizational benefits, they refer to orientation of the organization towards innovation and organizational performance, and gain some market shares. As seen in the research framework there are five variables divided in 15 dimensions (the dimensions are listed in appendix). The dimensions used in this research model are selected and adapted from previous studies; they are validated and used by most of researchers in the field. However, the novelty of the research model comes from the fact that knowledge sharing and organizational learning are combined under a single process and studied in accordance with the key factors of co‐opetition, individual and organizational factors and organizational and individual benefits. Therefore the following hypotheses are proposed to be studied in accordance with the previous framework presented: H1 – Individual factors positively influence learning by sharing process. H2 ‐ Organizational factors positively influence learning by sharing process. H3 ‐ Factors involved in co‐opetition positively influence learning by sharing process. H4 – Learning by sharing process positively influences individual benefits. H5 ‐ Learning by sharing process positively influences organizational benefits.

3.3 Research methodology Research in management can be exploratory, descriptive or causal (Zikmund, 2003). This study is designed to provide a comprehensive picture on synergistic processes involved in knowledge sharing and organizational learning, by describing the determinants involved in these concepts. Given the distinctive characteristics and selection criteria of the research paradigms widely accepted, namely quantitative and qualitative research, the present study will adopt a quantitative paradigm and will be use a deductive approach to test relationship between variables identified from the theoretical model. Among the four basic types used in research techniques, namely experiment, questionnaire, observation and survey data (Zikmund, 2003), the survey was chosen to correspond to the research questions and hypotheses due to several reasons. Firstly, the questionnaire provides a fast, efficient and accurate assessment of the information about respondents and is more suitable when there is a lack of secondary data, as in the proposed framework. Secondly, a review of previous empirical studies on knowledge sharing capabilities shows that the survey‐based research is the most popular method used to measure this concept. The presented framework will be based on an integrative study and requires a complex research strategy, given the large number of variables involved, the complexity of each variable, and the relatively ambiguous and poorly defined nature of some of the involved variables. As it is already stated, each dimension considered was constructed or adopted based on the literature, a set of representative questions in order to reach the most important aspects of the dimension. In designing and the adaptions of the questions were considered the following basic rules: questions applicability, clarity and accuracy of the questions, subject’s ability to respond correctly, simplicity of the language used, and avoidance of double meanings. The proposed questionnaire will comprise only closed questions, and the responses will be measured based on 5 Likert scales, due the fact that it creates the premises for a higher response rate of the respondents.

4. Conclusion and limitations 4.1 Conclusion remarks We are in a world which is in a continuous change since the twenties with the double mutation: everything is traded on world market and the knowledge and the innovation become more and more important in the

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Alexandru‐Ionuț Pohonțu, Camille Baulant and Costache Rusu world of competition. The value of this study resides from the fact that it’s includes both theoretical and practical perspectives. Based on the findings, the study makes an approach of the theoretical model learning by sharing and proposes a research framework to test this model. This article contributes to organizational learning and knowledge sharing theory. Knowledge creation and sharing is already seen as supporters of competitive advantage. Yet, a small part of the theory or empirical research focused on analyzing the relationship between learning and sharing, and more than that small emphasis is placed on learning by sharing. On a theoretical point of views, it has been showed that to increase competitiveness, we must have a diamond analysis (with interaction of 4 differentiations) and not a dual analysis which is now with no effect at all in such a world which was became complex. The concept of learning by sharing allows suggestions on new ways to solve problems. Yet, it does not mean that learning by sharing process will always succeed. Organizations have to work keeping in mind the long run outcomes, the workers must have interest in their job, both of them must adapt to a moving environment. They must every day forecast (in fact making scenarios because real forecast is now impossible) to solve the problem of lack of competitiveness which affect all the firms around the world. The learning by sharing process has three advantages compared to the traditional approaches (binary approaches). Firstly, learning by sharing approach is a dynamic way to increase in the long run the competitiveness of firm and nations. Secondly, the learning by sharing process could be useful and applied in each firm without any money. The workers must just cooperate and be opened to suggestions of other workers. Thirdly, being the most important for the long run dynamic, all the workers will work in a peaceful environment (without short run and unfair competition). All the workers will work with pleasure and they could have more and more satisfaction to go working, so they will work better. Therefore, in addition to contributing to theory, the findings of the study also contribute as an insight for practice. The relationships among knowledge sharing and organizational learning enablers, and the relationship of these processes combined with their outcomes may provide a clue regarding how organizations can promote knowledge sharing culture. By combining the power of organizational learning and knowledge sharing, organization can create procedures, cultures, and structures that allow scanning, evaluating, anticipating and taking concrete actions on unexpected threats or opportunities.

4.2 Limitations and future research The proposed research framework depends on our collective understandings of the underlying psychological process that induce knowledge sharing behavior and organizational learning. Therefore, learning by sharing framework could be used by organizations for developing realistic environment that are conductive for knowledge sharing and organizational learning. The survey instrument meant to be used in order to test if the research framework relies on self‐reported measures, in which the findings are dependent upon employee’s response regarding his attitudes on knowledge sharing and organizational learning. Moreover, conclusions drown in this study are based on a single method – survey. Therefore, it leaves open the possibility for the existence of the common method bias. Future research should be focused on construction of multiple methodologies in order to triangulate the study findings.

Appendix 1: For learning by sharing research framework Dimensions Knowledge self‐efficacy (derived from Spreitzer, 1995)

Individual competitiveness (derived from Mowen, 2000) Need for learning (derived from Mowen, 2000)

Items I am confident in my abilities to provide knowledge that people in my organization considers valuable. I have the required expertise to provide valuable knowledge for my organization. It does not really make any difference whether I share my knowledge with colleagues. Most of other employees can provide more valuable knowledge than I can. I enjoy competition more than others. I feel it is important to outperform others. I feel that winning is extremely important. In enjoy working on new ideas. Information is my most important resource. It is important for me to learn from each of my job experiences.

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Alexandru‐Ionuț Pohonțu, Camille Baulant and Costache Rusu Dimensions Trust (Ramaseshan and Loo, 1998)

Commitment (derived from Ramaseshan and Loo, 1998)

Mutual benefits (derived from Ramaseshan and Loo, 1998)

Organizational competitiveness (derived from Chen, et al., 2011)

Top management support (derived from Tan and Zhao, 2003)

Practices for promoting knowledge sharing and learning (derived from Moorman and Miner, 1998) Knowledge sharing (derived from De Vries, Van den Hoof and De Ridder, 2006)

Organizational learning (derived from Ames and Archer, 1988; Sujan, Weitz and Kumar, 1994)

Items In a relationship, I establish with my competitor, my partner must always be faithful to relationship. In a relationship, I establish with my competitor, internal knowledge and information must be used for any other purpose than for partnership. In a relationship, I establish with my competitor, participants must be willing to share internal information. Believing that my partner will try to take advantage of my firm will seriously hinder the relationship. In a relationship, I establish with my competitor, my partner must be honest and reliable. For a relationship success I establish with my competitor, I must be completely committed. For a relationship success I establish with my competitor, my partner must be committed as much as I am. The relationships I establish with my competitor are very important to my firm. In a relationship I establish with my competitor, the success of the relationship will be higher when my partner is willing to arrange his/her firm’s operations according to the structure of the relationship. In a relationship I establish with my competitor, I determine my responsibilities and commitments according to get goals of the relationships Even though the partner is my competitor, I would not hesitate to get into the relationship if my competitive position would be enhanced. Even though the partner is my competitor, we are open to sharing knowledge and information. Even if I establish a relationship with a competitor, competition with the partner is more important to me. I am willing to get into a relationship only when my partner has resources such as knowledge and information, which I do not have. I get into a relationship with a competitor only if both companies are of similar sizes. I get into a relationship with a competitor only if the firm is smaller than my company. To establish a relationship with my competitor, both companies must have mutual goals and objectives. I am disappointed when my competitors have higher organizational performance. I analyze situations when my competitors gain market share. I have low tolerance for failure of my organization. The value of my organization can be demonstrated when I perform better than my competitors. Competition is an opportunity to show that my organization is much performant than others. Mangers assist and offer required resources to enable employees to share knowledge. Knowledge sharing within the organization is rewarded and recognized by superior. Always, managers supports and encourages employees to share knowledge. Managers encourage regular meetings to share acumulated knowledge. Managers encourage a climate of communication at work. Managers are interested in managing their own learning and development. Within the organization there are practices that promote experiences learning. Failures within the organization are analyzed. Within the organization there are specific mechanisms for sharing the results. Organization tolerates mistakes and encourages learning. The organization is organized regulate meetings with members of the organization. When I learn something new, I share this knowledge with my colleagues. I think it is important that my colleagues know what I am doing. I regularly tell my colleagues what I am doing. When I need certain knowledge, I ask my colleagues about it. I ask my colleagues about their abilities when I need to learn something. When a colleague is good at something, I ask them to teach me how to do it. There are not many new things that I can learn from on the job. To become a good employee there should take place continuously work improving methods. For me a difficult project is a great satisfaction. For me it is very important to learn from experience.

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Enjoyment in helping others (derived from Wasko and Faraj, 2000) Employee expectations (derived from Ardichvli et al., 2003; Teigland and Wasko, 2004)

Rewards system (derived from Davenport and Prusak,1998)

Propensity to innovate (derived from Calantoneet al., 2002)

Organizational performance (derived from Morgan et al.,2003)

Items I spend a lot of time for learning new things at work. Making mistakes is just part of the learning process. Learning about how I can become a better employee has fundamental importance for me. Sometimes I make a great effort for learning. Innovative ideas within the organization represent the outcomes of employees learning. I enjoy sharing my knowledge with colleagues. I enjoy helping colleagues by sharing my knowledge. It feels good to help someone by sharing my knowledge Sharing my knowledge with colleagues is pleasurable. I participate in the community to learn new things. I participate in the community to pass on my own knowledge. I participate in the community to improve my career prospects. I participate in the community because it makes my work easier. I participate in the community to improve my contact with colleagues. I interact with my colleagues in order to learn from them. I acquire new skills and knowledge by working with others in the organization. By sharing my knowledge for specific tasks with my colleagues, colleagues could share workload with me. By sharing my knowledge for specific tasks with my colleagues, I could spend less time on the tasks. Sharing knowledge within the organization is rewarded with financial bonuses or pay increases. Sharing knowledge within the organization is rewarded by getting promoted. Sharing my knowledge with colleagues should be rewarded with an increased job security. Knowledge sharing within the organization is rewarded by a better reputation. Knowledge sharing within the organization is rewarded by recognition of personal contribution. Organization carried out innovation in business activities. The products offered by the organization are constantly developed and replaced. Technological processes are continously improved. The personnel of the organization are encouraged to innovate through research and experimentation. In our organization innovation is perceived as very risky and is handled with resistance. The organization knows its capabilities and adapt them to the market.. Within the organization there is a continually improving process. The organization is well positioned in the future strategy. The organization adapts to customer needs. The organization is experiencing a steady increase in market share compared to the competition.

References Ames, C., & Archer, J. (1988). Achievement goals in the classroom: Students’ learning strategies and motivation processes. Journal of Educational Psychology, 80, 260–267. Ardichvili, A., Page, V., and Wentling, T., 2003, Motivation and barriers to participation in virtual knowledge‐sharing communities of practice, Journal of Knowledge Management (7:1). Brown JS and Duguid P (1991) Organizational learning and communities‐of practice: towards a unified view of working, learning and innovation. Organization Science 2 , 40‐57. Bratianu, C. (2009) “Challenges for knowledge management research”, in: Bratianu, C., Lixandroiu, D., Pop, N. (eds.) Business excellence, Vol.1, pp.52‐56, Infomarket, Brasov. Calantone, R.J., Cavusgil, S.T. and Zhao, Y., 2002, Learning orientation, firm innovation capability, and firm performance, Industrial Marketing Management, Vol. 31 No. 6, pp. 515‐24. Davenport, H., Prusak, L. Working Knowledge: How Organizations Manage What They Know, Boston: Harvard Business School Press, (1998). Davenport, T. and Prusak, L., 1998, Working Knowledge, Harvard Business School Press, Cambridge, MA de Vries, R. E., van den Hooff, B., & de Ridder, J. A. (2006, April). Explaining knowledge sharing the role of team communication styles, job satisfaction, and performance beliefs. Communication Research, 33(2), 115‐135. doi: 10.1177/0093650205285366 Duguid, P., “The Art of Knowing: Social and Tacit Dimensions of Knowledge and the Limits of the Community of Practice”. The Information Society (Taylor & Francis Inc.), (2005).

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Alexandru‐Ionuț Pohonțu, Camille Baulant and Costache Rusu Hutchings, K., Michailova, S., 2004, “Facilitating knowledge sharing in Russian and Chinese subsidiaries: The importance of groups and personal networks”, Journal of Knowledge Management, 8(2). Kankanhalli, A., Tan, B. C. Y., & Wei, K. ‐K., 2005. “Contributing knowledge to electronic knowledge repositories: An empirical investigation”. MIS Quarterly, 29(1). Kim DH (2004) The link between individual and organizational learning. In How Organizations Learn: Managing the Search for Knowledge. (Starkey K, Tempest S and Mckinlay A, Eds), 2nd edn, pp 29‐50, Thomson, London, UK. Kolb DA (1984) Experiential Learning: Experience as the Source of Learning and Development . Prentice‐Hall, Englewood Cliffs, NJ, USA. Koulikov, M., Emerging Problems in Knowledge Sharing and the Three New Ethics of Knowledge Transfer, Knowledge Management & E‐Learning: An International Journal, 3(2), (2011). Lohman, M. C. (2005). A survey of factors influencing the engagement of two professional groups in informal workplace learning activities. Human Resource Development Quarterly, 16(4), 501‐527. March, J.G.; Olsen, J.P.. (1975). "The uncertainty of the past: organizational learning under ambiguity". European Journal of Political Research 3: 147–171. Moorman, Christine and Anne S. Miner, 1998, Organizational Improvisation and Organizational Memory, Academy of Management Review, 698‐723. Morgan D, Grant KA, Gage HD, Mach RH, Kaplan JR, Prioleau O, Nader SH, Buchheimer N, Ehrenkaufer RL, Nader MA., 2002, Social dominance in monkeys: dopamine D2 receptors and cocaine self‐administration, Nat Neurosci, 5:169– 174. Mowen, J.C., 2000, The 3M Model of Motivation and Personality: Theory and Empirical Applications to Consumer Behavior, Kluwer Academic Press. Nonaka, I., and Takeuchi, H., 1995, “The knowledge‐creating company: How Japanese companies create the dynamics of innovation”. Oxford University Press, New York Pohonțu, A.,I., Baulant C., Rusu C., (2012), Developing a learning framework: the systemic “learning by sharing” diamond, ”Quality‐Access to Success”, vol.13, S5. Polanyi, M., 1966, The Tacit Dimension, Routledge & Kegan, London Porter, M., E., Competitive advantage of Nations, Harvard Business Review (1990). Porter, M., E., Competitive Advantage: Creating and Sustaining Performances (1985). Rajagopalan, N., Rasheed, A.M.A. and Datta, D.K. (1993), “Strategic decision processes: critical review and future directions”, Journal of Management, Vol. 19 No. 2, pp. 349‐84 Ramaseshan, B. & Loo, P.C. (1998). Factors affecting a partner’s perceived effectiveness of a strategic business alliance: some Singaporean evidence. International Business Review, 7: 443‐458 Sharif, M., Zakaria, N., Ching, L. and Fung, L. (2005). Facilitating Knowledge Sharing Through Lessons Learned System. Journal of Knowledge Management Practice , 6, (Electronic). Spender, J.‐C., Organizational learning and knowledge management: Whence and whither? Management Learning, vol. 39 no. 2, (2008). Spreitzer, Gretchen, M., (1995), Psychological empowerment in the workplace: Dimensions, measurement, and .validation, Academy of Management Journal, 38(5): 1442‐1465 Stewart, T.A. (2002) The Wealth of Knowledge: Intellectual Capital and the Twenty‐First Century Organization, Nicholas Brearley Publishing, Clerkenwell, London, ISBN: 1 85788 287 3. Sujan, Harish, Barton A. Weitz, and Nirmalya Kumar (1994), "Learning Orientation, Working Smart, and Effective Selling," Journal of Marketing, 58 (July), 39‐52. Sujan, Harish, Barton A. Weitz, and Nirmalya Kumar, 1994, Learning Orientation, Working Smart, and Effective Selling, Journal of Marketing, 58 , 39‐52. Tan, H. H., & Zhao, B., 2003, Individual and perceived contextual level antecedents to individual technical information inquiry in organizations, Journal of Psychology, 137(6), 597‐621. Teigland, R. and Wasko, M. (2003). Integrating knowledge through information trading: Examining the impact of boundary spanning communication on individual performance. Decision Sciences, 34, 261‐286. Thierry Nabeth, Liana Razmerita, Albert A. Angehrn, Claudia Roda (2005); InCA: a Cognitive Multi‐Agents Architecture for Designing Intelligent & Adaptive Learning Systems; A special issue of ComSIS journal, Vol. 3, No. 2., December 200 Tiwana, A., The Knowledge Management Toolkit: Practical Techniques for Building a Knowledge Management System, Prentice Hall, Upper Saddle River, NJ, (2000). Wasko, M., Faraj, S., Teigland, R., 2004, Collective Action and Knowledge Contribution in Electronic Networks of Practice, Journal of the Association of Information Systems 5 (11) Wasko, M.M., Faraj, S., 2000, It is what one does: why people participate and help others in electronic communities of practice, Journal of Strategic Information Systems, Vol. 9 No. 2, pp. 155‐73. William G. Zikmund (2003), Business Research Methods, 7E Xiao‐Ping Chen, Xiaofei Xie, Shiqing Chang, 2011, Cooperative and Competitive Orientation among Chinese People: Scale Development and Validation, Management and Organization Review, Volume 7, Issue 2, pages 353–379. Yu‐Chung Hung, and Ya‐Hsueh Chuang, 2009, “Factors Affecting Knowledge Sharing Behavior: A Content Analysis of Empirical Findings,” International Conference of Pacific Rim Management, Jul. 30‐Aug. 1, 2009, San Francisco, USA.

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A Leadership Framework for Organizational Knowledge Sharing Hong Quach Department of Engineering Management and Systems Engineering, The George Washington University, USA hquach@gwmail.gwu.edu Abstract: Many organizations have discussed the need for better organizational knowledge sharing. However, a framework for organizational knowledge sharing from a leadership and management perspective with emphasis on communication effectiveness and organizational trust has not been demonstrated. This paper is an excerpt of the research study conducted at the George Washington University. It provides research findings that help leaders integrate effective communication into everything they do to achieve organizational performance excellence. Communication has long been recognized as the most important element in leadership. It is also one of the most difficult challenges in any business. To get the right information to the right person at the right time for effective knowledge sharing, leaders need to be cognizant of the impacts of effective communication. Insights such as traits of leadership communication, timeliness for source of information, channel of communication to use for sharing knowledge are part of a framework created from the results of this research that can assist leaders in driving organizational results, build trust between leaders and followers, and create a successful enterprise. A focus on how organizations can learn, collaborate, share knowledge and innovate using the four pillars of knowledge management: leadership/management, organization, technology, and learning will be discussed. Understanding the importance of organizational trust and communication effectiveness will help leaders in enhancing organizational knowledge sharing. Keywords: knowledge management, organizational knowledge sharing, communication effectiveness, organizational trust, leadership and management

1. Introduction Organizational knowledge sharing has been discussed extensively in many organizations. Every day, large amount of data are being generated, information are being gathered and knowledge are being transferred in all types of organizations, academia, industry, and government. However, there was no leadership framework for organizational knowledge sharing created to address the issue of knowledge sharing with emphasis on organizational trust and communication effectiveness. This paper presents an excerpt of the doctoral dissertation research conducted at the George Washington University with focus of this paper related to knowledge management, leadership and management, and effective sharing of knowledge using a new framework. While details of the research methodology for the measurement of organizational trust and communication effectiveness are outside the scope of this paper and require another paper to describe the analyses, overview of the methodology and highlights of organizational trust and communication effectiveness are presented to provide insights that lead to the creation of a new framework for effective organizational knowledge sharing. This paper discusses an important topic in leadership and management that is relevant to the themes of the international conference on intellectual capital, knowledge management, and organizational learning.

2. Knowledge management and organizational knowledge sharing In reviewing the knowledge management literature, Quach (2004) found that researchers have discussed knowledge management in many ways. What is knowledge management and what are the issues in knowledge sharing? According to Wiig (1977), “Knowledge management is the systematic, explicit, and deliberate building, renewal, and application of knowledge to maximize an enterprise’s knowledge‐related effectiveness and returns from its knowledge assets.” According to Davenport and Prusak (2000, 1), “Knowledge is neither data nor information, though it is related to both, and the differences between these terms are often a matter of degree.” According to Stankosky (2005, 5), who is a pioneer in the field of knowledge management, “All the knowledge management elements are grouped into four pillars: leadership/management, organization, technology, and

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Hong Quach learning... Leadership/management deals with the environmental, strategic, and enterprise‐level decision making, processes… Organization deals with the operational aspects of knowledge assets... Learning deals with organizational behavioral aspects and social engineering. The learning pillar focuses on the principles and practices to ensure that individuals collaborate and share knowledge to the maximum… Technology deals with the various information technologies peculiar to supporting and/or enabling knowledge strategies and operations.” Knowledge sharing is an issue that many organizations face today. Some of the reasons identified by the knowledge management working group (KMWG, 2001) on why people don’t share knowledge are: (a) Others may not know with whom to share or how to share what they know. (b) It may be that sharing seems too difficult or too time consuming. (c) People resist sharing and using knowledge especially in environments where trust is low. Additionally, in discussing how organizational knowledge creation takes place on a global scale, Nonaka and Takeuchi (1995, 197) presented two cases studies on how a product surmounted many obstacles and successfully penetrated the global market. Based on the case studies Nonaka and Takeuchi presented in their book, they indicated that building trust requires two‐way, face‐to‐face dialogue. However, their book was written more than ten years ago when many of the current emerging technologies such as videoconference, teleconference, and web conference were not very mature. Today, people can communicate over the Internet and work in virtual workplace interactions; therefore, communication variable such as channels of communication used needs to be examined further. According to Waters (2012), who is a pioneer in the field of engineering management, in a webinar presented to the Society of Engineering and Management Systems, Institute of Industrial Engineers, he said “The search for knowledge and for understanding of the world around us, and for the creation of new technology to enhance the living standards and improve the human condition, are the fundamental goals of research in engineering and applied science.” The issues listed above as we thrive to search for knowledge or sharing knowledge lead to the need for additional research on organizational knowledge sharing and effective use of knowledge. This research study recommends that for leaders and managers who want to effectively leading their organizations, they need to consider and practice the key elements of organizational trust and communication effectiveness. The following sections provide some insights into the research methodology, organizational trust and communication effectiveness.

3. Overview of the methodology The study uses a quantitative analysis approach. Interviews and survey are used to collect data. The research instrument for the study is validated using statistical methods. The subjects and organizations participated in this study are from organizations in the government, industries and academia. Approval from the George Washington University Committee on Human Research Institutional Review Board (IRB) is obtained for the research plan to initiate the data collection process. The instruments included an organizational trust index (OTI) instrument, a communication effectiveness index (CEI) instrument, and a workplace profile index (WPI) instrument. The data analyses process consists of data reduction, data reporting, and statistical testing of the hypotheses. The results and findings of the research are used to draw conclusions and recommendations. The research goal is to determine if there is a positive relationship between organizational trust and internal organizational communication effectiveness. From this result, new frameworks are created to demonstrate the importance of organizational trust and communication effectiveness. Organizational knowledge sharing describes in this paper is one of the new frameworks that can help leaders in enhancing effective knowledge sharing.

4. Research plan The research plan is to measure trust levels and communication effectiveness levels in workplaces and determine if there is a relationship between them. Measured variables include trustworthiness dimensions and communication dimensions. The Shockley‐Zalabak Organizational Trust Index (OTI) (Shockley‐Zalabak et al., 1999) is used to measure organizational trust. Organizational trust includes competence, openness and

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Hong Quach honesty, concern for employees, reliability, identification. A new proposed Communication Effectiveness Index (CEI) is created to measure communication effectiveness. Communication effectiveness includes the individual (leadership competency behaviors), technology (channels of communication used), and organization (communication climate and timeliness of communication). Definitions of organizational trust and communication effectiveness are described in the following two sections. The CEI includes elements described in the four pillars of knowledge management.

5. Organizational trust What is Organizational Trust? The following definition of organizational trust is assumed relevant for this study: “The organization’s willingness, based upon its culture and communication behaviors in relationships and transactions, to be appropriately vulnerable based on the belief that another individual, group, or organization is competent, open and honest, concerned, reliable, and identified with common goals, norms and values.” (Shockley‐Zalabak et al., 1999) Shockley‐Zalabak et al. (1999) measured the organizational trust across cultures and showed the implications for organizational effectiveness. The organizational trust index addressed five dimensions: competence, openness and honesty, concern for employees, reliability, and identification. According to Shockley‐Zalabak, “Trust matters! Trust is related to profits, innovation, successful international business, organizational survival and a variety of crucial worker perceptions and behaviors. … Regardless of whether organizations engage in international business or not, most require enhanced network of trust.”

6. Communication effectiveness What is Organizational Communication? The following definition of organizational communication is assumed relevant for this study: “Process through which organizations are created and in turn create and shape events. The process can be understood as a combination of process, people, messages, meaning, and purpose.” (Shockley‐Zalabak, 2002, 28) Researchers have discussed the importance of communication effectiveness in the context of leading change (Kotter, 1996), great game of business (Stack, 1992), great leader communications (Baldoni, 2003), communicating for productivity (D’Aprix, 1982), and in many other contexts. Quach created a new instrument to measure communication effectiveness index by identify the individual (leadership competency behaviors), technology (channels of communication used), and organization (communication climate and timeliness of communication). The following definitions for the four dimensions of the communication effectiveness model were assumed relevant for this study:

Communication Climate Dimension: “The communication climate in any organization is a key determinant of its effectiveness. Organizations with supportive environments encourage worker participation, free and open exchange of information, and constructive conflict resolution. In organizations with defensive climates, employees keep things to themselves, make only guarded statements, and suffer from reduced morale.” (Costigan and Schmeidler, 2004)

Leadership Communication Competency Dimension: “Of importance for leaders, <psychological> type reveals that learning to access and appropriately express different mental competencies is crucial to building relationships, communicating appropriately, and promoting synergy among members of a team.” (Pearman, 1999) Leader communication competency behaviors are speaking effectively, listening to others, fostering open communication, preparing written communication, delivering presentations, informing appropriately, confronting effectively.

Communication Timeliness Dimension: “Timeliness is important. There may be an optimum time to disseminate information. It is possible to communicate too early as well as too late.” (Downs, Linkugel, and Berg, 1977)

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Channel of Communication Dimension: “Communication channels and networks both are crucial determinants of the effectiveness of an organization’s systems… These channels are what allow the organizational systems to become a system in the first place.” (Baird, 1977)

7. Communication channels to use for sharing knowledge Literature review showed that “In all organizations, networks and channels are the methods used for exchanging messages, information, meaning, and connecting. Networks represent the regularized communication interactions. Channels are the sanctioned means of communicating.” (Harris, 2002, 244) In this research study, several choices of communication channels to use for sharing knowledge were presented. The choices were based on Baird rank‐order communication channels that were developed more than 3 decades ago. Newer technology for communication such as email, web‐based communication choices were added to the questionnaire and older method for communication such as telegram was removed from the list of questions. Baird noted that “Given the effects of the channels chosen for communication, it seems worthwhile to study the choices made by individuals in modern organizations.” (Baird, 1977, 261) Table 1 below shows the comparison of communication channels used by modern organizations today and by organizations in the past. Table 1: Comparison of communication channels used today and 36 years ago

Communication Channels

Mean Score

Modern Organization Rank Order (this study)

Baird Rank Order Year 1977

4.08

N/A

Face‐to‐face communication

3.31

Telephone, teleconference, voicemail

2.92

Written memo, letters, formal documents

2.72

Rumor through an intermediary, grapevine, water cooler talk

2.47

Web‐based communication, remote screen sharing, instant message, text message, chat

2.36

N/A

Picture‐phone, videoconference, webcast

2.22

Direct intermediary (e.g., use messenger to deliver message)

1.85

Telegram (not included in this study)

No score

N/A

The results of the extent of use of a communication channel for sharing knowledge obtained from this research study indicated that email was used the most in sharing knowledge while direct intermediary was used the least. Given the current modern organizations are well trained in advanced technologies and the many communication devices that are available for use, email is now easier to access than decades ago. The use of messengers to deliver communication messages were used least in current modern organizations because organizations can easily send email messages which can be reached by the receivers quickly. While many organizations are working in a virtual workplace setting, face‐to‐face communication is still the preferred method of communicating after email. Telephone is the third choice and written memos, letters is the fourth choice of communication when sharing knowledge.

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8. Leadership framework for organizational knowledge sharing How can we share knowledge more effectively across the organization? In analyzing organizational trust and communication effectiveness, Quach created a new framework for organizational knowledge sharing as shown in Figure 1. This framework used the transfer elements to achieve the desired results of sharing knowledge at organizational level. To use this framework, leaders and managers need to understand the importance that trust builds credibility and complete trust is granted only after people get to know the person they want to share knowledge with. This research study has used organizational behaviors to measure trust based on several dimensions, being competence, open and honest, concern, reliable, and identified with organization’s culture. For effective organizational knowledge sharing, the transfer function which is a mathematical representation of the relationship between input and output is applied to the framework. As shown in Figure 1, the inputs are tacit knowledge and explicit knowledge and outputs are usable knowledge. To make the shared knowledge usable, the inputs must be usable as well. To achieve this result, filter can be used. In this case, the filter is the sum of organizational trust and communication effectiveness variables, which is used to modify the input signals (tacit and explicit knowledge) to a desired state of output signals (usable knowledge). This transformation using the transfer function ensures that the output shared knowledge is credible, trustworthy, and usable.

Figure 1: Diagram of a framework for organizational knowledge sharing

9. Conclusions The key elements of organizational trust and communication effectiveness were presented to demonstrate their importance in the new leadership framework for organizational knowledge sharing. With big data being generated every day, understanding how to transform data into information, and then information into knowledge, and apply the filter for organizational trust and communication effectiveness, will yield usable knowledge as we share knowledge, capture intellectual capitals, manage knowledge, and move toward a learning organization. The respondents participated in the research study worked in academia, government and industry. Nearly half of the respondents had roles in management, had doctoral degree or beyond, had master’s or specialist’s degree, and worked in organizations that had more than 5000 employees. Prior researchers have long recognized that organizational trust is complex and difficult to study, communicating is one of the most difficult challenges in any business, and one key to organizational excellence is effective communication. As leaders work to enhance their organizational knowledge sharing, understanding the key elements of organizational trust and communication effectiveness and applying the knowledge

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Hong Quach management elements described in this paper can help leaders and their organizations in sharing knowledge effectively.

Acknowledgments I am profoundly grateful to Dr. Michael A. Stankosky and Dr. Robert C. Waters in the Department of Engineering Management and Systems Engineering of the George Washington University for helping me in my journey for achieving my Doctor of Science. Their advice and support enabled me to work on my doctoral dissertation research and be able to share the doctoral research knowledge today.

References Baird Jr., John E. (1977) The Dynamics of Organizational Communication. Harper & Row, Publishers, Inc., New York, NY. Baldoni, John. (2003) Great Communication Secrets of Great Leaders. McGraw‐Hill, New York, NY. Costigan, James I. and Schmeidler, Martha A. (2004) Communication Climate Inventory, Available from Internet: <http://www.cps.usfca.edu/ob/studenthandbooks/321handbook/climate.htm> (cited 2008‐1‐9). <http://www.milligan.edu/speech/comm341/communicationclimage/communicationclimateinventory.htm> (cited 2012‐3‐15). D’Aprix, Roger. (1982) Communicating for Productivity. Harper & Row, Publishers, New York. Davenport, Thomas H. and Prusak, Lawrence. (2000) Working Knowledge, How Organizations Manage What They Know, Harvard Business School Press, Boston, MA. Downs, Cal W., Wil A. Linkugel, and David M. Berg. (1977) The Organizational Communicator, Harper & Row, Publishers, Inc. New York, NY. Harris, Thomas E. (2002) Applied Organizational Communication: Principles and Pragmatics for Future Practice. Mahwah, NJ: Lawrence Erlbaum Associates, Inc., Publishers, p. 244. KMWG, (2001) “Managing Knowledge at Work: An Overview of Knowledge Management”, Federal CIO Council Knowledge Management Working Group, August, pp. 9‐12. Kotter, John P. (1996) Leading Change. Harvard Business School Press, Boston, MA. Nonaka, Ikujiro and Hirotaka Takeuchi. (1995) The Knowledge Creating Company, Oxford University Press, New York, NY. Pearman, Roger R. (1999) Enhancing Leadership Effectiveness. Gainesville, FL: Center for Applications of Psychological Type, Inc., pp. 2, 51. Quach, Hong. (2004) “Trust as a Key Element for Knowledge Management: A Literature Review”, Proceedings of the 2004 American Society for Engineering Management (ASEM) National Conference, Alexandria, VA, October 20‐23, pp. 495‐ 501. Shockley‐Zalabak, Pamela, Kathleen Ellis and Ruggero Cesaria. (1999) Measuring Organizational Trust, Trust and Distrust Across Cultures: The Organizational Trust Index, IABC Research Foundation. Shockley‐Zalabak, Pamela. (2002) Fundamentals of Organizational Communication: Knowledge, Sensitivity, Skills, Fifth Edition, Allyn & Bacon, A Pearson Education Company, Boston, MA, pp. 28. Stack, Jack. (1992) The Great Game of Business. Doubleday, New York, NY. Stankosky, Michael A. (2005) Creating the Discipline of Knowledge Management, The Latest in University Research, Elsevier Inc., pp. 5‐6. Wigg, Karl. (1977) “Knowledge Management: Where Did It Come From and Where Will It Go?”, Expert Systems With Applications – Pergamon Press/Elsevier 14 (Fall). Waters, Robert C. (2012) Research in an Engineering Management Career, presented to Society of Engineering and Management Systems, Institute of Industrial Engineers Webinar, Washington, DC, April 5.

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Project Context and its Effect on Individual Competencies and Project Team Performance Mikhail Rozhkov, Benny Cheung and Eric Tsui Knowledge Management and Innovation Research Centre, Department of Industrial and Systems Engineering, The Hong Kong Polytechnic University, Hong Kong mnrozhkov@gmail.com benny.cheung@polyu.edu.hk eric.tsui@polyu.edu.hk Abstract: This paper presents a study of how a project context affects the competencies and performance of innovation project teams. Project context is understood as a work context in project environment. It is a set of conditions that guides team members to act. It is described in terms of organizational culture, organizational climate, team and manager characteristics, etc. If a project context is appropriate and well matched with the employees, it leads to a higher individual and team performance. The paper aims to discuss the theoretical and methodological issues of studying the effect of project context factors on team performance in innovation projects. It provides a conceptual model of project context and its relationship to individual competencies. The methodology to study the proposed relationship is also discussed. Competencies are considered as individual characteristics (including skills and knowledge) that are manifested in employee behavior and causally related to and can predict high level of individual performance. Individual competencies provide a basis for high level of team performance. The understanding of relationships between individual competencies and project context factors provides an important means to improve the management practices. The study not only provides methods for optimal matching between work context and individual competencies but also provides tools for the improvement of team performance by facilitating the required competencies in specific work context. A framework of a conceptual model is proposed which attempts to predict the performance and the competencies that will develop in the specific work contexts. Applications of the research results will have significant impact on business practices and help to develop a comprehensive view of organization management practices and their effect on team performance. Applications include decision making about forming project teams that are more likely to achieve high levels of performance as well as improving management practices and workplace environment. Keywords: competency, performance, innovation project, team, project context, individual competencies

1. Introduction The problems regarding project team performance are crucial for many organizations. Brown and Hyer (2010) indicate three major issues of projects in today’s “hypercompetitive” business environment: short product life cycles, global business and evolving technologies. Kerzner and Kerzner (2006), Busch, Nash, and Bell (2011),, Cerinšek, Petersen and Heikura (2011), Westergren (2011), Benali and Burlat (2012) state that projects become more complex, network‐based and knowledge intensive due to globalized economies and high business virtualization. Fast changing business environment exhibits new requirements for project team members knowledge, skills and attitudes. Managers should pay attention on human factors to achieve high performance of the team. Employees, their knowledge and competencies remain the most important drivers and factors for project’s success. This study aims to investigate the potential benefits of considering the relationships between workplace environment and employee characteristics to achieve improvements in individual professional performance, work climate and satisfaction. The research is focused on innovation project teams in knowledge‐intensive and innovation‐driven industries (manufacturing of materials, capital goods, automobiles, etc.). This paper presents a study of how a project context affects the competencies and performance of innovation project teams. It aims to discuss theoretical and methodological issues of studying the effect of project context factors on team performance in innovation projects. It provides a conceptual model of project context and its relationship to individual competencies. The methodology to study the proposed relationship is also discussed.

2. Literature review 2.1 Project team performance Boyatzis (1982) defines effective (high) performance as a rate of achievement of output objectives or as appropriate execution of a job (task). Output objectives are specific results or desired outcomes required by

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Mikhail Rozhkov, Benny Cheung and Eric Tsui the task. This is direct or indirect contribution to product or service of the organization. To achieve these objectives, people act in a specific way that is usually consistent with technological processes, organizational policies, procedures and conditions of the workplace environment. It is proposed that only specific actions lead the specific results in a consistent manner. As a result, random actions cannot be considered as a component of the effective performance. Turner (2007) proposed to distinguish the terms project success and project performance. A successful project should be fulfilled (1) within the assigned time period and the budget, at the appropriate performance or specification level; (2) with outcomes accepted by the customer and/or user; (3) within a minimum or permissible scope changes; (4) without disturbing the main work flow of the organization and without changing the corporate culture. Project success can be measured only after project completion whereas project performance can be for any current state of the project. Project team performance is considered as a rate of achievement of output objectives or as appropriate execution of a project tasks. The high performance project team is more likely to achieve success of the project goals. Low performance of the project team is more likely to lead to project failure. Common attributes of project failure are poor morale, low motivation, poor human relationships and low productivity, lack of employee commitment and/or functional commitment, delays in problem solving, and conflicts, etc (Kerzner & Kerzner, 2006). Project team performance deals with direct behavior of team members during the handling of project tasks. Hence, it is important to consider team members behavior and related factors to be able to understand, measure and manage project team performance.

2.2 Individual competencies An attribute of the project team performance is an appropriate execution of a job in a specific way. In according to Boyatzis (1982, p.12), “certain characteristics or abilities of the person enable him or her to demonstrate the appropriate specific actions” can be called competencies. A number of reviewed studies in competency management show differences in terms and definitions. For the purposes of this study the term “competency” is used while the terms “competency” and “competence” are considered as synonyms if they are applied to describe human characteristics or behavior related to the high level of job performance. Different approaches to defining competencies and their characteristics are listed in the following sections below. 2.2.1 Traits and abilities In the first approach, probably the most popular one, competencies are considered as human characteristics and abilities. Crowl et al. (2007) consider competence as ability to perform tasks according to desired outcomes. The “ability” here means that an employee has appropriate qualifications or training, possesses skills, physical and mental capabilities, knowledge, understanding, behavior and attitude. This broad definition covers various human qualities which provide the potential to reach the expected performance level. Crowl et al. (2007), Marques, Zacarias, and Tribolet (2010) use term “ability” as synonym to the capability held by humans or capacity to perform a task. Sebt, Shahhosseini, and Rezaei (2010) use the term “competency” to generalize clusters of attitudes, knowledge, skills and other features that affect a major part of job related tasks, roles and responsibilities, correlate them with the performance level. They can be measured against standards, improved and detailed. This approach adopts competencies to solve the human resources assignment problem in project management. This approach proposes that competencies have a potential for expected outcomes achievement. However, a potential ability to fulfill task is required but not effective characteristic of the competencies. Heneman and Ledford (1998) define competencies as demonstrable characteristics of a person including knowledge, skills, and behavior that enable performance. As a result, along with Cooper (2000), they require that competencies should only be demonstrable, i.e. explicitly shown and detectable.

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Mikhail Rozhkov, Benny Cheung and Eric Tsui 2.2.2 Behavior Heneman and Ledford (1998) as well as Cooper (2000) underline the importance of the demonstrable characteristics exemplified in human behavior to describe competencies. However, some authors consider competencies in terms of human behavior. Woodruffe (1993) considers competency as a set of “behavior patterns” that people use in a job to perform according to expectations. As a result, he excludes elements of work performance such as technical skills, knowledge and abilities from the competency definition. Kurz and Bartram (2002; 2008) also follow similar approach and suggest that competencies are not what people possess, but what people exhibit in their behavior. 2.2.3 Underlying characteristics Klemp’s (cited in Boyatzis, 1982), Boyatzis (1982), Spencer&Spencer (1993) pay attention to some underlying characteristic of a person as a basis of competencies. Klemp (cited in Boyatzis, 1982, p. 21) defines a job competency as “an underlying characteristic of a person which results in effective and/or superior performance in a job”. The underlying characteristics are generic and include motives, traits, skills, knowledge, etc. It means that these characteristics can be subconscious to the person, or be appeared explicitly and variety in some behaviour (actions). As a result, people specific behavior (performing an act) is under the influence of a set of the characteristics and can produce different results (outcomes) at each cycle of repeated action. Boyatzis (1982) states that competency concept reflects people capability (what they can do) and not necessary what they really do, regardless of context. He proposed causal relationships between competencies and job performance. By knowing a set of competencies of a person, we can predict specific actions. Similar to Boyatzis (1982), Spencer and Spencer (1993) consider competencies as underlying characteristics of an individual. However, they claim that these characteristics have causal impacts on criterion‐referenced effective and/or superior performance in a job (task). Superior performance by Spencer (1997) as follows:

top 10‐14 percent of performers in a job,

with known economic value added by performance deviation,

explicit approach to benchmark and developing.

Spencer (1997, p.6) states that competencies able to distinguish “superior performance from average performance, or effective from ineffective performance, at a statistical level of significance”. Competency characteristics include a person’s traits (motives, self‐concepts, attitudes, values, or occupational preference), declarative knowledge (content knowledge) and procedural skills (cognitive or behavioral). The three major approaches considered above are not mutually exclusive; they overlap and complement each other. In authors’ opinion, the approaches from Boytzis (1982) and Spencer and Spencer (1993) are more appropriate for the purposes of the present study. As a result, the further discussion about competencies will be based on their approaches. 2.2.4 Context characteristics Boyatzis and Fogg (cited in Ennis, 2008) highlighted internal and external constraints, environmental and relationship factors significant to the job. Funder (cited in Svyantek and Bott, 2004) states that the explanation of people behavior requires three types of factors to be taken into account which include personal, contextual and behavioral.) Erpenbeck and Sauter (cited in Simon, 2010) outline the influence of context factors such as rules, norms and attitudes on personal abilities to act. This approach proposes changeability and context‐dependability of the competencies. It raises methodological problems for assessing and applying competency models to different jobs, in different organizations at different time. Context‐dependability proposes that people in different organizations show different competencies.

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Mikhail Rozhkov, Benny Cheung and Eric Tsui Employees in organization are not isolated but interact with other employees, managers, the workplace, organizational rules and procedures. Organization mission, strategy, policies and procedures reflect organization as a system. All these factors form the organizational environment or context and influence employee’s motivation, behavior and performance. Competencies are considered as individual characteristics (including skills and knowledge) that are manifested in behavior of employee and causally related to and can predict high level of individual performance. The competency model is set of related competencies that describe successful performance for a specific job(task) (Boyatzis, 1982; Bozkurt, 2009). A competency‐based model of effective job performance should take into account that specific actions should be consistent (or fit) with policies and organizational environment. The more all components are consistent, the higher likelihood of the effective performance (Boyatzis, 1982). Hence, individual competencies that are consistent with context factors lead to effective performance.

2.3 Project context Project context is a set of conditions in which project team acts. It is described in terms of organizational culture, organizational climate, team, manager characteristics, etc. A project context that is appropriate and well matched with the employee’s characteristics leads to a higher individual and team performance. 2.3.1 Organizational culture Corporate (organizational) culture is “a pattern of shared basic assumptions invented, discovered, or developed by a given group” (Schein, 2010, p.18). It has worked sufficiently and can be taught to new members as the correct way to perceive, think and act. Culture integrates patterns of human behavior including ways of thinking, speaking, acting (Deal and Kennedy, 1982). The ability to affect human behavior makes organizational culture the important factor in effective performance. Kotter and Heskett (1992) assumed that culture strength is the most predictive factor of organizational performance. In strong cultures, managers have shared values, leading to goal alignment, strong motivation and self‐controlling behavior (Sparrow, 2001). However some researchers found that organizations characterized by a ‘strong culture’ are inclined to have larger adherence to procedures and behavioral homogeneity and become less effective in dynamic environments (Chatman et al., 2012). Sørensen (cited in Chatman et al., 2012) states that a strong culture supports consistent financial performance in a stable business environment and becomes unreliable in dynamic environments. Chatman, et al. (2012) indicates that the link between organizational culture and performance is not yet well understood and research results fail to recognize the multidimensional nature of organizational culture. One of the latest research studies on culture‐performance link was conducted by Chatman et al. (2012). They claimed that a strong culture can positively influence organizational performance and financial results in dynamic environments if it is “characterized by high consensus about a comprehensive set of norms and that intensely emphasizes a norm of adaptability” (Chatman et al., 2012, p.30). Organizational culture has an impact on a person mental, emotional and attitudinal states that affect effective performance (Sparrow, 2001). Beyer, Hannah and Milton (2000), Cartwright, Cooper and Earley (2001) showed that the link between culture and effective performance is provided via the following mechanisms:

shared patterns that follow employees interpretations and ways to behave,

from an emotional sense of involvement and commitment to organizational values to job commitment and involvement,

learned responses and understanding for problems and actions,

control systems.

Culture is important factor of innovation success. Michela and Burke (2000) stated that up to 25 % of the content in some books on innovations, focus on culture and culture management. Burningham and West (cited in Michela and Burke, 2000) distinguished the following culture‐related factors of group innovativeness:

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Mikhail Rozhkov, Benny Cheung and Eric Tsui support for innovation (as shared value of usefulness and importance of innovation), vision and mission (clear, attainable and shared). As a result, organizational culture is a significant factor that affects individual competencies by enabling opportunities, motivating or restricting his behavior. Culture has significant influence on innovation success. 2.3.2 Organizational climate Some authors (Ashkanasy, 2000; Dennison, 1990) emphasize the link between organizational culture and organizational climate. Schein (2010) considers climate as the manifestation of culture and defines it as “the feeling that is conveyed in a group by the physical layout and the way in which members of the organization interact with each other, with customers, or other outsiders” (Schein, 2010, p.15). Stringer (2002) stated that “the climate itself proved more powerful than previously ‘acquired’ behavior tendencies, and it was able to change the observed behavior patterns of the group members” (Stringer, 2002). Organizational climate “is a relatively enduring quality of the internal environment of an organization that (a) is experienced by its members, (b) influences their behavior, and (c) can be described in terms of the values of a particular set of characteristics (or attributes) of the organization” (Tagiuri cited in Stringer, 2002, p.27). It can combine both objective and subjective characteristics of the work environment that can be perceived or experienced by employees, can be shared by group members (as consensus) and understood in terms of shared meanings (Stringer, 2002). The following assumptions underlying the concept of organizational climate (Stringer, 2002):

people’s feelings about their work has a powerful influence on how they work,

climate determines the performance of an organization,

climate is directly linked to motivation.

Some organizational climate characteristic may affect innovation process. Burningham and West (cited in Michela and Burke, 2000) distinguished the following climate‐related factors of group innovativeness: participative safety (in giving ideas and suggestions in the innovation process) and climate for excellence (lead to innovation through seeking of new ideas and approaches). Along with positive influence on innovation, some climate states may resist and inhibit the innovation process. Lack of cooperation and the real value of innovation and creativity are often mentioned as factors resisting innovation (Michela and Burke, 2000). As can be seen from the literature review above, there are multiple relationships between individual competencies and work context factors. This study is an initial attempt to understand these relationships. This paper will discuss the conceptual model and methods proposed to be used in the study.

3. Conceptual model The proposed conceptual model represents relationships between project context factors and employee’s individual competencies and performance (Figure 1). Project context operationalized through culture, climate, manager and team related factors.

Figure 1: Project context factors and their effect on individual competencies

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3.1 Employee individual performance Team performance is an essential factor of project success. It is directly affected by the individual performances of the team members, which has only one major determinant: employee competencies. It is proposed that individual competencies causally determine performance level of an employee.

3.2 Employee individual competencies Individual competencies provide required knowledge, skills, experience and behavior to handle the job task. Task (job) is described in terms of “responsibilities that the job occupant is expected to perform, decisions that he or she is expected to make, and outcomes that he or she is expected to produce” (Boyatzis, 1982, p.16). These are functional requirements that should be fulfilled to contribute to team performance. On other hand, individual competencies manifested in employee behavior are driven by motivation. Because of the complex nature of motivation, it is operationalized through employee satisfaction by job/task, team, manager and organization. The conceptual model makes the following assumptions:

satisfaction provides the bottom level for motivation,

an employee with high satisfaction is likely motivated to demonstrate the needed competencies,

an employee with low satisfaction is likely not motivated to demonstrate needed competencies.

Employee motivation is affected by project context factors.

3.3 Project context 3.3.1 Climate The organization climate reflects employees’ feelings about the organizational environment (Figure 2). It summarizes personal preferences of team members around the climate dimensions including structure, responsibility, support, standards, recognition and commitment.

Figure 2: Environment determinants and characteristics 3.3.2 Culture Culture in general has the same determinants as the organizational climate, but it describes how people see and understand other employees, their behavior, values and attitudes. The culture is studied in three dimensions including content, consensus and intensity. The content (C) dimension describes what values, beliefs and behavior are shared in an organization and project team. The consensus (Cons) dimension describes people agreement about organizational values. The intensity (I) dimension describes the strength of how cultural values and beliefs govern employees’ behavior. 3.3.3 Manager The manager contributes to the achievement of project goals through “planning, coordination, supervision, and decision making regarding the investment and use of corporate human resources” (Boyatzis, 1982, p.16). His behavior is based on managerial competencies (practices), values and beliefs that affect climate and

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Mikhail Rozhkov, Benny Cheung and Eric Tsui culture in the team. Managerial competencies define specific actions and patterns of manager behavior in major management activities including planning, organizing, controlling, motivating and coordinating. 3.3.4 Team Team members influence thoughts and feelings of each other by interacting and communicating during the work process and at leisure time. They may rise or suppress a person motivation to behave in a specific way. It is proposed that team values and team competencies are important factors that affect a person behavior.

4. Methods The study of relationships between employee competencies and project context factors requires to consider many inter‐related variables. The relationships between these variables are not clear in strength and direction. Moreover, the joint effect of these variables has not studied enough. All these issues propose the high complexity of the studying relationships and require using multi‐method approach.

4.1 Hypothesis Two major hypotheses guide this study: H1. Project work context factors (organizational culture, organizational climate, team, manager and task characteristics) significantly influence individual competencies of an employee. H2. The higher matching between employees’ characteristics and project work context, the higher shown competencies, job satisfaction and levels of personal and team performance.

4.2 Variables Four types of variables are distinguished for the purpose of the study (Figure 3):

Independent variables (predictor variable) including organizational culture, organizational climate, team and manager values, team and manager competencies.

Dependent variable (affected by the independent variable) including employee individual competencies and performance.

Mediating (intervening) variables that come between a stimulus and a response including employee competency profile, motivation, values and satisfaction.

Extraneous variables that may affect the differences in dependent variables including respondents’ age, gender, education, task, experience, industry, team size.

Figure 3: Variables of the study

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4.3 Subjects and sample The study is focused on innovation intensive organizations in technological areas of Hong Kong including Electronics, Green Technologies, Information Technologies, and Precision Engineering. Subjects involved for purpose of data collecting are employees involved into project‐based work and their managers (supervisors). The sample size involves 204 projects and proposes to get results with 95 % confidence interval and margin error 5 %.

4.4 Data collection The research methodology will utilize two methods for data collection including survey based on self‐ administered questionnaires and Behavior Event Interview (BEI). The survey is based on a set of self‐administered questionnaires. Data collecting process and tools for manager and employees are separated. The questionnaire includes four main parts:

Part 1 intends to collect data about general personal and professional information. This information is needed for statistical analysis only. It will take around 5 minutes to complete.

Part 2 includes questions about Organizational Culture. It is based on the modified Organizational Culture Profile (OCP) (Chatman et al., 1991, 2012) and The Organizational Culture Assessment Instrument (OCAI) (Cameron and Quinn, 1999). It will reveal data about values and norms shared by employees. It takes around 20 – 25 minutes to complete.

Part 3 observes Organizational Climate. It was designed and validated by Stringer (2002). It will take around 10‐15 minutes to complete.

Part 4 includes questions about the respondent, his peers and managers competencies, performance and job satisfaction. It will take around 10‐15 minutes to complete.

On the whole, the set of questionnaires will take around 45‐60 minutes to complete. To complement data collected by questionnaires, Behavioral Event Interview (BEI) method will also be used. It was developed by David C. McClelland and McBer (cited in Spencer and Spencer, 1993). BEI will use short detailed stories of employees about their job in terms of three peak successes and three major failures. It will focuse on differences of employees behavior in critical situations. The duration of BEI will be about 60‐90 minutes per one respondent.

4.5 Data analysis The collected data will be analyzed by using descriptive and inferential statistical methods. Decisions about management changes needed to foster employee’s competencies will be based on matching between work context and individual competencies. The different multi‐dimensional scaling measures will be considered to find optimal matching between variables. A model and hence a simulation platform will be built to predict the performance and the competencies that will show in the specific work context. Scenario‐based analysis will be conducted based on this simulation platform.

5. Conclusion The study described above intends to investigate employee performance in innovation projects and organizations and its relationships with other work context, project manager, team and employee characteristics. The study has potential to provide not only methods for optimal matching between work context and individual competencies but also tools for the improvement of team performance by facilitating the required competencies in specific work contexts. Major deliverables of the study include a conceptual model of methodology for project team assignment and performance management, decision support model for competency management in innovation, simulation platform for scenario‐based analysis and prediction of project team performance.

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Mikhail Rozhkov, Benny Cheung and Eric Tsui Significance to theory originates through considering the relationships between organizational culture, organizational climate, team, manager characteristics and individual competencies, their importance in innovation projects; finding solutions to manage individual competencies and work context to achieve high team performance. Applications of the research results will have significant impact to business practices and help to get a comprehensive view of the organization management practices and its effect on team performance. Applications include decision making about forming project teams that are more likely to achieve high levels of performance as well as improving of management practices and workplace environment.

Acknowledgements The work described in this paper was fully supported by a grant from the Research Grants Council of the Government of the Hong Kong Special Administrative Region, China (Project No.: PF10‐14718). The authors would like to express their sincere thanks to the research office of The Hong Kong Polytechnic University for the support of the research work under project no RUXA.

References Ashkanasy, N. M., Broadfoot, L. E., & Falkus, S. (2000) Questionnaire measures of organizational culture. In N. M. Ashkanasy, C. Wilderom & M. F. Peterson (Eds.), The handbook of organizational culture and climate, Thousand Oaks: SAGE Publications, pp 131‐145. Beyer, J.M., Hannah, D.R. & Milton, L.P. (2000) "Ties That Bind. Culture and Attachments in Organizations", The handbook of organizational culture and climate, eds. N.M. Ashkanasy, C. Wilderom & M.F. Peterson, SAGE Publications, Thousand Oaks, pp 323‐338. Benali, M. and Burlat T.P. (2012) “Modeling cooperation links within networks of firms: a case study”, Journal of Intelligent Manufacturing, pp 1‐7. Boyatzis, R.E. (1982) The competent manager: a model for effective performance, Wiley, New York. Bozkurt, T. (2009) Determination of individual competencies by statistical methods, T.C. Marmara Üniversitesi Sosyal Bilimler Enstitüsü İngilizce İşletme Anabilim Dali Sayisal Yöntemler Bilim Dali. Brown, K.A. & Hyer, N.L. (2010) Managing projects: a team‐based approach, McGraw‐Hill Irwin, New York, N.Y. Busch, E., Nash, J., & Bell, B. S. (2011). Remote work: An examination of current trends and emerging issues. Ithaca, NY: Center for Advanced Human Resource Studies, Cornell University Cartwright, S., Cooper, C.L. & Earley, P.C. (2001) The international handbook of organizational culture and climate, Wiley, Chichester, New York. Cerinšek, G., Petersen, S. and Heikura, T. (2011) “Contextually enriched competence model in the field of sustainable manufacturing for simulation style technology enhanced learning environments”. Journal of Intelligent Manufacturing, pp 1‐15. Chatman, J., Caldwell, D.F., III, C.A.O. & Doerr, B. (2012) Organizational culture and performance in high‐technology firms: the effects of culture content and strength. Cooper, K. (2000). Effective competency modeling & reporting: a step‐by‐ step guide for improving individual & organizational performance. New York: Amacom. Crowl, D., Attwood, D., & American Institute of Chemical Engineers (2007) Human factors methods for improving performance in the process industries. Hoboken, N.J.: John Wiley & Sons. Deal, T.E. & Kennedy, A.A. (1982) Corporate cultures: the rites and rituals of corporate life, Addison‐Wesley Pub. Co., Reading, Mass. Denison, D.R. (1990) Corporate culture and organizational effectiveness, Wiley, New York. Ennis, M.R. (2008) Competency models: a review of the literature and the role of the employment and training administration (ETA), US Department of Labor. Heneman, R.L. & Ledford, G.E. (1998) "Competency Pay for Professionals and Managers in Business: A Review and Implications for Teachers", Journal of Personnel Evaluation in Education, Vol. 12, No. 2, pp. 103‐121. Kerzner, H. and Kerzner, H. (2006) Project management :a systems approach to planning, scheduling, and controlling. 9th edn. Hoboken, J. Wiley, N.J. Kotter, J. and Hasket, J.L. (1992) Corporate Culture and Performance, Free Press, New York. Kurz, R., & Bartram, D. (2002; 2008). Competency and individual performance: Modelling the world of work. Organizational effectiveness, John Wiely & Sons, pp 227‐255, DOI: 10.1002/9780470696736.ch10 Marques, J., Zacarias, M., & Tribolet, J. (2010) A bottom‐up competency modeling approach. In A. Albani, & J. L. G. Dietz (Eds.), Advances in enterprise engineering IV (pp. 50‐64) Springer Berlin Heidelberg, DOI: 10.1007/978‐3‐642‐13048‐ 9_4 Michela, J.L. & Burke, W.W. (2000) "Organizational Culture and Climate in Transformations for Quality and Innovation", The handbook of organizational culture and climate, eds. N.M. Ashkanasy, C. Wilderom & M.F. Peterson, SAGE Publications, Thousand Oaks, pp 225‐244. Schein, E.H. (2010) Organizational culture and leadership, 4th edn, Jossey‐Bass, San Francisco.

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Mikhail Rozhkov, Benny Cheung and Eric Tsui Sebt, M.H., Shahhosseini, V. & Rezaei, M. (2010) "Competency based optimized assignment of project managers to projects", 12th UKSim International Conference on Modelling and Simulation, UKSim 2010, 24 March 2010 through 26 March 2010, pp 311‐316. Schein, E. H. (2010). Organizational culture and leadership (4th ed.). San Francisco: Jossey‐Bass. Simon, B. (2010) "A Discussion on Competency Management Systems from a Design Theory Perspective", Business & Information Systems Engineering, Vol. 2, No. 6, pp 337‐346. Sparrow, P.R. (2001) "Developing Diagnostics for High Performance Organization Cultures", The international handbook of organizational culture and climate, eds. S. Cartwright, C.L. Cooper & P.C. Earley, Wiley, Chichester, New York, pp 86‐ 106. Spencer, L.M. & Spencer, S.M. (1993) Competence at work: models for superior performance, Wiley, New York. Stringer, R. (2002) Leadership and organizational climate: the cloud chamber effect, Prentice Hall, Upper Saddle River, N.J. Spencer, L.M. (1997) Competency Assessment Methods, Assessment, development, and measurement, eds. L.J. Bassi, D.F. Russ‐Eft & American Society for Training and Development., American Society for Training and Development, Alexandria, VA, pp 2‐36. Svyantek, D.J. & Bott, J.P. (2004) "Organizational culture and organizational climate measures: an integrative review", Comprehensive handbook of psychological assessment: Industrial and organizational assessment, ed. M. Hersen, Wiley, Hoboken, NJ, pp 507‐524. Turner, R. (2007) The Impact of Emotional Intelligence in Project Management as a Measure of Performance, RMIT University, Property, Construction and Project Management. Westergren, U. (2011) “Opening up innovation: the impact of contextual factors on the co‐creation of IT‐enabled value adding services within the manufacturing industry”, Information Systems and E‐Business Management, 9(2), pp 223‐ 245. Woodruffe, C. (1993). What is meant by a competency?, Leadership & Organization Development Journal, Vol 14(1), pp 29 ‐ 36.

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The Influence of Emotional Intelligence on Employees’ Knowledge Sharing Attitude in Organizations in Thailand Chanthawan Sathitsemakul1 and Francesco Calabrese2 1 Bangkok University, Bangkok, Thailand 2 IKI‐SEA, School of Business, Bangkok, Thailand chanthawans@yahoo.com Abstract: Employees are the most important factor in sustaining successful organizational strategies, especially a strategy for sharing knowledge. However, determining the influences which shape the attitude and intention to exercise a sharing behavior are still in need of additional exposure (Bock and Kim, 2002).Therefore, it is important to develop a further understanding of those influences affecting the employees’ attitude for knowledge sharing within both an individual and an organizational context. In the working environment, emotional intelligence, trust and organizational citizenship behaviour are necessary for knowledge sharing. Emotional intelligence is the ability to control emotions. It will facilitate the high value tacit, implicit or explicit knowledge sharing, of employees and customers, among team members (Othman,A.K., & Abdullah, H.S. 2011). The influences/factors to motivate a knowledge sharing attitude resemble many of the same attributes described in the past decade to components of emotional intelligence (EI). It will add to the existing body of knowledge to study and understand how different EI profiles influence employees’ knowledge sharing attitude. The following document presents my ongoing literature review and proposed research models regarding the influence of emotional intelligence on employees’ attitudes for knowledge sharing in organizations. Keywords: emotional intelligence, employees’ knowledge sharing attitude

1. Introduction KM has become more recognized as an important strategy in business options. Many companies put a lot of effort into implementing KM practices in order to maintain their competitive advantage by utilizing the best knowledge of their employees. Employees are one of the most important factors in every successful organizational strategy, especially a strategy of knowledge sharing. They are the key success factor for knowledge connection as they can choose whether or not to share their knowledge. Therefore, it is important to develop a further understanding of those influences affecting the employees’ attitude for knowledge sharing within both an individual and organizational context. The influences/factors motivating a knowledge sharing attitude resemble many of those attributed to emotional intelligence (EI) in the past decade. It will add to the existing body of knowledge to study and understand how different EI profiles influence employees’ attitudes toward sharing knowledge. The impact of Emotional Intelligence should be evaluated from the two perspectives of how EI influences both individual and organizational motivation factors. The first objective of the research paper is to examine the influence of EI on employees’ knowledge sharing attitude in organizations in Thailand. The second objective is to find relationships between three sets of variables: EI and individual motivation factors; EI and organizational motivation factors; and finally the relationship between organizational and individual motivation factors. These relationships lead to the four (4) research questions addressed as follows:

Does emotional intelligence influence attitude toward knowledge sharing?

Does emotional intelligence influence organizational motivation factors?

Does emotional intelligence influence individual motivation factors?

2. Literature review 2.1 Knowledge sharing Knowledge sharing is perceived as an important fundamental process for generating new ideas and developing new business opportunities (Muhammad, Nida, Kiran, & Adnan, 2011). Empirical research increasingly presents evidence that effective knowledge sharing strategies translate into higher productivity and competitiveness for an organization in comparison to one embracing less effective knowledge sharing strategies (Lapre & Van Wassenhove, 2001).

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2.2 Attitude towards knowledge sharing and intention to share knowledge In understanding the antecedents of actual knowledge sharing, extensive literature exists about attitude towards and intention to share knowledge as key predictors of actual knowledge sharing. Attitude toward knowledge sharing and the intention to share are used interchangeably in some literature. Based on the Theory of Reasoned Action (TRA), the intention to share knowledge is influenced by the combination of: First, attitude toward knowledge sharing as the result of the sum of an individual’s beliefs about knowledge sharing; and Second, the subjective norm as the result of the opinion of people in an individual’s environment. The dual combination of attitude and subjective norm combine to influence Intention to share, thereby leading to the actual behavior eventually exhibited by an individual. The Theory of Planned Behavior (TPB) is a more recent modification of TRA. It has the same basic premises as TRA with the exception of adding perceived behavioral control, which is based on control beliefs and perceived facilitation (Chatzoglou, P.D. & Vraimaki, E. (2009), Kuo & Young (2008), Miller (2005), Yang, J.T. (2008)). The resulting model combining attitudes, subjective norms, and perceived behavioral control is reflected in Figure 1 below:

Figure 1: Theoretical research model – Version 1.0 According to the combined TRA and TPB theories, the subjective norm and perceived behavior control within the organizational climate are external motivation factors, which can be controlled by the organization. On the other hand one’s attitude toward knowledge sharing may be influenced by external motivation factors, but the effect can vary by employee because it is based on internal individual factors. Consequently, employees can and do make choices of sharing or not sharing their knowledge. Therefore, understanding the determinants of attitude towards knowledge sharing is the focus in this study. Table 1 reinforces that focus based on a subset of literature references on the subject of attitude toward sharing knowledge. Table 1: Summarization of selected literature supports the linkages between attitude and intention towards sharing knowledge Selected literature summarization Rahab & Purbudi (2013) examined that individuals’ intentions to knowledge are effected by knowledge sharing, organizational structure, attitude toward knowledge sharing and subjective norms. Chatzoglou & Vraimaki, (2009) asserted that employee’s attitude towards knowledge sharing and subjective norms directly influence intention to share knowledge. Yang’s (2008) study confirmed the impact of individual attitudes toward knowledge sharing processes, such as sharing and storing knowledge, on organizational knowledge sharing. Kuo & Young (2008) indicated that based on “Theory of Reasoned Action” and “Theory of Planned Behavior, the predictors of knowledge sharing intention are attitude, subjective norm, and perceived behavior control.

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2.3 Factors influencing knowledge sharing attitude The focus of this research now turns to determinates which further influence attitude toward shaping the intention, and ultimately, the behaviour of actually sharing knowledge. Figure 2 reflects that emphasis with an expanded version of the Theoretical Research Model. In addition to the individual factors; trust, organizational commitment, and self efficacy, external Organizational/Leadership factors are also important sources of knowledge sharing attitude and motivations. The effective employees’ involvement in KM practices, management support and commitment, incentives, recognition, development of a favourable culture, and knowledge sharing tools are all essential.

Figure 2: Theoretical research model ‐ Version 2.0 2.3.1 Individual motivation factors Interpersonal Trust: A high level of trust helps to facilitate knowledge sharing. If employees have low trust, they will be uncertain with the outcome of sharing. The tendency of concealing their knowledge is higher when the trust is low; therefore, trust building in the workplace is a necessary for effective knowledge sharing. Table 2: Summarization of selected literatures supports the linkages between interpersonal trust and knowledge sharing Selected literature summarization Al‐Alawi et al., (2007) summarized that knowledge sharing in organizations has a significant relationship with trust, information system, rewards and organizational climate. Hsu et al. (2007) presented the influence of trust and self‐efficacy in the employees willingness to share their knowledge and how they effects knowledge sharing behavior. Hong‐ping Sun & Xiang‐yang Liu (2006) identified that trust, subjective norms, as individual motivators, are the predictors of intention to share knowledge of individuals. Many KM researches indicate that a high level of trust helps to facilitate knowledge sharing. It is presented as moderator for knowledge to be exchanged smoothly. However, if employees have low trust, they will be uncertain with the outcome of sharing. The tendency to conceal their knowledge is higher when trust is low. (Ford, 2003; Rolland & Chauvel, 2000).

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Chanthawan Sathitsemakul and Francesco Calabrese Organizational Commitment: In organizations with high levels of organizational commitment, employees are more willing to provide extra effort and to share their knowledge within the organization. Table 3: Summarization of selected literatures supports the linkages between organizational commitment and knowledge sharing Selected literature summarization Storey & Quintas (2001) and Thomson & Heron (2005) suggest that the critical factors to motivate knowledge workers to create and to share their knowledge are organizational commitment. Lin (2006), Peltokorpi (2006), Alvesson (2005), McKenzie et al. (2001), and Scarbrough & Carter (2000), assert that the success of knowledge management implementing and generating appropriated knowledge in the organizations is greatly related to the levels of employees’ commitment to the organization because the higher levels of organizational commitment reflects the higher willingness and motivation of employees to share their knowledge. Cabrera, Collins & Salgado (2006) defend that the higher levels of organizational commitment of employees, the more engagement in knowledge sharing they will be.

Self‐efficacy: Self‐efficacy is the belief of individuals in their capability to help solve problems or improve work efficiency in the work place (Constant et al. 1996; Hargadon 1998). When employees believe that sharing their knowledge can contribute to organization performance, they will have higher positive attitudes toward knowledge sharing and will be more willing to share. Table 4: Summarization of selected literatures supports the linkages between self‐efficacy and knowledge sharing Selected literature summarization Kuo & Young (2008) suggested that the knowledge sharing can be predicted by self‐efficacy. Luthans (2003) suggest that the self‐efficacy influence knowledge sharing behaviour. When employees believe that sharing their knowledge can contribute to organization performance, they will have higher positive attitudes toward knowledge sharing and will be more willing to share. Wasko & Faraj 2000 and Kankanhalli et al. 2005 stated that motivational factors which influence employees to share knowledge within the organization are competence and self‐efficacy.

2.3.2 Organizational and leadership motivation factors Organization and Leadership: Most of KM research affirms that organizational structure characteristics, culture and interaction are powerful motivational factors for knowledge sharing. (Elham et al. (2012), Lin (2008)). Bock & Kim (2002) proposed that expected rewards, associations, and contributions are three factors affecting attitude toward knowledge sharing. However, the influence of organizational factors cannot work alone; it has to be in line with leadership. Leadership roles strongly impact knowledge sharing and directly influence collaborative culture. Collaboration is a foundation for a knowledge‐friendly culture which is an essential factor in the success of planning, developing and accomplishing KM (Yang (2007) and Michailova & Husted, 2003). Information technology (knowledge sharing tools) is also seen as an essential factor for the success of KM initiatives. This research will subsume the Leadership variable influences as an integral part of the Organizational Motivation factor dependent variable. That aspect is reflected in the Version 3.0 of the Theoretical Research Model, Figure 3.

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Chanthawan Sathitsemakul and Francesco Calabrese Table 5: Summarization of selected literatures supports the linkages between organizational structure and leadership and knowledge sharing Selected literature summarization Hsu (2006) studied manufacturing companies in Taiwan and found that organizational structures that benefit employees’ knowledge sharing are management commitment, knowledge management systems, and existing knowledge sharing climate. Lin (2008) suggested that organizational structure characteristics, culture and interaction are the powerful motivational factors for knowledge sharing. Leadership roles as a facilitator, mentor and innovator strongly impact knowledge sharing and directly influence collaborative culture. Collaborative culture is a foundation of knowledge‐friendly cultural which is an essential factor in the success of planning, developing and accomplishing KM (Yang (2007) and Michailova & Husted, 2003).

2.4 Emotional intelligence Intrinsic motivation factors motivate individuals to perform for their own achievement and satisfaction rather than for the organization. In contrast, it has been accepted that EI can create a high performance culture since it interrelates emotional, personal, and social abilities. The advantage of focusing on EI rather than intrinsic motivation in order to achieve the employee’s knowledge sharing attitude is that it can be developed and significantly improved by coaching (Jordan, Ashkanasy, Hartel & Hooper, 2002). The literature review also revealed that intrinsic motivation such as trust and organizational commitment can be influenced by extrinsic motivation but resulting outcomes can lead to high levels of uncertainty. Emotional intelligence (EI) which can predict the mentioned variables, therefore came into interest and contributed to one more version of the Theoretical Research Model, Figure 3, to be used for this research.

Figure 3: Theoretical research model – Version 3.0

2.5 Measures of emotional intelligence At present there are three (3) accepted primary models of emotional intelligence:

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Chanthawan Sathitsemakul and Francesco Calabrese Ability models of emotional intelligence These models emphasize the individual’s ability level to perceive, to use, to handle and to interpret emotions. The ability‐based model views emotions as useful sources of information that help one to decode and navigate the social environment. Trait models of emotional intelligence Trait EI refers to an individual’s self‐perceptions of their own emotional abilities. It is a measure by self administered report within the dimension of an individual personality framework. Mixed models of emotional intelligence These models include abilities, personalities and characteristics into the same phenomena. They combine motivation, states of consciousness, social activity and ability to understand and conduct emotions together. Emotional intelligence mixed models are used more broadly by researchers compare to other models. They are also used in Bar‐on and Goleman Emotional intelligence models. The model introduced by Daniel Goleman (1998/2001) is the most popular and accepted mixed model that focuses on EI as a wide array of competencies and skills that drive employees’ performance, abbreviated as ECI. It consists of four core components; Self‐awareness, Self‐management, Social‐ awareness and Relationship management. Several other models have also attained a degree of larger acceptance. They are listed below. EQ‐i: Model of emotional intelligence by Bar‐On’s (1997) is one of the most well known mixed models. This model was fundamentally based on personality characteristics, and thus is also representative of the TEIQue type models. WLEIS: Wong and Law Emotional Intelligence Scale developed by Wong and Law (2002). This measurement includes appraisal and expression of one’s emotion, appraisal and recognition of emotion in others, regulation of one’s emotion and use of emotion to facilitate performance. TEIST: Thai Emotional Intelligence Screening Test developed by Department of Mental Health (2000 cited in Sucaromana, 2010) by reason of the belief that western measures may not accurately measure emotional intelligence in Thailand. The measure consists of three (3) categories with nine (9) subscales known as

Virtue ‐ Virtue deals with self and social awareness, included

(a) Emotional self control, (b) Empathy, and (c) Responsibility

Competence ‐ Competence deals with self‐regulation and relationship management, included

(a) Self‐motivation, (b) Problem‐solving, and (c) Interpersonal

Happiness ‐ Happiness is associated with self‐efficacy and self‐acceptance, along with the ability to manage one’s emotions, included

(a) Self‐regard, (b) Life satisfaction, and (c) Peace

2.6 The relationship between emotional intelligence (EI) and factors that influence attitude towards knowledge sharing Emotional intelligence scales developed by Western sources focus on achievement prediction, capability, ability, and motivation to achieve target. TEIST, on the other hand, was developed by Thai psychiatrists and psychologists based on Thai culture that emphasizes goodness, mindfulness, peace, happiness and competency (Suppapitiporn S, Kanchanatawan B. & Tangwongchai S. 2006). TEIST is a well‐accepted EI tool in Thailand which has been used for assessment of EI among various groups of Thai adults. The Department of Mental Health research team conceptualized the EI concept for Thai people that are derived from the mental health standpoint and Buddhist philosophy. Therefore, this research will use the TEIST EQ Model and “philosophy” as the independent variable reflected by the Model in Figure 4 and discussed below.

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Chanthawan Sathitsemakul and Francesco Calabrese EI and Interpersonal trust: Literature suggests that trust and safety within groups is correlated to interpersonal affective behavior. The strength of social ties and relationships within groups is directly related to emotional competency and it enhances knowledge sharing and learning opportunities (Rousseau et al. (1998), and McAllister (1995)). EI and Organizational Commitment: A positive relationship between EI and organizational commitment is established in many studies. For example, a study on direct health care workers by Humphreys, Brunsen, and Davis (2005) and a research conducted on public sector employees by Adeyemo (2007) both found EI is significantly related to organizational commitment. Also another study found that employees with high levels of EI have more capability to maintain their organizational commitment (Jordan, Ashkanasy & Hartel (2002)). EI and Self‐efficacy: Various studies focused on the interrelation between EI and self‐efficacy suggests that EI is important to develop employees’ self‐efficacy. (Schwarzer (1993),Chan (2004) Schutte et al. (1998)). Moafian & Ghanizadeh (2009) also found that three subscales of EQ‐i (Model of emotional intelligence by Bar‐On) are good predictors of self‐efficacy. EI and Organizational motivation factors: Hales and Gough (2003) found that employees are motivated by organizational motivated factors differently because they perceive valence in different ways. For example an attractive reward for one employee might not be attractive to others. Based on these literatures, it is crucial to understand the relationship between EI and the organizational motivation factors.

Figure 4: Conceptual research model Based on the review above, the attitude towards knowledge sharing is a significant factor to predict intention to share knowledge and will eventually lead to actual knowledge sharing. Assumingly, the better employees’ knowledge sharing attitude is the higher intention to share their knowledge which is in the same context of subjective norm, and perceived behaviour control. Also, Emotional Intelligence influences the organizational motivation factors and individual motivation factors which impact attitude towards knowledge sharing. Thus the following hypotheses are developed; H1: The higher the emotional intelligence the better the knowledge sharing attitude H2: The higher emotional intelligence positively influences the organizational motivation H3: The higher emotional intelligence positively influences the individual motivation. The theoretical framework for this study is shown in Figure 5. The dependent variables are attitude towards knowledge sharing, organizational motivation factors, and individual motivation factors, while the independent variable is emotional intelligence.

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Figure 5: Theoretical framework

3. Research methodology and analysis An attempt will be made to measure the organizational and individual motivation factors, and the knowledge sharing attitude of employees in organizations in Thailand, and assessed against TEIST EI results to determine if EI is a predictor of knowledge sharing attitude.

3.1 Data collection The sampling strategy will follow several stages. First, organizations with known KM practices in Thailand will be selected. The staff directory listed on each organization will serve as the sampling frame. Then, respondents will be randomly selected by Stratified Random Sampling Design. Respondents will complete both the TEIST and a questionnaire on the dependent variables.

3.2 Measurement and data collection The items for the constructs on the dependent variables will be adapted from past studies and will be measured on a 5‐point Likert scale; ranging from 1 = strongly disagree, 2 = disagree, 3 = neither agree nor disagree, 4 = agree and 5 = strongly agree. Questionnaire will be developed to measure the influence of emotional intelligence on attitude towards knowledge sharing and organizational and individual motivation factors. 3.2.1 Emotional intelligence The measure of emotional intelligence will be adapted from the Thai Emotional Intelligence Screening Test (TEIST) to measure the respondents’ Emotional Intelligence level in each of the Emotional Intelligence component. Sample of the question are;

When I am angry or unhappy, I can recognize these feelings.

I do not know what I am good at.

I can relax myself when I feel tired and stressed.

3.2.2 Organizational motivation factors, individual motivation factors and attitude towards knowledge sharing The measure of dependent variables will be developed based upon the previously validated instruments. Sample of the question are;

There is a necessary tool to share knowledge.

I may get a higher salary if I share knowledge.

Sharing knowledge improves my reputation.

My colleagues will definitely help me whenever I want to

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I will never leave my company.

Sharing knowledge with my co‐workers is a good thing to do.

As the measures for the dependent variables will adapted from the previously validated instruments, a pilot test will be conducted to validate the questionnaire using students in knowledge management related classes with extensive work experience. Respondents will examine the content validity and applicability. The completed responses will be analyzed to ensure that the questionnaire is measuring the constructs of interests and the questions are concise, clear and unambiguous.

3.3 Results testing Respondents’ findings will be compared to their EI results to test the merits of the hypotheses in relating EI to attitude, organizational and individual motivation.

4. Conclusion Literature review has shown relationships between EI and KS attitude of employees and raised interesting questions about the influence of EI on KS attitude in organizations. Understanding the relationship of EI to the attitude, and organizational and individual motivation for knowledge sharing will allow organizations to determine adjustments in all areas, if required, to improve and sustain long term knowledge sharing environments. To avoid adjusting for cultural differences, the research focuses on data in a Thai context which is one of the research limitations. However, the theoretical and conceptual models and methodology can serve as a general approach with different EI models and questionnaires for other cultures.

References Adeyemo, D.A. (2007) Emotional intelligence and the relationship between job satisfaction and organizational commitment of employee in public parastatals in Oyo State, Nigeria, Pakistan Journal of Social Sciences, 4, 324‐330. Bar‐On, R. (1997) Bar‐On Emotional Quotient Inventory (EQ‐i): A Measure of Emotional Intelligence, Multi‐Health Systems, Toronto, Canada. Bar‐On, R. (2006) The Bar‐On model of emotional‐social intelligence (ESI). Psicothema, 18, 13‐25. Bock, G.W. & Kim, Y. (2002) Breaking the Myths of Rewards: An Exploratory Study of Attitudes about Knowledge Sharing, Information Resources Management Journal (IRMJ), Vol. 15(2), 14‐21. Chan, D.W. (2004) Perceived emotional intelligence and self‐efficacy among Chinese secondary school teachers in Hong Kong. Personality and Individual Differences,36 (8), 1781–1795. Chatzoglou, P.D. and Vraimaki, E. (2009) “Knowledge‐Sharing Behavior of Bank Employees in Greece”, Business Process Management Journal, Vol. 15 No. 2, 245‐266. Department of Mental Health. (2000) The Development of Thai Emotional Intelligence Screening Test for Ages 12‐60, Thailand: Department of Mental Health. Elham, A., Rosman, B.M.Y. & Nik, H.N.M. (2012) Determinants of knowledge sharing behavior, 2012 International conference on Emonomics, Business and marketing management, Vol. 29. Fabrice, G. (2007) Intrinsic‐Extrinsic Motivations, Knowledge Sharing and Innovation in French Firms, Organisational Innovation: the dynamics of organisational capabilities and design, Gredeg Demos, Sophia Antipolis, November, 15‐ 16. Ford, D.P. (2003) Trust and knowledge management: The seeds of success, Handbook on Knowledge Management Holsapple CW (Ed.), Springer‐Verlag, Berlin, Heidelberg, 553‐576. Goleman, D. (1998) Working with Emotional Intelligence, Bantam, New York, NY. Goleman, D. (2001) An EI‐based Theory of Performance, In C. Cherniss and D.Goleman, The Emotionally Intelligent Workplace, Jossey‐Bass, San Francisco,CA, 27‐45. Hales, C. and Gough, O. (2003) Employee evaluations of company occupational pensions: HR implications. Personnel Review, 32 (3), 319‐340. Hsiu‐Fen L. (2007) Effects of extrinsic and intrinsic motivation on employee knowledge sharing intentions, Journal of Information Science, 135‐149. Humphreys, J., Brunsen, B., & Davis, D. (2005) Emotional structure and commitment: Implications for health care management, Journal of Health Organization and Management, 19, 120‐129. Jordan, P.J., Ashkanasy, N.M., & Hartel, C.E.J. (2002) Emotional intelligence as a moderator of emotional and behavioral relations to job insecurity, Academy of Management Review, 27, 361‐372. Jordan, P.J., Ashkanasy, N.M., & Hooper, G.S. (2002) Workgroup emotional intelligence: Scale development and relationship to team process effectiveness and goal focus, Human resource management review, 12(2):195‐214.

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Chanthawan Sathitsemakul and Francesco Calabrese Khalid Al‐Mabrouk. (2006) Critical Success Factors Affecting Knowledge Management Adoption: A Review of the Literature, Innovations in Information Technology, 1‐6. Kuo, F. & Young, M. (2008) Predicting knowledge sharing practices through intention: A test of competing models, Computer in Human Behaviour, 24(6), 2697‐2722. Lapre, M.A. & Van Wassenhove, L.N. (2001) Creating and transferring knowledge for productivity improvement in factories, Management Science, Vol.47, Iss. 10, 1311‐1325. Luthans F. (2003) Positive organizational behaviour: developing and managing psychological strengths, Academy of Management Executive, Vol. 16(1): 57–75. Mayer, J.D., & Salovey, P. (1997) What is Emotional Intelligence? P. Salovey& D. Sluyter (Eds.), Emotional Development and Emotional Intelligence: Implications for Educator, Basic Books, New York, 3‐31. McAllister, D.J. (1995) “Affect‐ and cognition‐based trust as foundations for interpersonal co‐operation in organizations”, Academy of Management Journal, Vol. 38, 24‐59. Miller, K. (2005) “Communications theories: perspectives, processes, and contexts”, New York: McGraw‐Hill. Moafian, F & Ghanizadeh, A. (2009) “The relationship between Iranian EFL teachers’ emotional intelligence and their self‐ efficacy in Language Institutes”, System, 37, 708–718. Muhammad S., Nida J., Kiran S. & Adnan R. (2011) “Antecedents of Knowledge Sharing Attitude and Intentions”, European Journal of Scientific Research, Vol. 56, No.1, 44‐50. Othman, A.K. & Abdullah, H.S. (2011) “The Influence of Emotional Intelligence on Tacit Knowledge Sharing in Service Organizations”. M. Al‐Shammari (Ed.), Knowledge Management in Emerging Economies: Social, Organizational and Cultural Implementation, 171‐185. Palladino, J. J., & Bloom, C. M. (2008) “Motivation and emotion” In S. F. Davis & W. Buskist (Eds.), 21st Century PSYCHOLOGY: A Reference Handbook, Los Angeles: Sage Publications. Pintrich, P. R. (2003) “A motivational science perspective on the role of student motivation in learning and teaching contexts”, Journal of Educational Psychology, 95(4), 667‐686. Rahab & Purbudi W. (2013) “Predicting Knowledge Sharing Intention Based on Theory of Reasoned Action Framework: An Empirical Study on Higher Education Institution”, American International Journal of Contemporary Research, January, Vol. 3 No. 1. Rolland, N. & Chauvel, D. (2000) “Knowledge transfer in strategic alliances”, Knowledge Horizons: The Present and the Promise of Knowledge Management Despres C & Chauvel D(Eds.), Boston, MA, 225‐236. Rousseau, D.M., Sitkin, S.B., Burt, R.S. & Camerer, C. (1998) “Not so different after all: a cross‐discipline view of trust”, Academy of Management Review, Vol. 23, 393‐404. Schutte, N. S., Malouff, J. M., Hall, L. E., Haggerty, D. J., Cooper, J. T., Golden, C. J., & Dornheim, L. (1998) “Development and validation of a measure of emotional intelligence. Personality and Individual Differences”, 25, 167–177. Schwarzer, R. (1993) Measurement of perceived self‐efficacy. Psychometric scales for cross‐cultural research. Berlin, Germany: Freie Universität Berlin. Storey, J. & Quintas, P. (2001) “Knowledge management and HRM”, J. Storey (Ed.), Human Resource Management, a Critical Text. London: Thomsom Learning. Sucaromana U. (2010) “The Thai Emotional Intelligence Screening Test: Measurement and Validation with Pre‐Service Teachers”, 8‐16. Suppapitiporn S., Kanchanatawan B. & Tangwongchai S., (2006), Emotional Intelligence of the third year Chulalongkorn Medical Students. Chula Med J 2006 Mar; 50(3): 165‐173 Thompson, M. & Heron, P. (2005) “The difference a manager can make: organizational justice and knowledge worker commitment”, International Journal of Human Resource Management, Vol. 16 (3), 383‐404. Wong, C. S. & Law, K. S. (2002) “The effects of leader and follower emotional intelligence on performance and attitude: An exploratory study”, The Leadership Quarterly, 13(3), 243‐274.

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Quality of Higher Education Institutions as a Factor of Students’ Decision‐Making Process Petr Svoboda and Jan Cerny University of Economics, Prague, Czech Republic xsvop30@fm.vse.cz cerny@fm.vse.cz Abstract: This paper deals with quality in higher education (HE). The quality of HE institutions can mean different things to different people, depending on their roles and perspectives. Therefore, quality is, as is generally known, very difficult to be evaluated, although the question of how to do this has been studied for many years. Nevertheless, the intention to improve the quality of HE institutions and their services is agreed by all stakeholders and innervated further by the current worldwide economic recession and the urge to overwhelm the impact of the crisis on economic growth. This study is a continuation to and a further extension of our previous work in which we have developed a system‐dynamic knowledge‐ based model characterising the decision‐making process of prospective students when choosing a HE institution and later on in the course of their studies. Many factors which influence the process have been identified and the quality of HE institutions has appeared to be one of the most important of them. This is caused especially by the fact that various characteristic features of quality have an impact on a number of students in each stage of the study process. The paper attempts to contextualize many influential research papers and reviews on the perception of HE institutions’ quality from the students’ point of view. During the last few years, many of them have been published. Generally the most obvious finding is that the academic domain significantly differs from the well‐known business principles. This is primarily due to the inconsistent goals of service providers (HE institutions) and their students. Some students prefer just the tangible product, i.e. a diploma or a degree while others are interested rather in the content and the quality of the educational process, leading to intangible knowledge. The primary aim of this paper is to identify and examine the most important determinants of the quality of HE institutions that influence students’ interest in their studies. Research data have been obtained from 175 undergraduate business students currently studying at Prague’s University of Economics’ Faculty of Management, Czech Republic. The study will help HE institutions’ managers to better understand the wants and needs of their customers in order to meet their expectations. Keywords: higher education, quality, students’ perception, students’ behaviour, knowledge‐based modelling

1. Introduction According to many experts, service quality is the most powerful competitive tool currently reshaping marketing and business strategy in today’s competitive environment and thus, rendering quality service is a key for success. In the course of years, the quality of services has been connected with increased profitability by generating repeated sales, competitive products, service differentiation, positive word of mouth feedback and customer loyalty (Kimani et al, 2011). The essential principle of service quality is focusing on the customer. In HE, customers for the services can be divided into the following groups; students, employees, the public sector and the government, and industry and the general public (Martensen et al, 2000). In this study, present and future students are viewed as the main customers. However, in the context of a HE institution, defining the customer concept is not a trivial undertaking. Wallace (1999) has pointed out that the primary customers are the students – as without students to teach – there is no business or services for HE institutions to provide. The growing importance of service quality has been influenced mainly by the changing nature of the world economies and related customers’ changing needs and preferences. The quality of services has become a critical factor in enabling companies to achieve a competitive advantage and thus, it significantly contributes to productivity and profitability (Sanchez at al, 2007). In a competitive corporate strategy, service quality has become a key concept.

1.1 The research questions The major research questions for this study are: 1) What is the most important dimension influencing students’ perception of the quality of higher education? 2) Which sub‐criteria most determine students’ perception of the quality of higher education institution? 3) Are there any correlations between the underlying quality factors and other demographic factors? The purpose of the research is to make a preliminary study for

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Petr Svoboda and Jan Cerny the creation of a knowledge‐based model, which will best reflect the reality of HE institution and its components. This will be reached by adopting the insights and experience of the stakeholder groups, beginning with students – through the knowledge management approach. The main goals of this analysis are to identify the determinants of the HE institutions’ quality to evaluate the individual weight of the determinants in defining quality from a students’ point of view, and to find out, which of the factors are significant and assess the correlations among them. The most important determinants will be elaborated in a further research. The purpose of the research is not only of academic interest, but it should also have important practical meaning for the management of institutions offering higher education. The structure of this paper is as follows: the following section presents a review of the related literature. Subsequently, the context, data, and research methodology are briefly discussed, followed by a presentation of the achieved results. The last chapter is dedicated to the discussion of findings and their implications for managers. It also presents some limitations and recommendations for a further research, and winds up with a conclusion.

2. Literature review Universities are continually trying to meet the needs of employers, governments, and especially students and thus they have to constantly solve new emerging problems. These issues certainly include the quality assurance, because the importance of the quality of HE institutions is continuously increasing also in the countries, where the majority of local universities are financed from the public resources. Although such arrangement necessarily influences the overall market functionality, there still remains a reasonable space where universities heavily compete against each other for new students (Svoboda, Voracek and Novak, 2012). Providing HE has become a product and HE institutions have been driven by competition to review the quality of their services, to redefine their product and to measure and track customer satisfaction in ways that are well known to service marketing specialists (Kotler, 1985). The long‐term survival of HE institutions depends on a good or bad quality of their services. The HE institutions’ leaders have also realized that quality distinguishes one HE institution from the rest (Aly and Akpovi, 2001; Kanji et al, 1999).

2.1 Evaluating the quality of higher education institutions What exactly is the quality in HE? Unfortunately, there is no commonly accepted definition of quality that would apply specifically to the HE sector. This is mainly due to the fact that education services are often difficult to measure and intangible in their nature, since their outcome is reflected in the transformation of individual students in their knowledge, characteristics and behaviour (Michael, 1998). Furthermore, issues such as independence or autonomy complicate the whole quality assessment process of HE institutions (Middlehurst and Gordon, 1995). There have been attempts to assess the quality offered by universities by accreditation agencies. However, these agencies, operating in each country, have not clarified issues on institutional quality assessment. They have neither greatly influenced the perception of quality in the HE sector by accrediting degrees and educational work offered by universities (Parri, 2006). According to Zeithaml et al (2009), service quality is a focused evaluation that reflects customers’ perceptions of responsiveness, empathy, assurance, tangibles and reliability. Evaluation of quality through a simplistic approach can mean (i) a brief description of the quality term, (ii) setting certain factors that can be evaluated, (iii) comparison of these factors with the work done in each institution and (iv) offering a conclusion on the quality of the questioned institution. Unfortunately, evaluating the quality of a HE institution is not a simple issue (Parri, 2006). Furthermore, since the set of quality factors to be evaluated and their relative weight is not constant, the complexity of the process increases. The relative weight of these factors varies according to different points of view of the individual stakeholders.

2.2 Students’ perception of quality Customer perception of service quality is crucial as it determines how they evaluate the service. The evaluation is based on customers’ expectations. As the expectations are dynamic in nature, customers’ evaluations may also change from time to time. Thus, how customers evaluate what they term as a quality service today, may change tomorrow. This is why it is necessary to continuously monitor and evaluate the quality at any institution offering services to the public.

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Petr Svoboda and Jan Cerny Within the HE institutions, the conceptualization of quality does not differ from the conceptualizations in other service contexts. Certain factors, however, are discovered to be more specific to HE settings. Benoit et al (1998) conducted a conceptual study on the emerging contribution of online resources and tools to classroom learning and teaching trends in HE. The researchers identified seven issues: (i) the emergence of a new mixed mode of learning (face to face and on‐line learning activities); (ii) information access is more direct interactive and flexible; (iii) social interaction recovers its importance in the learning process; (iv) the learning community, supported by network technologies, is a new collaborative learning arrangement being tested in a large number of ways; (v) computer resources are used to enlarge the notion of performance as regards teaching and learning on university campuses; (vi) the university as an institution is invited to adapt its activity to new higher education needs; and finally, (vii) the computer linked to other computers constitutes an important element in the modification of academic administrative procedures at both micro and macro levels. Student satisfaction and quality of a HE institution were investigated at the University of Bari in Italy (Petruzzellis et al, 2006). The institution had suffered damage to its image due to various events and was experiencing a process of repositioning. The main factors of student satisfaction were good to the student needs in general, together with a positive level of education quality and the location of the institution. Tsinidou et al (2010) conducted another study on quality factors in higher education among business and economics students in Greece. In this case, the main categories were: academic staff, administrative services, library service, curriculum structure, facilities, location, and career prospects. A factor analysis was used to find out the important factors in each category. The results suggested that students rated communication skills as the most important variable in the academic staff category; clear guidelines and advice in the administrative service category; availability of textbooks and journals in the library service category; practical or hands on experience in the curriculum structure category; cost of transportation was the top ranked factor in the location category, quality of classrooms and laboratories was the biggest concern in the infrastructure category; and regarding the career prospects, students highly appreciated their professional careers.

3. Research methodology In this paper, the authors attempted to approach to the quality determinants of HE institutions rather than to quality as such. The goal was to measure the weights of particular aspects of quality in order to find out the hierarchy of the impacts of these aspects on student satisfaction. The research community at large does not agree on how to evaluate these concepts. A number of researchers recommend quite different approaches.

3.1 Data collection A structured questionnaire survey was adopted for this study to evaluate the students’ perception of the entire HE institution’s environment. This design helped to describe the nature of perception that students have in terms of the various factors affecting their perception of the HE institution. The questionnaire was distributed among students of Prague’s University of Economics’ Faculty of Management, situated in a small South‐Bohemian town of Jindrichuv Hradec. The University of Economics is the largest economic educational institution in the Czech Republic, even though the Faculty of Management is its smallest faculty with a total of 977 registered students in a three‐year bachelor and a two‐year master‐study programmes. The faculty also has a doctoral study programme, but this one is not covered in the current study. To collect data from a sample, a survey method using self‐completion questionnaires was used. The questionnaires were distributed among current undergraduate students of the above‐mentioned faculty. The questionnaire was distributed among students from all five academic years to provide a spherical point‐of‐ view about the particular HE institution and the opportunity to point out the differences among students of different years of their studies. Students’ participation was voluntary and completely anonymous. The sample has been differentiated only by gender, the type of study and the year of study. The instrument of the survey was a self‐explanatory questionnaire that could be filled in by respondents themselves. The questions asked were short, clear and easy to understand. The questionnaire contained brief written instructions assisting students in answering the questions and a statement of the study’s purpose. A pre‐testing of the questionnaire was performed with several students of the faculty, which helped check for any perceived ambiguities, errors or omissions.

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3.2 Data sample The data have been collected in a survey (spring 2013) among bachelor and master‐level students of Prague’s University of Economics’ Faculty of Management. The sample consisted of 175 respondents, representing about 18% of the population of the faculty’s students. 51% of the respondents are at the bachelor‐level, the rest at the master‐level. 75% of the respondents are full‐time students and 30% of them are male. The year distribution is 13% first‐year, 17% second‐year, 21% third‐year, 16% fourth‐year, and 34% fifth‐year students. A comparison of this sample to the number of students in each field of studies suggests that the sample is not non‐representative.

3.3 Data analysis The questionnaire used in this study has been constructed thanks to the utilization of some quality factors proposed by the researchers previously mentioned. Additional features were added concerning questions related to the specifics of the Faculty of Management. The questionnaire has covered three dimensions for the evaluation of the aspects: education, facilities, and information and communication channels. Figure 1 shows these three areas and the sub‐criteria that were used in the survey. The tree‐map appears to be a particularly efficient way of how to visualize the hierarchy of the stated problem as a diagram.

Figure 1: The evaluated dimensions and the sub‐criteria used in the survey The goal is placed at the top of the diagram, the main features are put in the middle and finally, the sub‐ criteria are located at the bottom. The data analysis uses 26 factors to measure the HE quality. All indicators use a five‐point Likert scale where 5 = the most favourable response alternative and 1 = the least favourable response alternative. Arguably, this scale has the advantage of being more specific in the area of the HE sector. The analysis converts these measurements to a scale from 0 to 100 and they are displayed in percentages.

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Petr Svoboda and Jan Cerny Finally, the levels of quality determinants were compared with demographic variables such as the type of study and the years of study to find out the differences in perceptions among various segments of the student population.

4. Research results Research results concerning students’ perception of the HE institutions’ quality are very interesting. While students’ perception over the five years has been relatively at the same level, quite different opinions and ratings between the full‐time and distant students have been found. In the following paragraphs, the reasons and possible explanations will be discussed in more detail. It would also be appropriate to mention that considering the gender differentiation, women evaluated almost all factors in all dimensions as more important for the HE institutions’ quality than men did. The only exceptions were related to informatics, more specifically, they concerned two factors: (i) teaching of informatics and (ii) ICT Department, which is quite understandable. This study does not deal with a further analysis of this result, but it could be an interesting topic for a further research.

4.1 Students’ perception of quality in the course of time In examining students’ perceptions of quality factors regarding the study programme, no significant difference between the bachelor and master students has been found. This indicates that perceived quality of the HE institution does not grow or diminish in the course of time. However, among students of the master‐study programme, different characteristic features influencing student satisfaction – concerning the quality of various evaluated factors – have occurred. A first interesting finding is that students’ perception of the teachers’ practical knowledge and experience has increased in comparison with bachelor students. Master students attach greater importance to the involvement of experts from practice in the teaching process and are calling for a closer interconnection between education and practice. Another interesting topic is students’ perception of the university’s image. Some master students appreciate the excellent image of the institution’s brand, other students criticize the decreasing demands and easier passibility through their studies, which – according to them – has a negative impact on the HE institutions’ quality. Many respondents among the master‐study programme students are strongly dissatisfied with a low hour dotation in foreign language education at the above‐mentioned institution, which they consider as a very important factor.

4.2 Students’ perception of quality regarding the type of their studies The type of study has been found out as the most important demographic variable, where significant differences in perceptions of HE institutions’ quality between full‐time and distant students have been found. The impact of the particular dimensions’ quality on the HE institutions’ quality as perceived by students can be seen in Figure 2. The majority of students perceive the quality of education as a crucial dimension of HE institutions’ quality. The facilities’ quality along with information and communication channels’ quality are perceived rather as complementary in nature, but they have also a great impact on HE institutions’ quality according to respondents. While the perceptions of the “education” dimension are relatively the same among distant students (92%) and full‐time students (90%), the perceptions of the “facilities” dimension are significantly different. Full‐time students evaluate facilities as much more important (75%) than distant students (65%). This is a relatively logical finding, since full‐time students spend much more time at school and adjacent facilities. Therefore, they consider them more important than distant students do. On the contrary, the importance of “Information and communication channels” dimension is slightly more highly evaluated by distant students. This is mainly due to the fact that distant students rely on various factors of this dimension. Thus they attach greater importance to the whole dimension. However, even here, exceptions were found and are briefly discussed in the sections below. The impact of the evaluated factors’ quality on the HE institutions’ quality as perceived by students is shown in Table 1.

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Figure 2: The impact of the particular dimensions’ quality on the HE institutions’ quality as perceived by students (100% = decisive impact, 0% = no impact) Table 1: The impact of the evaluated factors’ quality on the HE institutions’ quality as perceived by students (100% = decisive impact, 0% = no impact)

Perceived impact on the quality of HE institution

Factor

Distant students

Full‐time students

Education quality (intangibles)

92%

90%

A1: Teachers’ professional knowledge

77%

A2: Teachers’ pedagogical abilities

74%

68%

A3: Teachers' approach to students

69%

70%

A4: Diversity of subjects

68%

69%

A5: Foreign language teaching

54%

68%

A6: Subjects read in foreign languages

46%

55%

A7: Teaching of informatics

49%

56%

Facilities quality (tangibles)

65%

75%

B1: Library

43%

54%

B2: Reading rooms

38%

52%

B3: Classrooms

38%

47%

B4: Cleaning service

31%

36%

B5: Indoor temperature

36%

42%

B6: Students’ canteen

26%

37%

B7: Students’ Hostel

30%

44%

B8: Student Affairs Department

46%

56%

B9: Editorial Department

33%

40%

B10: ICT Department

40%

46%

Information and communication channels quality

74%

71%

C1: Student Information System

61%

55%

C2: Official website

50%

48%

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Perceived impact on the quality of HE institution

Factor

Distant students

Full‐time students

C3: Bulletin boards

26%

25%

C4: Information screens

28%

27%

C5: Webmail interface

47%

45%

C6: Social network sites

39%

49%

4.2.1 Students’ perception of education quality As to the quality of education dimension’s sub‐criteria (Figure 3), students evaluate the professional knowledge of teachers as the most important factor followed by the pedagogical abilities of teachers, teachers’ approach to students, and the diversity of subjects. The quality of all these factors has great impact on the HE institutions’ quality as perceived by students. The perception of the other factors significantly differs between distant and full‐time students. The highest difference has occurred in foreign language teaching, which full‐time students evaluated as of great importance, while distant students only of average importance. This could be caused by the low hour dotation of foreign language subjects in the distant‐study programme. The same effect partially appeared also in the remaining two factors: subjects read in foreign languages, and teaching of informatics.

Figure 3: The impact of the quality of particular “education” factors on the HE institutions’ quality as perceived by students (100% = decisive impact, 0% = no impact) 4.2.2 Students’ perception of facilities quality As stated above, full‐time students evaluate the facilities as much more important than distant students (Figure 4). The greatest differences are in the following three factors: reading rooms, students’ canteen, and students’ hostel; all of these full‐time students consider much more important than distant students. Both distant and full‐time students most appreciate the Student Affairs Department. This is probably due to the fact that this department helps them solve various problems and issues related to their studies, which is highly important for most students. In case of the Faculty of Management, students praise the friendly milieu of the town and especially the family atmosphere of the small faculty and related helpfulness of both the academic and the administrative staffs. On the other hand, relatively high percentage of students is dissatisfied with the faculty location and the necessity of commuting, which have also a certain impact on the perceived quality.

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Figure 4: The impact of the quality of particular “facilities” factors on the HE institutions’ quality as perceived by students (100% = decisive impact, 0% = no impact) 4.2.3 Students’ perception of the quality of information and communication channels Figure 5 shows that the majority of factors among the information and communication channels dimension is rated almost equally by both distant and full‐time students. The only significant differences concern Student Information System and the social network sites. Full‐time students perceive the quality of institutions’ social network sites as more important, probably due to the closer relations among their colleagues and a higher rate of using social networks to communication and information sharing. On the other hand, distant students rely mainly on the common information and communication channels, such as the Student Information System. HE institutions’ official websites and webmail interfaces are rated as of medium importance by both groups, bulletin boards and various information screens of a rather low importance.

Figure 5: The impact of the quality of particular “information and communication channels” factors on the HE institutions’ quality as perceived by students (100% = decisive impact, 0% = no impact). 4.2.4 Significant correlations among the quality factors As for the correlations among particular quality factors, some of them are expectable, such as “foreign language teaching” and “subjects read in foreign languages” (correlation coefficient = 0.65), “library” and “reading rooms” (correlation coefficient = 0.52), “cleaning service” and “indoor temperature” (correlation

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Petr Svoboda and Jan Cerny coefficient = 0.58), “students’ canteen” and “students’ hostel” (correlation coefficient = 0.69) or “bulletin boards” and “information screens” (correlation coefficient = 0.46). Some other correlations are rather surprising and interesting, e.g. the correlation between “education” and “teachers’ professional knowledge” (correlation coefficient = 0.54), while the correlation between “education” and “teachers’ pedagogical abilities” is much less significant (correlation coefficient = 0.33). There was a long and profound debate on the topic, which of the two factors is more important: if a teacher’s professional knowledge or his/her pedagogical abilities. It is still topical and questionable, but the result was that the essential condition is professional knowledge. Apparently, respondents are of the same opinion. Another interesting correlation between “foreign language teaching” and “teaching of informatics” (correlation coefficient = 0.51) could indicate that some students prefer just the tangible product, i.e. a diploma or a degree, while others are interested rather in the content and the quality of the educational process, leading to intangible knowledge.

5. Conclusion This study examined the quality of HE institutions from the students’ perspective. The most important demographic variable identified is the type of study, since significant differences in perceptions of HE institutions’ quality between the distant and full‐time students have been found. This indicates that HE institutions’ managers should approach to distant students in a different way than to full‐time students. After the detailed analysis of the conducted survey, recommendations can be made, based on the students’ perceptions. These could be taken into consideration by HE institutions’ managers in order to improve their knowledge management systems. Providing of the optimal quality on all dimensions, identified by students, could be attractive for HE institutions’ managers, but putting wrong priorities to the important factors could result in inefficient allocation of resources. HE institutions have to continuously identify the needs and wants of their students as their primary customers, and try to fully satisfy them. As the students‐respondents identified many important factors related to the HE institutions’ quality, these HE institutions should not treat them in isolation since they have a cumulative effect on student satisfaction. They should focus on a continuous improvement of the quality within different departments of a HE institution. This study suggests that the quality of a HE institution is a clear antecedent of student satisfaction. The quality of education appears to be the most important factor, but the quality of facilities also has a strong impact on these factors, mainly in case of full‐time students. HE institutions have begun to be much more interested in student satisfaction due to the increasingly performance‐based nature of public funding. The performance‐ based funding will most probably become even more important in the future. Consequently, managers of HE institutions are very interested in knowing the drivers which have the greatest influence not only on student attraction, but also on student retention. Such knowledge and insight can help managers make decisions concerning the allocation of scarce resources. Moreover, managers can identify processes and activities that will increase student satisfaction. This could be done by conducting and thoroughly analysing student surveys. In this way, a HE institution can enhance the quality of education offered to students, thus increasing student satisfaction. These steps will finally be reflected in the increased financial performance of a HE institution. This paper is meant as a contribution for those working with quality knowledge, mainly with regard to higher education and decision‐making. It deals with data from a small faculty located in a relatively small town, therefore, more studies from a higher education sector are highly recommended. It should also be noted that the quality of HE institutions cannot be defined by a single group of stakeholders. It is very important to investigate the perceptions of other stakeholders, such as the staff, employers, the government, or alumni. Therefore, a further research should focus on this set of problems.

Acknowledgements This research has been partially supported by the Czech Science Foundation grant project number 402/12/2147 and partially by the Internal Grant Agency of the University of Economics in Prague grant project number IG632093.

References Aly, N. and Akpovi, J. (2001) “Total quality management in California public higher education”, Quality Assurance in Education, Vol 9 No. 3, pp 127‐131. Benoit, J., Abdous, M. and Laferriere, T. (1998) “The emerging contribution of on‐line resources and tools to classroom learning and teaching trends in Higher education”, Report submitted to Rescol by Tele‐learning Network Inc.

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Petr Svoboda and Jan Cerny Kanji, G.K., Malek, A. and Tambi, A. (1999) “Total quality management in UK higher education institutions”, Total Quality Management, Vol 10, No. 1, pp. 129‐153. Kimani, S.W., Kagira, E.K., Kendi, L. (2011) “Comparative analysis of business students’ perceptions of service quality offered in Kenyan universities”, International Journal of Business Administration, Vol 2, No. 1, pp 98‐112.

Kotler, P. (1985) Strategic Marketing for Educational Institutions, Prentice‐Hall, London. Martensen, A., Gronholdt, L., Elkildsen, J.K. and Kristensen, K. (2000) “Measuring student oriented quality in higher education: Application of the ECSI methodology”, Sinergie rapporti di icercan, Vol 9. Michael, S.O. (1998), “Restructuring US higher education: analyzing models for academic program review and discontinuation”, Association for the Study of Higher Education, Vol 21, No. 4, pp 377‐404. Middlehurst, R. and Gordon, G. (1995) “Leadership, quality and institutional effectiveness”, Higher Education Quarterly, Vol 49, No. 3, pp 267‐285.

Parri, J. (2006) “Quality in higher education”, Vadyba/Management, Vol 2, No. 11, pp 107‐111. Petruzzellis, L., Duggento, A.M. and Romanazzi, S. (2006) “Student satisfaction and quality of service in Italian universities”, Managing Service Quality, Vol 16, No. 4, pp 349‐364. Sanchez, P.M., Gazquez Abad, J.C., Carrillo, G.M.M. and Fernandez, R.S. (2007) “Effects of service Quality Dimensions on Behavioral Purchase Intentions; A study in public sector transport”, Managing service quality, Vol. 17, No. 2, pp 134‐ 151. Svoboda, P., Voracek, J. and Novak, M. (2012) “Online Marketing in Higher Education”, In Knowledge Management. Cartagena: Universidad Politécnica, pp 1145‐1152, ISBN 978‐1‐908272‐64‐5. Tsinidou, M., Gerogianissis, V. and Fitsilis, P. (2010) “Evaluation of the factors that determine quality in higher education: an empirical study”, Quality Assurance in Education, Vol 18, No. 3, pp 227‐244.

Wallace, J.B. (1999) “The case for students as customers”, Quality progress, Vol 32, No. 2, pp 47‐51. Zeithaml, V.A., Bitner M.J. and Glemler D.D. (2009) Service Marketing, Integrating Customer Focus Across the Firm, 5th Ed., McGraw‐Hill publishing company, New Delhi.

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Business Clusters and Knowledge Management: Information Flows and Network Concepts Mariza Tsakalerou and Stefanos Katsavounis Department of Production Engineering and Management, Democritus University of Thrace, Xanthi, Greece mtsakale@pme.duth.gr skatsav@pme.duth.gr Abstract: Business clusters, as geographical concentrations of vertically and horizontally integrated firms in related lines of business, are extremely popular with policy‐makers. The basic premise behind the concept is that by clustering together firms can achieve economies of scale and scope and lower their business costs. Furthermore, clusters have been associated with innovation capacity and are assumed to confer competitive advantages to their members and their regions. It appears though that the case that clusters invariably boost business performance and local development is not conclusively proven and even the demonstration of a positive association between clusters and innovation capacity has not been consistent. Despite scant empirical evidence to support these claims, business clusters remain at the forefront of regional development policies. As ambiguities in defining clusters and identifying their members and their borders prevent accurate policy evaluation, the objective of this paper is to show that knowledge management mapping maybe the critical first step. Identifying the type of networks present and recognizing information flows within the cluster boundaries may allow for objective assessment of the wealth of data collected around the world. Success factors may then be shown to depend upon a multitude of issues, in that clusters seem to behave differently in different parts of the world, in different economies and in different stages of their development. Keywords: clusters; business networks; intellectual capital; knowledge management; innovation

1. Introduction Clusters are local concentrations of firms in related lines of business together with their supporting organizations. Local productive systems, industrial districts or business networks are examples of clusters and describe the tendency of firms in a particular field to concentrate geographically. By clustering together, it is assumed that firms can achieve economies of scale and scope and lower their business costs. The term business cluster, also known as an industry cluster, was introduced and popularized by Michael Porter in his book The Competitive Advantage of Nations (1990) as an extension of ideas of agglomeration economics presented in Alfred Marshall’s seminal work of the previous century, Principles of Economics (1890). In his own work (Porter 1998a), Porter has eventually defined clusters as geographic concentrations of interconnected businesses, suppliers, service providers and associated institutions in a particular field that compete but also co‐operate. Porter argued that a cluster is a form of network that occurs within a geographic location, in which the proximity of firms and institutions ensures certain forms of commonality and increases the frequency and impact of interactions (Porter 1998b). Key in this concept is the hypothesis that when enough resources and competences amass to reach a critical threshold in a geographical location, this confers a sustainable competitive advantage over other places in a given economic activity. Porter claimed that clusters have the potential to affect competition by increasing the productivity of the companies in the cluster, by driving innovation in the field, and by stimulating new businesses in the field. The business cluster concept has grasped the imagination of policy makers and proved extremely popular with governments eager to develop regional policies to promote employment and growth. In an era of globalization, where small and medium‐sized firms increasingly have to compete internationally, clusters can play an important role in supporting firm competitiveness by increasing productivity, innovation and firm formation and providing spill over effects to the entire geographical region. UNIDO defines business clusters as sectoral and geographical concentrations of enterprises that produce and sell a range of related or complementary products and, thus, face common challenges and opportunities (UNIDO 2001). Interestingly, UNIDO differentiates them from business networks which are defined as groups of firms that cooperate on a joint development project complementing each other in order to overcome common problems, achieve collective efficiency and penetrate markets beyond their individual reach.

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Mariza Tsakalerou and Stefanos Katsavounis Networks can be horizontal or vertical and they can be developed within or independently of clusters (UNIDO 2010). Over the years, the concept of Porterian clusters has evolved to include diverse types of agglomeration, yet a globally accepted definition of clusters remains elusive. For most of the world, OECD’s broad definition of clusters as geographical concentrations of vertically and horizontally integrated firms in related lines of business appears to be a convenient vehicle for policy‐makers (OECD 1999). Industrialised and developing countries alike have been pursuing cluster policies under this umbrella definition that allows for a wide range of activities to stimulate regional development for innovation, sustainability and growth (OECD 2001). Admittedly, a large part of the popularity of clusters lies in the vagueness and definitional elusiveness of the concept (Martin & Sunley 2003). It is precisely this ambiguity that allows both to apply the cluster concept to different realities and to prevent an accurate policy evaluation. Yet clusters have become a worldwide fad primarily because they have been associated with innovation and the knowledge economy (OECD 1999; OECD 2001). Most national innovation systems and policies from industrial districts to science parks and university research include clusters as an integral part of their arsenal. The evidence though of a positive association between clusters and innovation capacity is not consistent (Ferreira et al 2012; INNOVA 2008). Similarly, it appears that the case that clusters invariably boost business performance and local development is not conclusively proven (Temouri 2012). Despite these weaknesses, the popularity of the cluster concept amongst policymakers remains intact. Business clusters ride the policy trend of focusing on the distinct potential of individual regions. The reality of the case is that at this point there is not enough empirical evidence to support or reject clusters primarily because of the inherent ambiguities in defining clusters, identifying their members and recognizing their geographical borders. The purpose of this paper is to provide elements of a concise framework to assess cluster empirical evidence and thus enable cluster policy evaluation. The proposed framework is based upon the two themes that are integral to the concept of clusters in all cases:

Geography (spatial proximity)

Member linkages (relational proximity)

Both themes are in essence dimensions of business proximity. Information flows within clusters through the networks established and affects directly the quality and quantity of the benefits of their members and their regions.

2. Spatial proximity It has been well established since the last century that economic activity tends to agglomerate over time on a national, regional or urban scale. The observed concentration of economic activity in an area does not necessarily constitute a cluster. Porter’s original definition gave rise to a multitude of interpretations (Martin & Sunley 2003), which either extend it to include a wider variety of possible members or reduce it to local supply chain relations alone. Although all interpretations assume that geographical location is a defining characteristic of cluster activity, none of them defines the spatial scale on which such specialized activity should be construed as a cluster. Specialization, concentration and agglomeration are all assumed to confer competitive advantage and the corresponding terms have been used with significant overlap in the literature. It is important however to distinguish between these concepts, as they are not identical. Figure 1, adapted after (Brakman & Marrewijk 2013), summarizes how these three terms differ. Specialization is assumed to be sectorial in nature and can take place with or without any spatial clustering. Concentration and agglomeration are concerned with spatial clustering. Concentration refers to the regional clustering of a limited number of well‐defined sectors, whereas agglomeration refers to the geographical clustering of a broader set of economic activities. Porter’s cluster definition is closely related to the concepts of concentration and agglomeration.

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Figure 1: The cases of spatial proximity ‐ specialization, concentration and agglomeration From Figure 1, it is clear that if an economic activity is not homogeneously spread over space, a boundary can always be drawn to define a potential cluster. Even Porter admits that drawing cluster boundaries involves a creative process (Porter 1998a). The most popular spatial scale for cluster policy is at the regional level, but the number of clusters reported by relevant organizations is often conflicting severely (Martin & Sunley 2003; Crawley & Pickernell 2012). The issue of spatial proximity is essential in assessing cluster performance and regional policies and it is imperative that an objective metric be developed to gauge cluster boundaries.

3. Relational proximity It would appear then that defining the geographical boundaries of clusters requires merely the consolidation of specialized economic activity without necessarily paying attention to the linkages between the actors involved. Yet Porter insists that cluster boundaries should encompass all firms with strong linkages and should safely leave out those with weak and non‐existent linkages (Porter 1998b). The problem of course is in differentiating between strong and weak linkages and no appropriate definitions have ever been provided. To complicate matters more, it appears that member relations within a broadly defined cluster can be of two topologies (Wall & Knaap 2011):

Hierarchical or star‐based, where a major company dominates the cluster.

Heterarchical or universal, where the members are more or less equivalent.

Hierarchical structures are typical of industrial clusters where one or two major companies are connected to their supply chain partners. Heterarchical structures on the other hand are more typical of service clusters where a lot of similar companies cooperate and compete with each other (Todeva & Knoke 2005). The existence of linkages of courses does not automatically imply a clear understanding of their strength. Thus the issue of relational proximity remains as vague as that of geographic location and the definition of a cluster.

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Figure 2: Network structures

4. Knowledge management The presentation up to this point has elucidated the problems present in defining clusters, in assessing their performance and in developing coherent, science‐based policies. But this is not a polemic on clusters; rather it is an attempt to establish the groundwork for a more sober analysis. If the concept of clusters has any merit, the critical factor is in the spatial and relational proximities. In an era of globalization, land‐based advantages that have to do with labor, natural resources, taxation schemes and infrastructure costs quickly diminish. Apparently there is another distinct dimension of proximity that may make all the difference. A number of researchers have theorized that the advantage of clusters, if there is one, has to do with knowledge management and the flow of information in business networks (Sureephong et al 2007; Christopherson, Kitson & Michie 2008). Indeed, many see as the fundamental characteristic of the contemporary knowledge‐based economy the production, dissemination and absorption of knowledge (Diaz‐ Perez, Aboites & Holbrook 2012; Charoensiriwath 2009). Within this framework, there is a crucial distinction between knowledge that can be codified using symbolic forms of representation (explicit knowledge) and other forms of knowledge that defy this representation (tacit knowledge). Tacit knowledge is recognized as a pillar of the learning economy and for a number of scholars provides the explanation for the persistence of local clusters in spite of a globalizing environment and a networked world (Gertler 2003; Cong & Weng 2011). The assumption is that firms located in clusters benefit from local knowledge spill overs. Empirical data on the knowledge advantage of clusters remains ambiguous and further research is needed (Cai, Lian & Li 2009; Huber 2012).

5. Research hypothesis and methodology Our research aims to examine the validity of the following main research hypothesis: The competitive advantage of clusters is relational and not necessarily spatial. Spatial proximity enhances knowledge transfer and is thus a sufficient condition but not a necessary one. Modern modalities of communication and information dissemination may lead to a scenario where operating in the same geographic area is not a mandatory pre‐condition of cluster success.In order to certify this hypothesis we have to account for the variables of industry sector (service vs. manufacturing), company size (big vs. small) and geographic location (developed vs. developing countries) that appear to be the major discriminants of knowledge management praxis. The proposed methodology is based on an exhaustive literature search to identify relevant empirical quality studies, classify the results across the manufacturing/services, big/small and developed/developing economies axes and the design of a statistically valid meta‐study. Identification of initial benchmarking standards from the results of previous studies will determine the appropriate quantification of the variables defined above. This is multi‐disciplinary research on knowledge‐based development built upon a unified framework of the cluster

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Mariza Tsakalerou and Stefanos Katsavounis concept in combination with the intellectual capital theory. At baseline, the research project will examine the evolution of the intellectual capital in selected clusters, regions and countries around the world (typically, in Europe and Asia) to gain perspective on the features that have to be studied further and on those that could be ignored in the first phase of the analysis. The research question will be further refined after the literature review and baseline study, with the objective of studying the impact of knowledge innovation zones and knowledge city experiments around the world to test the hypothesis of their importance for regional development. Finally, policy recommendation within the proposed intellectual capital framework will be analyzed with the objective of identifying the optimal global‐regional‐local knowledge that has to be achieved to reap developmental benefits. The deliverables of the research include:

a methodology on the measurement and management of the intellectual capital of regional clusters;

adaptation and implementation of this methodology to a larger scale (regions and/or countries) in order to achieve value creation and development;

a framework on how to measure the effects on regional intellectual capital of knowledge cities and knowledge innovation zones; and

6. In conclusion Business clusters have been associated with innovation capacity and are assumed to confer competitive advantages to their members and their regions. The current thesis is that clusters confer distinct advantages to their members via knowledge management, knowledge spill overs and intellectual capital primarily based on tacit knowledge. Despite mixed empirical evidence to support these claims, business clusters remain at the forefront of regional development policies. As ambiguities in defining clusters and identifying their members and their borders prevent accurate policy evaluation, the objective should be to develop well‐designed, well‐monitored pilot studies so that a body of evidence can be amassed and used for meta‐studies. It is apparent that there will not be a single unifying theory for business clusters. Success factors depend upon a multitude of issues and clusters seem to behave differently in different parts of the world, in different economies and in different stages of their development. The major demarcation lines appear to be:

service vs. manufacturing (as specialization affects cluster structure (Todeva & Knoke 2005),

European vs. Asian clusters (primarily on cluster policy issues (Pessoa 2012), and

small and medium enterprises vs. multinational corporations (as they have distinct needs in terms of tax treatment and competition laws (Hui & Yang 2008).

The significant database of case studies on knowledge management in business clusters that has evolved over the recent years in China (Chen, Guo & Shi 2007; Yang & Zhu 2010; Diaz‐Perez, Aboites & Holbrook 2012; Mo, Hu & Sun 2007; Cai, Lian & Li 2009; Liu & Liu 2010) is a step in the right direction and warrants particular attention for in‐depth comparative studies.

Acknowledgements This work was supported in part through a grant from the Technical Chamber of Greece.

References Brakman, S. & Marrewijk, C.v. (2013) “Reflections On Cluster Policies”, Cambridge Journal of Regions, Economy and Society. Available from Oxford Journals [March 12, 2013]. Cai, Y., Chen, G. & Zhang, Q. (2009) “Evaluation of Clustered Enterprises' Knowledge Management Based on Weighted Grey Relational Model”, Proceedings of the ΙΕΕΕ International Conference on Grey Systems And Intelligent Services, pp. 792‐796. Charoensiriwath, C. (2009) “Analyzing Intellectual Capital Cluster Index in Thailand's Hard Disk Drive Cluster”, Proceedings of the IEEE International Conference on Management of Engineering & Technology, pp. 200‐204. Chen, J., Guo, F. & Shi, C. (2007) “On Evaluation of Knowledge Sharing Risks in Virtual Enterprises Based on Grey Cluster Analysis”, Proceedings of the IEEE International Conference on Grey Systems and Intelligent Services, pp. 323‐329.

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Mariza Tsakalerou and Stefanos Katsavounis Christopherson, S., Kitson, M. & Michie, J. (2008) “Innovation, Networks and Knowledge Exchange”, Cambridge Journal of Regions, Economy and Society, Vol. 1, pp. 165‐173. Cong, H. & Weng, H. (2011) “The Research on “Explicit of Tacit Knowledge” in the Industrial Cluster Based on Knowledge Management”, Proceedings of the IEEE International Conference on Computer ScienceaAnd Service System, pp. 886‐ 889. Crawley, A. & Pickernell, D. (2012) “An Appraisal of the European Cluster Observatory”, European Urban and Regional Studies, Vol. 19, No. 2, pp. 207‐211. Diaz‐Perez, C., Aboites, J. & Holbrook, A. (2012) “Intellectual Property Strategies, Collaboration and Technological Capabilities: The Fuel Cell Cluster in Vancouver”, Proceedings of the IEEE Conference on Technology Management for Emerging Technologies, pp. 947‐957. Ferreira, M.P., Sierra, F.R., Costa, B.K., Maccari, E.A. & Couto, H.R. (2012) “Impact of the Types of Clusters on the Innovation Output and the Appropriation of Rents from Innovation”, Journal Of Technology Management and Innovation, Vol. 7, No. 4, pp. 70‐80. Gertler, M.S. (2003) “Tacit Knowledge and Economic Geography of Context”, Journal of Economic Geography, Vol. 3, pp. 75‐99. Hou, L. & Parrilli, D.M. (2009) “SME Cluster vs. Multinational Companies: Similarities and Differences for Tax and Competition Lawmakers”, International Journal of Private Law, Vol. 2, No. 4, pp. 400‐411. Huber, F. (2012) “Do Clusters Really Matter for Innovation Practices in Information Technology? Questioning the Significance of Technological Knowledge Spillovers”, Journal of Economic Geography, Vol. 12, pp. 107‐126. Hui, L. & Yang, Z. (2008) “Empirical Research on Sustainable Development of Industrial Cluster”, Proceedings of the International Conference on Risk Management & Engineering Management, pp. 553‐558. Europe INNOVA (2008) “Case Studies of Clustering Efforts in Europe: Analysis of their Potential for Promoting Innovation and Competitiveness”, European Presidential Conference on Innovation and Clusters. Liu, M. & Liu, Y. (2010) “Core Competence Evaluation Of Modern Service Industry Cluster”, Proceedings of the IEEE International Conference on Management and Service Science, pp. 1‐4. Martin, R. & Sunley, P. (2003) “Deconstructing Clusters: Chaotic Concept or Policy Panacea?”, Journal of Economic Geography, Vol. 3, No. 1, pp. 5‐35. Mo, L., Hu, B. & Sun, W. (2007) “Research on Collective Learning Mechanism of Industrial Cluster: The Case of Wuhan Optics Valley”, Proceedings of the IEEE International Conference on Grey Systems and Intelligent Services, pp. 1685‐ 1690. Organization for Economic Cooperation and Development (2001) Innovative Clusters: Drivers of National Innovation Systems. OECD Directorate for Science, Technology and Industry, Paris. Organization for Economic Cooperation and Development (1999) Boosting Innovation: The Cluster Approach. OECD Directorate for Science, Technology and Industry, Paris. Pessoa, A. (2012) “Regional Cluster Policy: The Asian Model vs. the OECD Approach”, MPRA Paper No. 42024, available from Munich Personal RePEc Archive [18 October 2012]. Porter, M.E. (1998a) “Location, Clusters and the ‘New’ Microeconomics of Competition”, Business Economics, Vol. 33, No. 1, pp. 7‐17. Porter, Μ.Ε. (1998b) “Clusters and the New Economics of Competitiveness”, Harvard Business Review, pp. 77‐90. Shi, Y., Qu, L. & Tian, Z. (2012) “Knowledge Stock Measurement of Enterprises in Industry Cluster Based on Grey Relational Analysis”, Proceedings of the IEEE International Conference on Fuzzy Systems and Knowledge Discovery, pp. 816‐820. Sureephong, P., Chakpitak, N., Ouzrout, Υ., Neubert, G. & Bouras, A. (2007) “Knowledge Management System Architecture for the Industry Cluster”, Proceedings of the IEEE International Conference on Industrial Engineering and Engineering Management, pp. 1970‐1794. Temouri, Y. (2012) The Cluster Scoreboard: Measuring the Performance of Local Business Clusters in the Knowledge Economy. LEED Working Papers, OECD Publishing, Paris. Todeva, E. & Knoke, D. (2005) “Strategic Alliances and Models of Collaboration”, Management Decision, Vol. 43, No. 1, pp. 123‐148. UNIDO (2010) Cluster Development for Pro‐Poor Growth: The UNIDO Approach. UNIDO Business, Investment And Technology Services Branch, Vienna. UNIDO (2001) Development of Clusters and Networks of SMEs: The UNIDO Program. UNIDO Private Sector Development Branch, Vienna. Wall, R. & Knaap, B.v.d. (2011) “Sectoral Differentiation and Network Structure within Contemporary Worldwide Corporate Networks”, Economic Geography, Vol. 87, No. 3, pp. 267‐308. Wang, X. & Jiang, Y. (2010) “Analysis on Mechanism of Knowledge Management in Forming Innovative Clusters”, Proceedings of the IEEE International Conference on Information Science and Management Engineering, pp. 501‐505. Yang, T. & Zhu, Y. (2010) “Formation of Sustainable Competitiveness of Native‐Type Industrial Cluster from the Perspective of Knowledge Management Innovation”, Proceedings of the IEEE International Conference on Information Management and Engineering, pp. 612‐615.

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An Analysis of Mobile Applications for the Purpose of Facilitating Knowledge Management Serkan Varol and Ryan Underdown Lamar University, Beaumont, Texas, USA Serkanvarol23@gmail.com Ryan.underdown@lamar.edu Abstract: The purpose of this paper is to understand the extent to which a group of selected mobile applications facilitate knowledge management (KM). The proliferation of mobile devices is changing the way people enter and access information. Mobile applications facilitate quick and easy access to information. Users can enter and access information from almost any location on a wide variety of hardware. The research question is: “to what extent do mobile applications facilitate knowledge management”? In this study, several android applications are chosen based on their similarity and popularity. Rubrics based on sound KM principles were developed to analyze the extent to which these selected applications facilitate KM. A small group of faculty and students evaluated the mobile applications using the rubrics. The results of this preliminary study are presented. Results of this study will be valuable to those companies seeking to use mobile applications as part of their knowledge management strategy. Keywords: knowledge sharing, mobile applications, mobile devices, android

1. Introduction One of the goals of this paper is to streamline understanding of advancements in knowledge management field. Managing knowledge requires a series of disciplines supported by technological devices (Capozzi, 2007). Nowadays, the development stages of knowledge management may not be traceable due to continues innovations in intangible computerized platforms. Determinants of organizational growth are firmly depending upon managing and utilizing knowledge in sophisticated internet era. Latest Mobile devices lead the way to creativity in knowledge management, thus performance and productivity increase in organizations. In this study, various android applications are categorized by their means of use in knowledge management platform. Knowledge management is one of the most demanding topics in organizations. Many companies seek instant knowledge sharing and transfer to overtake their competitors (Alavi & Leidner, 2001). Since we have been introduced to knowledge management (KM) discipline in 1991, larger organizations have built their business strategies partially based on KM principles. These principles are not well established and altering towards the best practice of an organization. Considering the fact that all companies aim to increase the productivity while reducing the costs, significance of decision making becomes more important for all KM principles. Instant judgments may bring accurate decision makings. However, it is very unlikely to make right decisions without getting fed with healthy information. Nowadays, the competition level between same types of organization is reasonably high due to advancements in technology. The convection of explicit knowledge is speedy and more accessible than before. Thus, investments may be riskier but also more accurate, thanks to the rapid developments in gathering information on electronic platform.

2. Technology and knowledge management Technology has been continuously improving, and contributing a lot to industry. There is no doubt that internet is the key part of the emerging technology. Human being was initially introduced to cyber world when a message was sent to ARPANet from a computer science Professor Leonard Kleinrock's laboratory at University of California, Los Angeles (UCLA) in 1960s. As Internet usage grew through the 1980s and early 1990s, data distribution was the primary issue for the sector. Companies invested big money into important projects to find an efficient way to send and receive data. The milestone of web browsing began with the introduction of NCSA mosaic which was implemented from FTP, NNTP and gopher protocols .Recently, fiber technology that can move data at 99.7 percent of the speed of light has just been announced by the research team from the University of Southampton in England. Fiber cable sends 37 streams of 40 gigabits per second each, with a capacity of 1.48Tbps (The first internet connection, with ucla's leonard kleinrock, 2013). This enhances maximum data transfer between computers.

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Serkan Varol and Ryan Underdown Introduction of knowledge management discipline intersect with the development of web browsing system as they both initialized in the early years of 1990s. Even though KM is still a fairly new concept for public, its evolution was rapid but also inevitable along with the advancements in computer systems. However, KM components are not limited to computers and technology (Maier, 2007). It should also cover people, culture, processes and some other structural concepts such as leadership and motivation (Natemeyer, 2011)

3. Research project In this paper, the relationship among human being, knowledge and technology are deeply investigated and illustrated with rubrics. According to the base literature of KM, there are two fundamental concepts of knowledge management; tacit and explicit. The operative transfer of tacit knowledge is depending upon personal interaction and social networks. Although people are living the in the world where they interact with tacit knowledge daily, it is not very easily articulated as cultures, ideals, and other factors play role for granted. Language of tacit knowledge varies from shapes to words. As long as the information is transmitted and implementation of the knowledge is illustrated, tacit knowledge acknowledges any practical notifications (Leonard & Sensiper, 1998). Due to knowledge management principles are countless, it is hard to set up a set of principles that guidelines knowledge management. In mobile knowledge management, the principles are more clear and understandable because objectives are obvious (Derballa & Pousttchi, 2007). In this research, we have determined four fundamental principles based on users’ demand; Knowledge Acquistion, Preservation, Development, Distribution/Sharing. This four phase method was created by combining all popular methods under a roof and implement them into our needs for the new model. Knowledge acquistion is catogorized in four different sections (table 3); data source , data variability, accessibility and online or offline accessibility to saved data. In data source, the location of the gathered information is conducted and seperated from other sources. Data variability determines the type of obtained data. Data can be obtained in different mehtods such as a written document, JPEG file or voice. Some of these sources are not compatible with certain android applications. This happens to be a crucial drawback when it comes to upload uncompatible files to the cloud. Accessibility matters the optimal point of gaining accurate information in a short time. The purpose of this feature is to save time and give users more freedom on using the apps. Development phase begins with event tracking (table 4_Figure_4). The system must be tracked and give alarm when needed. One of the common issues with mobile KM applications is that there is no warning alert which is often preffered by users at larger organizations. Document editing/tagging is another important step of our phase 2. The user must be able to edit somebody else’s files and share it with other friends if needed. This feature is more appropriate to organizations that have field engineers at different locations. It will enable the instant decision making with more accurate outcome. Knowledge sharing/distrbution (table 5) is the core of four principles. Sharing methods is the initial step of this phase.The user may want to share information on more than one platform. This can be done by simultanous distribution. Information can be private to some instiutions and individuals, thus appropriate settings must be done to increase the level of privacy at organization. Free accounts come with limited sharing limits. This can be prevented by subscribing on to paid plans offered by the applications’ providers. However, most consumers (individuals) would stay with free versions and expect the most out of it .Also, some applications restrict the daily data sharing which is a handicap for most users. Thereofore, the allowance on limits is as important as the value of the data. In data preservation (table 6), storage of saved data is conducted. Many applications save data on web clouds. This is found risky when there is no internet connection. Thus, offline accessibility must be provided as mentioned in our phase 1.Storage limit is as important as data limit.In spite of the fact that there are many website that offer free spacing, most of them have requirements that uncomfort users. In this study, four android applications; google drive, dropbox, evernote, skydrive are chosen based on their similarity and popularity. Rubrics based on sound KM principles were developed to analyze the extent to which these selected applications facilitate KM. A small group of faculty and students evaluated the mobile applications using the rubrics. The results of this preliminary study are presented in this research. Results of this study will be valuable to those companies seeking to use mobile applications as part of their knowledge management strategy. As it can be seen on our comparison table below (table 1), we’ve determined 12 criteria based on what is seen more important on an application in order to compare the features of apps in different categories. Thus, each criteria is linked to a type of a source (principle) and associated with official data from

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Serkan Varol and Ryan Underdown companies’ websites. This table is given to give reader a pre‐opinion while evaluating rubric surveys of participants. Table 1: Comparison table

3.1 Rubrics research 44 people (31 male, 13 female) participated in our rubric survey. The selection was random among college students who aged between 20‐22. The users were demanded to use an android compatible device and given 5 days to complete the rubrics. The rubrics seek for certain answers as well as personal opinions about specific elements of each element, so the applications. In brief, we asked students to concentrate on what they are examining on applications rather than the written features that can be found online. On the other hand, measuring the quality of rubrics was one the priorities of this research. Therefore, an exclusive rubric (table 2) was created to examine different aspects of our four phase rubrics. By doing so, we aim to improve quality of our rubrics for future use. Table 2: Rubric

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Serkan Varol and Ryan Underdown Each element of our four phase rubrics was evaluated by taking the average score of all 32 participants. Numbers are rounded up to nearest fractional value. The total points are accumulated under each principle to assign a winner for the rubric.

3.2 Rubrics results Based on the results of the knowledge acquisition (table 3) phase which is also our initial principle in our KM model, Evernote is the leading apps in acquisition section of our first rubric. Menu commands on Dropbox seemed complicated to some users. Evernote and Dropbox have similar features in terms of data variability. Both apps support fundamental formats and offer service on various devices. Users think that all four apps have similar appearances in the form of data source. Multiple access points and simultaneous activities from multiple devices are essential features as they are valuable to organizations and companies Table 3: Knowledge acquisition

In the development/editing phase of the rubrics (table 4), the scores are close to each other in most elements. This clearly shows that all four apps carry similar characteristics and offer comparable features. Evernote and Skydrive are the most preferred applications of this phase. Both are leading in events tracking and simultaneous document editing whereas Google drive and Dropbox stay behind their competitors in these areas. Events tracking is a major component of our development KM principle. The user must be able to keep track of past and future events and edit them when needed. This is also known as an organization skill that is an infrastructure of managing knowledge. An important step of our knowledge management apps model is that the user must be able to share and distribute links (table 5) if necessary. Sharing methods vary depending on the type, size and load of the file. All four apps satisfy the needs of sharing methods. They are all compatible with major networking services such as facebook and twitter. However, the user can not share some formats such as voice and other documents with different formats. This creates an issue when it comes to sharing files. The participants think that distribution and sharing methods phase address individuals rather than professionals who work for larger organizations. Dropbox is the best application in this phase our rubrics.

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Serkan Varol and Ryan Underdown Table 4: Development/editing

Table 5: Distribution/sharing

Preservation (table 6_Figure_6) is the last phase of our KM model. It consists of only two elements: storage access location and file limit. Evernote has a monthly limit of data limit on its servers. Even though this is a big handicap for some users who utilize Evernote for a business purpose, the limit is only satisfactory for most individuals. Skydrive offers more storage than its competitors which can be an advantage for users who demand free version of these applications but also ask for the most storage unit.

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Serkan Varol and Ryan Underdown Table 6: Preservation

We also used a rubric to grade other rubrics (table 2). Depending on the numeric results of this rubric, participant expect better description in categories and demand more details in grading system. Thus, we will consider all these facts for the future use to improve the quality on rubrics.

4. Conclusion and future work Mobile applications facilitate quick and offer easy access to information. Users can enter and access information from almost any location on a wide variety of hardware. The usage of mobile devices has sharply increased in past 10 years. The most helpful feature of these devices is to access needed information wherever and whenever necessary. “The moment of truth” has become more meaningful in mobile platforms. For instance: People are no longer going to banking branches to make a deposit of a check. Newer mobile apps help consumers to deposit a check as long as the internet connection is available. One other example is radio stations. Cell phones with radio tuner features were among popular devices about ten years ago. Now, radio stations offer free mobile apps on certain platforms such as android, IOS. The connection between knowledge management and technology strengths parallel to the development of technology In our research, we have reviewed a number of applications to determine to what extend they facilitate knowledge management, based upon the participants’ opinions and reviews, as well as the authors’ assessments. Knowledge management concept is an intangible asset that is utilized daily. It is a need for human being and environment as it addresses and offers solution for many issues that we carry. The knowledge acquisition table clearly (table 3) shows that the results of rubric indicates a strong relationship between the application and our knowledge management model. It facilities KM by offering variety methods in data gathering and flexibility in data source selection. Improving the quality of data develops the core opinion and promote it to more advanced level of knowledge. The reviewed applications offered some ways of sharing and distributing information which also facilitate knowledge management by increasing the speed of transformation of the tacit knowledge to explicit knowledge. To sum up, knowledge management is a broad topic that is meant to address different needs at assorted situations. Distinguishing reviewed applications and launching a winner out of four applications requires more research and effort as it may need more involvement and diversity in selection of participants. By looking at the results of rubrics, we can claim that all applications facilitate knowledge management in the fields of

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Serkan Varol and Ryan Underdown knowledge acquisition and development. However, sharing and preservation phases stand apart from our knowledge management model due to lacking in sharing and storage limits of units. Fortunately, technology has the potential and power of solving all these issues as long as new innovations arise and release limits freely.

References Alavi, Maryam and Leidner, Dorothy E.. (2001). "Review: Knowledge Management and Knowledge Management Systems: Conceptual Foundations and Research Issues,"MIS Quarterly, (25: 1) Capozzi, M. M. (2007). "Knowledge Management Architectures Beyond Technology". First Monday 12 Derballa, V, Pousttchi, K, (2004). “Extending knowledge management to mobile workplaces”, Proceedings of the 6th international conference on Electronic commerce. Delft, The Netherlands, 583‐590. Grove, IL: Waveland Press, Inc The first internet connection, with ucla's leonard kleinrock. (n.d.). Retrieved from http://www.internet‐history.info/media‐library/mediaitem/183‐the‐first‐internet‐connection‐with‐ucla‐s‐leonard‐ kleinrock.html Krogh, G. V. (1998). “Care in Knowledge Creation.” California Management Review 40(3): 133‐153 Leonard, D. and S. Sensiper (1998). “The Role of Tacit Knowledge in Group Innovation.” California Management Review 40(3): 112‐132. Maier, R. (2007). Knowledge Management Systems: Information and Communication Technologies for Knowledge Management. Berlin: Springer. Natemeyer, W.E., & Hersey P. (2011). Classics of organizational behavior (4th ed.), Long

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The Implications of Tacit Knowledge Utilisation Within Project Management Risk Assessment Paul Woolliscroft1, Marcin Relich2, Dagmar Caganova1, Milos Cambal1, Jana Sujanova1 and Jana Makraiova1 1 Faculty of Materials Science and Technology, Slovak University of Technology, Trnava, Slovakia 2 Faculty of Economics and Management, University of Zielona Gora, Zielona Gora, Poland paul.woolliscroft@stuba.sk m.relich@wez.uz.zgora.pl dagmar.caganova@stuba.sk milos.cambal@stuba.sk jana.sujanova@stuba.sk jana.makraiova@stuba.sk Abstract: The capabilities of an organisation to create, share and utilise knowledge effectively are today regarded as one of the key drivers of competitive advantage for industrial enterprises, predominately for those operating within increasingly globalised marketplaces. The elevated significance of knowledge management is particularly evident in the context of Slovakia due to the rapid influx of large‐scale industrial enterprises since the country joined the EU and the necessity to utilise sector specific knowledge. The knowledge held within an organisation can typically be categorised as either explicit or tacit. Explicit knowledge relates to information which is formal and systematic; it can be easily communicated and shared throughout the organisation, and embodied in a computer program or set of procedures. In contrast, tacit knowledge relates to human interactions and is regarded as “highly personal” and not easily formalised or standardised. The terms tacit and explicit knowledge directly reflect the two dominant perspectives on knowledge management relating to “people” and “technology”. From a tacit, “people” perspective, the strategy is to develop knowledge sharing between individuals, and while IT is useful it is often peripheral to issues. From the “technology” perspective, the strategy is to capture, codify, store, and distribute knowledge through the use of IT systems. Both perspectives assert that knowledge is a critical asset for the organisation and the delivery of projects. The primary aim of the paper is to adopt a cross‐ disciplinary approach to knowledge management by assessing the implications and utilisation of explicit and tacit knowledge in the context of project management risk assessment. Within project management, evaluating the opportunities and risks of a new project proposal is a complex process including objective and subjective factors, not only in the process itself but also in the selection of the data used to support or justify the evaluation. The systematic nature of project management ensures that practitioners and academics alike often prefer explicit knowledge which can be expressed by defined scientific or technical principles. In contrast the ability to quantify and articulate the role of tacit knowledge and the impact of human capital within the process remains highly problematic. The paper aims to address the issue through the creation of a reference model of organisation and project management which codifies knowledge in terms of fuzzy set theory. The constraints are specified in the form of fuzzy rules that develop the knowledge base. The main purpose for the use of fuzzy rules is that human interaction has many characteristics which are difficult to quantify and measure precisely. The proposed approach therefore allows for the codification of explicit and tacit knowledge and a new model is presented which integrates both explicit and tacit knowledge as measures within the project risk assessment process. Keywords: knowledge management, project management, project risk assessment, tacit knowledge

1. Introduction Knowledge management coordinates the processes of creating, acquiring, capturing, organising, storing and distributing information within an enterprise to make it available to others, and it can be understood as complex, multilayered and multifaceted (Blackler, 1995). Knowledge may be divided into two forms: explicit and tacit (Nonaka, 1991). Explicit knowledge is formal and systematic, and therefore it is easily communicated and shared throughout the firm. For example, explicit knowledge is embodied in a computer programme or set of procedures. Large firms demonstrate many examples of explicit knowledge, indicated by their complex administrative procedures and controls. Smaller firms, on the other hand, are often defined by the opposite with many decisions suggesting an absence of formal project management methods and practices (Currie, 2003).

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Tacit knowledge is defined as “highly personal” and not amenable to formalisation and standardisation, as well as not easily communicated to others. Furthermore, tacit knowledge is deeply rooted in action and in an individual’s commitment to a specific context – a craft or profession, a particular technology or product market, or the activities of a work group or team. Tacit knowledge consists partly of technical skills – the kind of informal, hard to pin down skills captured in the term ‘‘know‐how’’. A master craftsman after years of experience develops a wealth of expertise ‘‘at his finger‐tips’’. However, he is often unable to articulate strictly the scientific or technical principles behind what he knows (Nonaka, 1991). This is the motivation to consider fuzzy set theory as an attractive tool for the assessment of project risk, since it incorporates imprecision and subjectivity into the model formulation and solution process. Most companies have to deal with uncertain risks and uncertain expected performance in every aspect of their internal and external environments. The management of uncertainty is seen as a necessary condition for effective project management. Sources of uncertainty are wide ranging and have a fundamental effect on projects and project management (Atkinson et al, 2006). Uncertainty is an important issue in the support of any decision‐making in the process of new product development. In a company, this process involves objectives and information concerning schedule, financial, design or communication issues. Uncertainty can be defined in several ways. Essentially, it is a lack of information, which may or may not be obtainable (Rowe, 1994). Uncertainty is also linked with risk based on the distinction between aleatory and epistemic uncertainty in the following couplet: uncertainty is immeasurable risk; risk is measurable uncertainty (Hillson, 2004; Olsson, 2007). The term risk has different meaning to different people according to their viewpoint, attitudes and experiences. Engineers, designers and contractors view risk from the technological perspective, whereas lenders and developers tend to view it from the economic and financial side (Baloi and Price, 2003). The paper adopts a cross‐disciplinary approach to knowledge management through the application of tacit knowledge management in the context of project management risk. It is proposed that through the practical measurement of tacit knowledge within projects, the process can more accurately forecast the role of intangible or fuzzy elements within the overall project risk assessment. The structure of this paper is organised as follows: Section 2 explores the role of knowledge and the transition from tacit to explicit knowledge. Section 3 and 4 presents a new project risk identification model for product development. The new proposed model of risk identification takes into account risk factors connected with external environment, schedule, finance, design, and organisational structure. The proposed tacit knowledge risk factor includes communication and culture issues which are often omitted in project risk assessment. In section 5, the risk assessment methodology is shown, which clearly indicates the methodology for measuring tacit knowledge risk in explicit terms. This process within risk project management is based on fuzzy set theory and is especially useful to express vague, linguistic terms such as high risk, very complex projects, and insufficient communication in project team. An illustrative example of the approach is presented in Section 6. Lastly, the concluding section addresses the significance of tacit knowledge in the risk assessment process and proposes the future research direction.

2. Transition from tacit to explicit knowledge It is widely acknowledged by authors that knowledge can be classified in two forms. Firstly, explicit knowledge, which can be expressed clearly as numbers and figures and secondly, tacit knowledge, which is highly personal and hard to formalise (Nonaka and Takeuchi, 1995). Holden and Glisby (2011) developed the notion of knowledge further stating that both theorists and practitioners agree that knowledge is one of the most important of all intangible resources for firms. Moreover, whilst technology and globalisation have created a fast‐track to cross‐cultural interactions, it is the deep understanding of tacit knowledge, or the ability to create and share knowledge across cultural boundaries which is key to competitive competence for companies (Holden and Glisby, 2011). The key to unlocking the full value of tacit knowledge is to transfer tacit knowledge into explicit knowledge (Holden, 2013; Nonaka and Takeuchi, 1995). Thus tacit knowledge relating to culturally specific elements can be understood and operationalised in the same way as explicit knowledge. Holden (2013) further argues that only once tacit knowledge becomes explicit, can it then be understood and elevated to a strategic level in the organisation. Nonaka and Takeuchi (1995) conceptualised this process of knowledge transition through the proposed “Spiral of Knowledge Creation”, depicted in Figure 1. The model suggests the

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Paul Woolliscroft et al. process consists of four phases with knowledge transcending through each stage. In order for tacit knowledge to transfer to explicit knowledge firstly, the process of “socialisation” must take place whereby tacit knowledge it shared between individuals. The second phase is the process of “externalisation”, whereby the tacit knowledge is translated into comprehensible forms that can be understood by others. This stage is regarded as the most significant in the context of project risk management because intangible tacit knowledge is interpreted and converted into tangible measurable aspects. As a result the above mentioned two stages of the transition process will be the focus of the paper. The “Spiral of Knowledge Creation” is completed with the two stages of “combination”, whereby explicit knowledge is analysed and interpreted to a deeper extent and lastly, “Internalisation” whereby explicit knowledge is explained in tacit terms, or the applicability of explicit knowledge is interpreted in a context pertinent to the individual.

Tacit knowledge

Explicit knowledge

Tacit knowledge

Explicit knowledge

Figure 1: Spiral of knowledge creation (Nonaka and Takeuchi, 1995)

3. Identification of project risk assessment Project risk is defined by Project Management Body of Knowledge (PMBOK) as an uncertain event or condition that, if it occurs, has a positive or a negative effect on a project objective (PMI, 2013). PMBOK included risk management as one of the nine areas in project management and described it as the process concerned with conducting risk management planning, identification, analysis, monitoring, and control on a project. Risk management is often considered in the aspect of risk identification and assessment. Risk identification refers to the evidence from previous experience or similar cases which would apply to the current project, in order to increase the probability of the project’s success. After the identification a list of risk events that can probably occurred in the process of project performance, these risks are assessed. Risk assessment involves measures, either conducted quantitatively or qualitatively, to produce the estimation of the significance level of the individual risk factors to the project. With a better quantification measuring result, the managers can recognise which risks are more important and then deploy more resources to eliminate or mitigate the expected consequences (Karimi et al, 2011). The identification and assessment of project risk are the critical procedures for project success, and they usually become the essential factors in the decision‐making process (Williams, 1995). Several methods have been proposed to evaluate and select the best projects in order to decide which projects are more risky. Risk assessment methods have ranged from the classical quantitative methods (e.g. Monte Carlo methods, sensitivity analysis, critical path method, fault and event tree analysis) to fuzzy approach mathematical models (Relich, 2012). Using fuzzy logic, sets may be defined for vague, linguistic terms such as high risk, very complex project, poor performance of a subcontractor. These terms cannot be defined meaningfully with a precise single value, but fuzzy set theory provides a means by which these terms may be formally defined in mathematical logic (Carr

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and Tah, 2001). Project managers make decisions on the basis of their knowledge of the facts and personal experience. Their judgments and preferences are often vague, inexact, imprecise and uncertain by nature which makes it difficult to estimate their preference with an exact numerical value since crisp data are inadequate to model real life situations.

4. Project risk model for new product development Risks concerning new product development can be categorised in a number of ways based on the source of risk, impact of risk or by project phase. For instance, project risks are divided into two groups, according to their source, into internal and external. External risk concerns the attitudes of clients, availability and performance of subcontractors, partners, and suppliers, as well as law, politics, technology, economy, culture, and environment. Internal risk of new product development includes the issues connected with the project triangle, i.e. schedule, cost, and scope that can be considered in the aspect of a company’s organisational structure, project management culture, and communication in the project team (Caganova et al, 2012). The project risks can be categorised in a number of ways, for instance, as the fields concern organisation, requirements, communication, and product development methodology (Choi and Ahn, 2010); county, construction, design, payment, client, and subcontractor risk (Dikmen et al, 2007); technical, organisational, design, procurement, implementation, and operations risk (Dey, 2010); project management, engineering, execution, and suppliers risks (Nieto‐Morote and Ruz‐Vila, 2011). After the above literature review and assuming that project risk identification model should be tailored to the nature of a company that develops new products, the following risk factors are identified: external, schedule, financial, design, organisational. An illustration of a risk identification model is presented in Figure 2.

Figure 2: Project risk identification model (Author elaboration, 2013) Table 1 presents a sample of major risk factors and subfactors in the context of multi‐project management in a product development. The factors depict tacit knowledge which cannot be easily quantified and measured. Table 1: Tacit knowledge project risk factors Risk factor External

Schedule

Risk subfactor 1. Insufficient demand Attitude of client Incomplete understanding of customer requirements Poor macroeconomic conditions 2. Environmental requirements Inappropriate materials, hazardous substances; amount of waste Inconsistency with sustainability standards and certification Unclear legal requirements 3. Unavailability of suppliers Poor performance of suppliers and subcontractors Natural disaster such as earthquake, fire, flood, storm 1. Project complexity Schedule inaccuracy Project density Task dependencies

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Financial

Design

Organisational

Risk subfactor 2. Project duration Reserve time inaccuracy Incomplete task assignments Duration inaccuracy 3. Unavailability of resources Inaccurate estimation of personnel availability Inaccurate estimation of material ordered and schedule responsibility 1. Unavailability of funds Delay in payments Contractor’s financial instability Inaccurate price conformance of material supplier Inaccurate project budget estimation 2. Inappropriate financial reserves Economic power of company Working capital requirements 1. Lack of experience in similar projects Product complexity Incomplete product performance (functionality) evaluation 2. Incomplete product specification Unfamiliarity with the technology Inaccurate estimation of material specification Incomplete conceptual manufacturing process design 1. Complexity of organisational structure Instabilities in management structure (including changes of project team members) Unstable working relations Inappropriate delegation of responsibilities Incompatible hardware/software (IT infrastructure breakdown, IT hacking) Inappropriate methods/techniques/tools for planning Inappropriate metrics (or lack of metrics) 2. Ineffective communication Ill‐determined team size Incompatible team Inaccurate estimation of team skill and training requirements Inappropriate leadership style (e.g. lack of clear goals, motivation, trust, commitment.) 3. Inappropriate project management culture Inappropriate organisation’s culture Lack of team member commitment (inappropriate skills, motivation, trust, Personality conflicts (including competition between project managers over resource allocation to “own” project)

The presented model of risk identification includes a set of decision variables (risk factors) and the constraints, e.g. rules that are formulated by the experts. The decision problem can be reduced to a following question: what is the total project portfolio risk for the considered values of risk factors. The methodology concerning the solution of the considered problem is presented in the next section.

5. Proposed methodology for project risk assessment The methodology for project risk assessment is based on fuzzy set theory and consists of the following stages:

Definition of variables and selection of a membership function for each variable.

Obtaining the rule base from the experts.

Determination of subjective level for risk subfactors.

Obtaining the ratings for each risk factor, project, and project portfolio.

In fuzzy logic, the membership defines how the value of a fuzzy variable is mapped to a degree of membership between 0 and 1. Membership functions are used to calculate the degree of membership of a fuzzy risk score to different sets expressed by linguistic terms such as e.g. low risk, medium risk, and high risk (see Figure 3). The shape of a fuzzy number and scale of a linguistic variable depends on user needs. In this paper, a subjective risk level is assigned to a risk factor using 1‐10 scale.

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Figure 3: Membership function In the next stage, the rule base is obtained from the experts. In this study, aggregation rules demonstrate how the risk levels change under different scenarios. Aggregation rules are the IF … THEN rules that reveal the value of an output variable (risk rating) if values of input variables (risk subfactors) are expressed by different linguistic terms. An example of decision matrix is depicted in Table 2. Table 2: Decision matrix of aggregation rules Project management culture

Low (L) Medium (M) High (H)

Communication in the project team Low (L) Medium (M) High (H) L M M L M M M H H

The above example concerns the tacit knowledge elements of “project management culture” and “communication” on “organisational risk”. For instance, low organisational risk means the project should be completed without delay, medium – the project should be completed within 20% delay, and high – the project should be completed above 20% delay. As an example, three of nine fuzzy rules for “organisational risk” are given below: IF “Project management culture” is low AND “Communication” is low THEN “Organisational risk” is low. IF “Project management culture” is low AND “Communication” is high THEN “Organisational risk” is medium. IF “Project management culture” is medium AND “Communication” is high THEN “Organisational risk” is high. In the next step, the user determines subjective level for risk subfactors that after defuzzification enable obtaining the ratings for each risk factor, i.e. external, schedule, financial, design, organisational risk. Finally, project risk is calculated as the average of all risk factors, in turn project portfolio risk is determined as the average of all project risks.

6. Proposed methodology example The proposed methodology has been applied to the projects concerning three new product lines. Materials are ordered from different suppliers, as well as they vary considerably in the cost, sustainability standards, etc. This leads to the determination of different risk level for subfactors, such as “Insufficient demand”, “Environment requirements”, and “Unavailability of suppliers” for the field of external risk. Subjective risk level is from 1 to 10, where 1 depicts the lowest level of risk and 10 the highest level of risk. The calculations concerning risk assessment have been generated with the use of Fuzzy Logic Toolbox Matlab® software. After converting the linguistic variables (subjective risk level) into triangular fuzzy numbers, the centroid of area method was performed for defuzzifying the triangular fuzzy numbers into corresponding non‐ fuzzy performance values.

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Figure 4: Project portfolio risk rating The goal of quantitative risk analysis is the numerical analysis of probability of occurrence of each risk subfactor and their outcomes on risk factors, project, and finally on project portfolio. Project risk is calculated as average of risk factor, i.e. external, schedule, financial, design, and organisational. For example, presented in Figure 4 the average of risk factors equals 7.189 for third project. In turn, project portfolio risk is calculated as the average of all project risks as follows: (5.511+5.892+7.189)/3=6.197. If a company considers minimization of project portfolio risk, then it can test for which projects the entire risk is the lowest (e.g. for project 1 and 2 the project portfolio equals 5.701).

7. Conclusions As the present economic environment is full of turbulent changes concerning technology, economics, and society, the rapid globalisation and world economic crisis creates completely new conditions for activities of enterprises (Caganova et al, 2012). Most projects are executed in the presence of uncertainty and are difficult to manage, due to comprising of many activities linked in a complex way. Hence, there is an increase in demand for new knowledge that enables the solution of problems encountered during complex project execution (e.g. automotive industry new product development). Fuzzy risk assessment provides a promising tool to quantify tacit knowledge in an explicit manner through the measurement of risk ratings where the risk impacts are typically vague and defined by subjective judgments rather than objective data. In this research, a risk identification model is proposed for multi‐project environment in the context of the product development. In the proposed methodology, fuzzy set theory is utilized for project portfolio risk assessment. The major contribution of this research paper to the fields of knowledge management and risk management literature is through the adoption of a cross‐disciplinary approach which proposes a practical methodology to translate tacit knowledge into explicit knowledge and to enable the comparable measurement of external and internal risk factors, including less tangible elements such as communication and culture issues in project teams. The proposed approach of risk assessment has been implemented in the form of a decision support tool that can be used by the professionals to quantify risk ratings. The advantage of the tool is that it can provide guidance for a company about the amount of risk premium that should be included in the mark‐up. If new products are similar to developed products in the past, then the rule base can be verified according to past experiences with risk assessment. Another potential advantage is the tool’s utilisation as an organisational learning tool. As the experience of the managers is captured in the form of IF … THEN rules and uploaded to the tool, it can help development of a corporate risk memory. Less experienced staff can refer to this risk information while calculating risk premiums in a similar project.

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The disadvantage of the approach is connected with the challenges with capturing knowledge from experts. This obstacle leads to the increase of research interest in the area of automatic knowledge discovery, for instance, with the use of statistical analysis or artificial intelligence techniques, such as case‐based reasoning, neural networks, hybrid fuzzy neural system. This system can estimate a function without any mathematical model and learn from experience with sample data. Further research will aim to focus on the extension of the proposed risk identification model towards an enhancement of organisational risk in a project. Moreover, further research will be aimed at developing a fuzzy neural system and its real life verification. The subject of future research can also include formulation of the risk identification model in terms of a constraint satisfaction problem that facilitates seeking a feasible set of alternatives for project portfolio completion according to risk level.

Acknowledgements This paper has been published as a part of submitted VEGA project no. 1/0787/12 „The identification of sustainable performance key parameters in industrial enterprises within multicultural environment“. This article was created with the support of ESF project: “Quality improvement and rationalization of the study programme Industrial Management oriented on the career consulting ‐ ITMS 26110230055.”

References Atkinson, R., Crawford, L. and Ward, S. (2006) “Fundamental Uncertainties in Projects and the Scope of Project Management”, International Journal of Project Management, Vol 24, pp 687‐698. Baloi, D. and Price, A. D. F. (2003) “Modelling Global Risk Factors Affecting Construction Cost Performance”, International Journal of Project Management, Vol 21, pp 261‐269. Blackler F. (1995) “Knowledge, Knowledge Work and Organisations: An Overview and Interpretation”, Organisation Studies, Vol 16 No. 6, pp 16‐36. Caganova, D., Cambal, M., Sujanova, J. and Woolliscroft, P. (2012) “The Multiculturality Aspects and Human Capital Management within Slovak Industrial Enterprises”. In: 4th ECIC. Helsinki: Academic Publishing International, pp 106‐ 117. Carr, V. and Tah J.H.M. (2001) “A Fuzzy Approach to Construction Project Risk Assessment and Analysis: Construction Project Risk Management System”, Advances in Engineering Software, Vol 32, pp 847‐857. Choi, H. and Ahn, J. (2010) “Risk Analysis Models and Risk Degree Determination in New Product Development: A Case Study”, Journal of Engineering and Technology Management, Vol 27, pp 110‐124. Currie W.L. (2003) “A Knowledge‐Based Risk Assessment Framework for Evaluating Web‐Enabled Application Outsourcing Projects”, International Journal of Project Management, Vol 21, pp 207‐217. Dey, P.K. (2010) “Managing Project Risk Using Combined Analytic Hierarchy Process and Risk Map”, Applied Soft Computing, Vol 10, pp 990‐1000. Dikmen, I., Birgonul, M.T. and Han, S. (2007) “Using Fuzzy Risk Assessment to Rate Cost Overrun Risk in International Construction Projects”, International Journal of Project Management, Vol 25, pp 494‐505. Hillson, D. (2004) Effective Opportunity Management for Projects – Exploiting Positive Risk, New York, Marcel Dekker. Holden, N.J. (2013) Professional MBA Automotive Industry Communication Skills and Social Competence Module, Lecture th notes, Slovak University of Technology, 8 March 2013. Glisby, M and Holden, N.J (2011) “Mastering Tacit Corridors for Competitive Advantage: Cross‐Cultural Knowledge Creation and Sharing at Four International Firms”, Global Business and Organisational Excellence, pp 64‐77, July/August 2011. Karimi, A., Mousavi, N., Mousavi, S.F. and Hosseini, S. (2011) “Risk Assessment Model Selection in Construction Industry”, Expert Systems with Application, Vol 38, pp 9105‐9111. Nieto‐Morote, A. and Ruz‐Vila, F. (2011) “A Fuzzy Approach to Construction Project Risk Assessment”, International Journal of Project Management, Vol 29, pp 220‐231. Nonaka I. (1991) “The Knowledge‐Creating Company”, Harvard Business Review, Vol 69, No. 6, pp 96‐104. Nonaka, I. and Takeuchi, H. (1995) The Knowledge Creating Company: How Japanese Companies Create the Dynamics of Innovation, Oxford University Press, USA Olsson, R. (2007) “In Search Of Opportunity Management: Is the Risk Management Process Enough?” International Journal of Project Management, Vol 25, pp 745‐752. Project Management Institute (2013) A Guide to the Project Management Book of Knowledge (PMBOK), Newtown Square. Relich, M. (2012) “An Evaluation of Project Completion with Application of Fuzzy Set Theory”, Management, Vol 16, No. 1, pp 216‐229. Rowe, W.D. (1994) “Understanding Uncertainty”, Risk Analysis, Vol 14, pp 743‐750. Williams, T. (1995) “A Classified Bibliography of Recent Research Relating to Project Risk Management”, European Journal of Operational Research, Vol 85, pp 18‐38.

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Managing Learning Style Across Generation in Workplace Juli Purwanti, Fadillah Rizky and Wahyudi Handriyanto PT Telekomunikasi Indonesia, Tbk., Bandung, Indonesia yuli_purwanti@telkom.co.id Abstract: PT Telekomunikasi Indonesia, Tbk. (Telkom) has dominated the competition in telecommunication and information industry in Indonesia for years. Businesses today face serious challenges as they set a path forward through unprecedented, uncertain and challenging economic times. Nowadays, Telkom is currently facing issues related to business growth because of area of business growth potential in Indonesia is getting smaller. In the meantime, amid increasingly fierce competition, business growth is the key to long term survival of the company. Therefore, Telkom decided to expand its business outside Indonesia and stop relying on a single service. Diversification is a consideration to expand the business growth. In order to cope with business growth issue, Telkom must prepare its human resources to become employees with high capability and possess global mindset. Human resource with strong will to continuously learn to improve their competency and able to adapt to a variety of changes is important key to meet company goal. It is well known as a “fast learner”. The company wants all employees have equal ability to be a fast learner or even exceed the speed of business growth. Now, Telkom has 18,285 employees and there are 4 (four) generations in it, which are the mature generation, baby boomers, generation X and generation Y/ millennium. Each of generation has different speed in learning process. Telkom’s competency development system (learning system) that exists today was designed based on the fact that the needs and pace of learning for all employees is basically the same. Learning system was designed to only consider the needs of the company, not the needs and characteristic of employee learning style. Consequently, although Telkom’s organizational learning system provides a fairly comprehensive, such as classroom learning, e‐learning, and knowledge management systems, the result does not very motivate employees to learn and not significantly impact on business growth. Company’s investment in learning is quite large (1.5% of revenue), which is established by agreement between management and Telkom employee union. Considering the fact above, this paper tries to identify each learning style from all generation of Telkom employees. Based on writers’ survey, recommendation will be provided for managing needs of learning and method, which is appropriate for the employees to be expected to get optimal result. This paper provides some proposed solutions, which addresses to the company and also be given as a contributions of practitioners to the enrichment of human resource management science in the academic world. Keywords: across generations, fast learner, learning organization system, learning style, learning method

1. Introduction When business competition is getting more competitive and complex, Telkom starts to believe that human resource is a major force for determining company victory and survival of the company. Therefore, Telkom states that developing human resource competency is the first strategic initiative for maintaining company business in tight competition. Telkom Corporate University (TCU) was established in 2012. TCU is expected to be able to create a system that can produce great leaders and great people which are superior. Moreover, managing corporate learning process within the framework of a comprehensive human capital development is another important task for TCU. According to the tasks above, TCU must be able to provide suitable and high quality learning solution as well as provide optimal learning for increasing the capability of the human resources and company performance. TCU is designed to perform activities that strengthen the learning ability of employees and the organization. In addition, TCU is expected to strengthen the implementation of the learning organization system that has been initiated several years ago and motivate employees to continually develop their competency. In order to improve employee competency, Telkom issued policies and made an integrated learning media, which are expected to support forming a learning culture among employees. To speed up the learning process, Telkom expects its employees to utilize all learning facilities so all employees can be turn into a Fast Learner. Employee learning activity carries out under an annual program that is packaged in Human Capital Development Program regarding to the analysis of organizational assessment and individual assessment. From year to year, Telkom continues to develop a strong culture of learning. Telkom seriously gives its best effort in developing a variety of media to facilitate the learning process of the employees. Through TCU, the company tries to continuously improve media and learning method to leverage employees’ competencies.

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Juli Purwanti, Fadillah Rizky and Wahyudi Handriyanto Telkom introduced the ‘sandwich method’, such kind of a blended learning which is a combination of classroom training and practice in the workplace in the form of action learning program. Learning media that has been utilized is varied, which are off‐line media, such as classroom, and on‐line media, and on‐line media, such as e‐learning and digital learning. In addition, the learning method which has been provided is also varied, such as a lecture, seminar/ conference, on the job training, and workshop. In this paper we define that lecture is a learning method which is conducted in class for 3 to 5 days. On the Job training is a learning method implemented in the workplace are guided by Coach or supervisor. Workshop contains a sharing session from a practitioner or professional, and ended with working groups to develop some recommendations based on the sharing session topics. Beside the methods that have been mentioned earlier, coaching and mentoring are also used during the action learning program. In leadership development programs, all of these methods can be used simultaneously in blended learning method (we called sandwich method). The question that may arise are ‘would it be effective if there are lots of method and learning media involved?’ and ‘should those learning media be evaluated and specified which are more effective to be utilized in each generation?’. On April 2013, Telkom has 18.285 employees, which consists of three generations, namely, Baby Boomers, Gen‐X and Gen‐Y.

Figure 1: The number of employee in each generation Based on the pie chart above, it indicates a significant issue, which is the number of baby boomers extremely high. There will be a lot of baby boomers getting pension at the same time. In the near future, Telkom will have balance composition between all those three generations. Moreover, there will be a new generation involved after that. Those three generations are the subject of competency development that must be done by TCU. Based on some of literatures, those three generations have different characteristics in learning style. Therefore, it is still relevant that Telkom treats the same methods and media for those generations in learning program. What is happened if the three generation in the same learning program. What are methods and media to be prepared by the facilitator? In this paper, the authors try to identify the difference in learning style from those three generations, which are the Baby Boomers, Gen‐X and Gen‐Y by conducting a survey about the learning style of each generation. Authors would like to believe that the different in learning styles across generation, which is written in some of literatures, does happen in the real working world. Based on that survey, the authors also try to identify the media or learning method that is suitable for any generation for providing maximum learning outcome. Based on the survey result analysis, the authors try to identify the media or learning methods are suitable for any generation which is expected to provide maximum learning outcomes.

2. Theoretical background Many organizations are trying to manage a multigenerational workforce. Organizations have begun to shift their focus from the aging worker to issues related to a multigenerational workforce (Sprague, 2008). In fact, many workplaces now employ four different generations of workers (Hart, 2008).

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Juli Purwanti, Fadillah Rizky and Wahyudi Handriyanto According to Charles' article “Generations: Great Divide or Amazing Opportunity” (2009), the four generations can be defined as such: Traditionalists: Born 1925–1945 which generational personality are hardworking, stable and reluctant to buck the system. Baby Boomers: Born 1946–1964 which generational personality are driven, team players and judgmental of those who see things differently. Generation X: Born 1965–1980 which generational personality are adaptable, techno literate, poor people skills, and cynical. Generation Y: Born 1980–2000 which generational personalities are optimistic, tenacious, and need supervision and structure. Companies are stepping back and looking more holistically at how to develop programs and deploy technology that will speak to four distinct generations in the workforce. Each age group requires a different approach when designing career and compensation strategies, creating performance motivators, and addressing learning styles (Sprague, 2008). Learning is the process whereby knowledge is created through the transformation of experience (David A. Kolb, 1984). Learning style is an individual's natural or habitual pattern of acquiring and processing information in learning situations. Everyone processes and learns new information in different ways. One of the most common and widely‐used categorization of the various types of learning styles is Fleming's VAK model: Visual Learners, Auditory Learners, and Kinesthetic Learners. (Leite, Walter L.; Svinicki, Marilla; and Shi, Yuying, 2009). Fleming claimed that visual learners have a preference for seeing (think in pictures; visual aids such as overhead slides, diagrams, handouts, etc.). Auditory learners best learn through listening (lectures, discussions, tapes, etc.). Tactile/kinesthetic learners prefer to learn via experience—moving, touching, and doing (active exploration of the world; science projects; experiments, etc.). Learning styles were developed by Peter Honey and Alan Mumford, based upon the work of Kolb, and they identified four distinct learning styles or preferences: Activist, Theorist; Pragmatist and Reflector (Honey, P. & Mumford, A., 1982). Activist Learner is those people who learn by doing. Activists need to get their hands dirty, to dive in with both feet first. Have an open‐minded approach to learning, involving them fully and without bias in new experiences. Theorist Learner likes to understand the theory behind the actions. They need models, concepts and facts in order to engage in the learning process. Pragmatist Learner needs to be able to see how to put the learning into practice in the real world. Abstract concepts and games are of limited use unless they can see a way to put the ideas into action in their lives. Experimenters always try out new ideas, theories and techniques to see if they work. Reflector Learner learn by observing and thinking about what happened Prefer to stand back and view experiences from a number of different perspectives, collecting data and taking the time to work towards an appropriate conclusion A completely different Learning Styles Inventory is associated with a binary division of learning styles, developed by Felder and Silverman. In this model, learning styles are a balance between four pairs of extremes: Active/Reflective, Sensing/Intuitive, Verbal/Visual and Sequential/Global. Students receive four scores describing this balance. (Felder, Richard. M, Barbara. A. Solomon,. 1993). Some characteristics of the learning methods for each generation are as follows: For Baby Boomers, a trainer should tap into their experiences through activities such as discussions, simulations, case studies or problem‐solving exercises. Small classes are particularly effective for these learners because such settings encourage them to share their experiences (Quinney, Smith & Galbraith, 2010). These learners also like to see the value in what they are learning, whether it is useful for them at work or at home. For Gen‐X, Provide appropriate feedback and summarize key points learned so that they clearly understand what they have learned and how to apply the information (Tulgan, 1997). Find ways to engage and involve these learners (Prensky, 2005). They also are more comfortable with technology than previous generations, so they are comfortable in an environment that utilizes technology such as the Internet and multimedia.

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Juli Purwanti, Fadillah Rizky and Wahyudi Handriyanto Gen Y typically are skilled multitasking who prefer to learn through visual methods (pictures, sounds, videos), rather than reading text; or by using video to stimulate discussion (Myers, Sykes & Myers, 2008). With their ability and desire to be connected to technology and information, these individuals are independent learners. Set them in motion and they will find the information they need to learn the rest (Hart, 2008)

3. Learning style survey and analysis According to David A. Kolb, Learning style is “an individual's natural or habitual pattern of acquiring and processing information in learning situations”. Everyone processes and learns new information in different ways and opinions of Sprague (2008), which states that every generation needs a different approach in designing its learning program. The authors did a Learning Style Survey in each generation. In this papers, a survey is carried out by using three models of learning styles, which are Flemming's Model VAK (Visual, Auditory, Kinaesthetic), Global & Sequential Learner Style, and Honey & Mumford Model (Activist, Theorist, Pragmatist, and Reflector). The common characteristics of each learning style above can help authors to understand how employee learns and what methods of learning best fits for each generation. Understanding on how employee learn can help to maximize time people spend studying by incorporating different techniques to custom fit various subjects, concepts, and learning objectives. The survey result is for identifying learning styles of each generation. Moreover, it will prove the written theory or research as proposed by Quinney, Smith & Galbraith (2010), Tulgan (1997), Prensky (2005), Myers, Sykes & Myers, (2008), and Hart, (2008) about the differences in learning styles between generations really happening in the workplace. Is it the difference very significant so the company must be extra careful in designing its learning program? Although the number of Telkom employees is quite large and spread throughout Indonesia, but in this paper the authors just conduct a survey in Department of Human Capital Management and Telkom Corporate University with the total number of employee is about 225 people. Using Slovin Method, the number of sample which is required in survey is 220 with error significant 0.05. However, due to the limitations of the data collection time, the survey was conducted using a sample of 30 employees of each generation. The bar chart below shows the results of the survey of VAK Learning Style.

Figure 2: Survey result on VAK learning style in each generation Based on the bar chart above, it is seen that almost all generation have a tendency to be a Visual Learning Style, especially Baby Boomers. There is a few numbers of Auditory Learning Style in Baby Boomers. While for Gen‐Y, most of employees have a Visual Learning Style as well as there are some employees who have Auditory or Kinaesthetic Learning Style and the number itself is more than in Gen‐X. The next bar chart below shows the results of the latest survey and Sequential Learning Style.

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Figure 3: Survey result on global & sequential learning style in each generation Based on bar chart above, all generations apparently have a tendency to be a Global Learning Style that means learning everything starts from the big picture and fast in solving big problem. In Generation Y, the number of Sequential Learning Style is the largest. The bar chart below is the survey results of Cognitive Honey and Mumford Learning Style Model.

Figure 4: Survey result on cognitive learning style in each generation Based on the survey result above, it appears that Generation Baby Boomers have a tendency to be an Activist and Pragmatist Learning Style. Meanwhile, both Generation X and Generation Y have a tendency to be an Activist and Theorist Learning Style. The number of Pragmatist Learning Style in Generation X is the smallest. Besides authors did a Learning Style Survey, authors also determined the most effective learning media by conduction a survey. The survey consists of media learning Classroom Learning, On Line / Web Based Learning and Learning by Doing Learning (Workplace Learning). The bar chart below shows the survey result. Based on the result above, it appears that the generation of Baby Boomers have a tendency to choose classroom than over on Line / web based learning. Generation X likes to use both classroom and on Line / web based learning. Generation Y prefers to use on line / web‐based learning than classroom. In order to identify effective methods of learning in each of generations, the authors also conducted a survey of the most preferred methods of learning every generation and the image below shows the results of the survey question.

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Figure 5: Survey result on learning media in each generation

Figure 6: Survey result on learning method in each generation Based on the result above, it shows that Baby Boomers and Generation X prefer to choose On the Job Training, Seminar or Conference, and workshop but does not prefer on Lecture. Generation Y prefers to choose Workshop and many of them liked the lecture and seminar or conference too. From all the results, it can summarized as follows: Table 1: Summary of survey result

Generation Baby Boomers

Learning Style Visual (the number of Auditori is very few) Global Activist & Pragmatist

Learning Media Classroom

Generation X

Visual Global Activist & Theorist (the number of pragmatist is very few)

Classroom & on line/ web based learning

Generation Y

Visual (the number of auditory and kineasthetic is quite a lot) Global Activist & Theorist

On line/ web based learning

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Learning Method Seminar Conference Workshop (does not like to utilize lecture/ sharing) Seminar Conference Workshop (does not like to utilize lecture/ sharing) Workshop

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4. Proposed learning method related to learning style survey Based on the results and analysis of Learning Style Survey as described in point 3 above, the suitable and effective learning method will be identified in designing learning programs employees. Learning methods determines by taking into account the characteristics of each learning style model used, the characteristics of the VAK Learning Style, Global and Sequential Learning Style, and Cognitive Learning Style. While, the survey results of learning media and learning delivery media is very helpful in determining the type of media and learning type in accordance with the wishes and comfort for employees. This is important because when in the process of learning, a person who is in comfortable conditions, it is expected that the transfer of knowledge or competence enhancement will run without a hitch. Based on the survey result, the proposed Learning Method for each generation are as follows. Generation Baby Boomers According to Figure 2. Generation Baby Boomers have Visual, Global, Activist and Pragmatist learning style. There is a very few Baby Boomers who have Auditory learning style. This means that the Baby Boomers think in pictures and learn best in visual images. Enthusiasm for learning and their understanding depends on how the facilitator or instructor presenting their materials, for example reflected on his or her body language. Their learning characteristics are remember written directions well, need to see the material to learn it, requires the artistic appearance and difficult to understand the material when it is not visualized. In many learning process, it is suggested using diagrams, drawings, colourful markers, rich illustrations and images, using a lot of video for each topic. Based on survey results, Generation Baby Boomers in Telkom also have a Global Learning Style. With this style of learning, the baby boomers need the big picture of explanation of the material before going into the details matter. Employee which is included in the generation of baby boomers, according to the survey also has Activist and Pragmatist Learning Style. According to Honey and Mumford's, Activist Learner enjoys the here and now, and happy to be dominated by immediate experience. They are open‐minded, not sceptical. They growing niche to thrive on the challenge of new experience. Their days are filled with activity. They like to do brainstorming activity, problem solving, group discussions, role play and competitions. While Pragmatist Learner, according to Honey and Mumfords, it requires a lot of time to implement something, problem solving, discussion and case study. Learning media which is attracted more by Baby Boomers is classroom and they do not like the media on line / web‐based, while the Learning Method is highly preferred seminar / conference or workshop and do not like the lecture. For Baby Boomers, they should not be too much given the e‐learning or mobile learning but not too often included in the classroom with lecture method. It can be summarized that for the generation of Baby Boomers, the ideal learning method is classroom which has some case study as a problem solving exercise and competition event. Seminars or conferences may be provided with brainstorming activities, group discussions, role play and use a lot of video on the topic that will be explained. Explanation of the material should always start from the big picture regarding the matter. This is accordance with the opinion Quinney, Smith & Galbraith who said that the discussions, simulations, case studies and problem solving exercises are very effective to use in the learning process of a Baby Boomer. Generation X & Generation Y Both Generation X and Generation Y almost have the same learning style, which are Visual, Global, Activist and Theorist. The difference is that the number of Generation X who is pragmatist is very little, while there are quite a lot of Generation Y employees who are Auditory and Kinaesthetic Learning Style. There is not too much difference Learning Style between Generation X and Generation Y to Baby Boomers, it is just Generation X and Generation Y has a tendency to be a Theorist Learning Style. According to Honey & Mumford, the Theorist

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Juli Purwanti, Fadillah Rizky and Wahyudi Handriyanto learning styles requires background explanation clearly material, statistical data, examples of the application of the theory is explained and requires a lot of models. Learning media which is the most preferred by Generation X is the Classroom and on line / web‐based, whereas the most favoured media learning of Generation Y is on line/ web based learning. The most preferred learning method for Generation X is seminar or conference and workshop while Generation Y prefers to utilize the workshop as a Learning Method. The Learning methods which is suitable for Generation X is the Classroom with brainstorming activities, group discussions, role play and use a lot of video to the topic that will be explained, the background material explanations, many displayed statistics and examples of applied theory‐related material. On line / web‐based learning style is also preferred learning media by Generation X. Generation X also likes learning method seminar / conference or workshop. Explanation of the material should always start from the big picture regarding the matter. Learning methods which is suitable for Generation Y is workshop with brainstorming and project solution activity and also competition event, group discussions, and use a lot of video on the topic that will be explained. Explanation of the material should always start from the big picture regarding the matter. Media on line / web‐based can be utilized as an effective medium of learning. At the time of learning delivery, it should be designed the room that motivates or inspires them, use Role Play, active games, outbound activities and field visits. According Myers, Sykes & Myers, Generation Y prefers to learn through visual methods (pictures, sounds, videos) rather than reading text or using video to stimulate discussion.

5. Conclusion Understanding the employee learning style will help company managing competency development across generation. Knowledge of learning style helps to control the process learning. It is very important to understand and explore each generation learning style. Analyzing learning style across generation can be very helpful and beneficial to the employee in becoming more focused which ultimately increase learning success. By utilizing the survey result, TCU is able to design a learning program with respect to learning style characteristic of each generation. This survey results are expected to help the TCU facilitators to prepare learning method in accordance with the learning style of participants even if the three generations is in the same class. Based on survey results, it turns out that there is indeed a different in learning style across generation although it not very significant. Baby Boomers prefers more on utilizing classroom, problem solving and case study exercise and do not like on line / web‐based learning. Generation X and Y have similar learning more style. It is just that there are quite a lot of Generation Y who loves more activities in the process of learning. To make the policy apply to all employees, Telkom needs to conduct a survey with a larger sample size regarding to the number of employees as much as 18,285 people. By large sample, the result will represent the real population. Because the author did not intend to categorize this paper as an academic paper, the author did not conduct statistical tests before data analysis such as reliability test and variance test. Research in this paper is still an early stage of research that must be equipped with a complete set of statistical tests and supported by statistically adequate data. Author must identify the factors that influence the success of learning process and searching for the factors that most influence learning process success. Authors should examine whether managing learning style across‐generation is crucial to the learning process succeed. Finally, the authors hope that this basic research is able to give benefit to the company in improving human capital development program. For the academic world, this is the sharing of a practitioner who is expected to enrich the case should be formulated or developed more comprehensive theory.

References Felder, Richard M., "Reaching the Second Tier: Learning and Teaching Styles in College Science Education." J. College Science Teaching, 23(5), 286‐290 (1993). Hart, J. (2008, Sept. 22). Understanding today’s learner. Learning Solutions Magazine. Retrieved Jan. 27,2012, from www.learning solutionsmag.com/articles/ 80/understanding‐todays‐learner.

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Juli Purwanti, Fadillah Rizky and Wahyudi Handriyanto Honey, P. & Mumford, A. (1982) Manual of Learning Styles London: P Honey Kolb D. (1984). Experiential learning: experience as the source of learning and development. Englewood Cliffs, New Jersey: Prentice Hall. Kolb D. (1999). The Kolb Learning Style Inventory, Version 3. Boston: Hay Group. Leite, W. L., Svinicki, M. & Shi, Y. (2010). Attempted Validation of the Scores of the VARK: Learning Styles Inventory With Multitrait‐Multimethod Confirmatory Factor Analysis Models. Educational and Psychological Measurement. 70, 323‐ 339. Myers, D., Sykes, C. & Myers, S. (2008). Effective learner‐centered strategies for teaching adults: Using visual media to engage the adult learner. Gerontology & Geriatrics Education, 29(3), 234‐238. Prensky, M. (2005, Sept./Oct.). Engage me or enrage me: What today’s learners demand. EDUCAUSE Review, 60‐64. Retrieved Jan. 27, 2012, from http://net.educause.edu/ir/library/pdf/erm0553.pdf. Quinney, K., Smith, S. & Galbraith Q. (2010). Bridging the gap: Self‐directed staff technology training. Information Technology and Libraries, 24(4), 205‐213. Sprague, C. (2008, Feb. 13). The Silent Generation meets Generation Y: How to manage a four‐generation workforce with panache. White River Junction, VT: Human Capital Institute. Tulgan, B. (1997). The manager’s pocket guide to Generation X. Amherst, MA: HRD Press.

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A Critical Analysis of Intellectual Capital Reports in Banking Industry from 1994 to 2011 Linlin Cai, Eric. Tsui and Benny Cheung Knowledge Management and Innovation Research Centre, Department of Industrial and Systems Engineering, The Hong Kong Polytechnic University Hung Hom, Kowloon, Hong Kong linlin.cai@connect.polyu.hk eric.tsui@polyu.edu.hk Benny.Cheung@polyu.edu.hk Abstract: As a supplement to financial reports, Intellectual Capital (IC) reports are indispensable for disclosing intangible assets values. However, IC reporting has also received some negative arguments. In this paper, the development of IC reports published by five European banks from year 1994 to year 2011 is reviewed by manual content analysis. The original IC management Models (ICMMS) in the IC reports are used to examine the actual practical application. In the present study, not only the quantitative (IC metrics) and qualitative (Narratives) IC‐related information are concerned, but also, the negative and positive aspects are included in the analysis during the study. Although the banks being investigated are found to have partially similar IC frameworks, it is interesting to note that each bank makes use of its own set of metrics for reporting. Hence, it is very difficult to compare IC among the banks due to the varied metrics being adopted. Every bank uses narratives to construct their IC reports. However, the main part of each IC report is still composed of quantitative IC metrics. Narratives are just used as the introduction and description. Furthermore, little negative IC‐related information is being disclosed. This makes IC reports to be ineffective to serve as internal management and external communication tools. Even though pictures, tables, diagrams and charts in the reports are discarded in the analysis, which may undermine the accuracy of the findings, nevertheless, it is extremely tedious and time‐consuming to manually identify metrics from 51 IC reports. Based on the results in the study, some suggestions are provided for addressing the latest IC practice and theories in IC reporting. Keywords: intellectual capital reports; IC metrics; IC model; narrative; content analysis; critical analysis

1. Introduction In the past two decades, the world’s economy has rapidly changed from an industrial one to knowledge‐based economy. Conventional financial statements have found to be inadequate to fully and accurately disclose the whole and true value of an organization. Intellectual capital (IC) is the non‐financial capital which has attracted a lot of interest (Edvinsson and Malone, 1997, Roos et al., 1997, Edvinsson, 1997, Stewart and Ruckdeschel, 1998). IC has been applied for external reporting, auditing, accountability and governance, management control/strategy, and performance measurement. Among these applications, management control and external reporting are the two most popular areas(Guthrie et al., 2012). To disclose IC, a separate and new kind of report is often created which is often presented as a supplement to an annual report. It has been 18 years since the first IC report was released by Skandia in 1994. Recently, it has been advocated by Leif Edvinsson, the author of the first IC report that “we need to go beyond IC reporting” (Dumay, 2013). Accordingly, if companies go beyond the traditional IC reporting, the role and purpose of future IC reports also need to be re‐determined. Several major challenges exist for the new mission of IC reporting. Among others, how will IC reporting be adopted and practiced in the real world in the future? In order to shed light into the above challenges and more, a critical review of what really happened with IC reporting during these 18 years is much needed.

2. Data analysis This research focusses on the banking industry which is not only knowledge‐intensive but probably also has the most of IC reports produced among other industries. A thorough review has identified that there were 51 IC reports published by five European banks (Skandia, ATP, BBVA, Bankinter, OeNB) from year 1994 to year 2011. The set of IC frameworks adopted by these banks can be seen in Table 1. Skandia is one of the world’s leading independent providers of solutions for long‐term savings and investments. They offer products and services that cater for various financial needs and security. As a leading organization in managing IC, Skandia produced the world’s first IC report supplementing its financial reports in 1994. The goal

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Linlin Cai, Eric. Tsui and Benny Cheung of Skandia’s IC reports is to manage and create value from all the intellectual resources(Mourisen et al., 2001). One of the most famous IC frameworks, Skandia Navigator, was created to help to construct Skandia’s IC reports that contain human focus, structural focus, customer focus, financial focus as well as renewal and development focus, which had been the supplement to its annual reports until 1997. In 1998, IC report with human capital (HC) metrics was separated from Skandia’s annual report. From 2000 to 2001, the environmental report which considers ethics and environment indicators became a supplement of annual report. From that year, IC reports were not issued any more. Subsequently, HC metrics emerged in the annual report again in 2002. The changing trend of Skandia IC metrics is shown in the Figure 1. As the first IC report in the world, the narratives of this report also play a vital role to explain the purpose of the IC framework and IC metrics. Table 1: Framework of IC reports Company

IC reports components

IC Model

Goal of IC report

Human Focus (HF)

Skandia

IC metrics; Narrative; IC model

Structural Focus (SF) Skandia Navigator

Customer Focus (CF)

Internal management

Financial Focus (FF) Renewal and development focus (R&D) Clients

ATP

IC metrics; Narrative; IC model

Staff

Balanced Scorecard

Business procedures

Internal management

Finance focus

BBVA

BankInte r

IC metrics; IC model

Intellectual capital measurement model Intellectual capital measurement model

Human capital (HC) Structural capital (SC) Relational capital (RC) Human capital (HC) Structural capital (SC) Relational capital (RC)

Internal management; external communication Internal management; external communication

Human capital (HC) OeNB

IC metrics; Narrative; IC model

Process‐oriented model

Structural capital (SC) Relational capital (RC)

Internal management; external communication

Innovation capital (InC)

Figure 1: Skandia IC metrics

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Linlin Cai, Eric. Tsui and Benny Cheung ATP is one of the largest pension and insurance companies. The group is responsible for operation and development of ATP Livslang Pension and Supplerende Pension. ATP is also one of the leading companies who produced IC reports among the financial services companies. Since the first IC report was released in 1999, ATP had been producing IC reports for 10 years. Their reports aim to present a vision that pursues prioritized business challenges and targets. The framework was originated from the Balance Scorecard(Kaplan and Norton, 1996); ATP’s IC framework provides the principles for the company to develop its aims, actions and results. ATP’s style of IC reporting relies on IC metrics to tell the stories about how IC to create values. As a result, the fate of ATP’s IC reports has a close relationship with its identified IC metrics. As shown in the Figure 2, the number of IC metrics was close to zero in year 2011. Subsequently, IC report was also not produced anymore. Finally, the responsibility report has replaced by the IC reports and the climate report in 2011. Another important component of IC reports is narratives. It is a scenario which is a story line of the capabilities of the firm (Mouritsen et al., 2001). The main function of narratives in ATP’s IC reports is to describe the IC metrics in the context.

Figure 2: ATP IC metrics BBVA is a multinational Spanish banking group which is the second largest bank in Spain. As one of the leaders in managing IC, BBVA has released its IC reports for eight years. The BBVA’s IC reports aim to present a quantification of its intangible assets; its effort in generating an IC report is seen to be an essential factor in value creation and extraction which generates competitive advantage of the highest order. BBAV used a one‐ page metrics with the definition of the major IC elements. The main IC metrics are presented each year to allow the market to assess certain aspects that are not covered by financial reports. The IC metrics were first used in 2000. For BBVA, the IC report was embedded into the annual report in 2002. The reporting framework was called Intellectual Capital Measurement Model (ICMM), which covers human capital (HC), relational capital (RC) as well as structural capital (SC). The main content in the IC report was also IC metrics. As a result, once the IC metrics were not used, there was no further IC report. As shown in Figure 3, it is interesting to note that a few IC metrics were used in 2008 and then the number of them decreased dramatically. Since 2008 afterwards, IC reports were also nearly dead. In 2011, the sustainability report substituted the IC report for disclosing IC in BBVA. The narratives of these IC reports are used to describe the context of producing IC reports and the ICMM. BankInter is a commercial bank and was ever the sixth‐largest listed banking group in Spain. The first IC report that can be tracked in the annual report was produced in 2006, and then the IC reports have been issued year by year. The BankInter’s IC reports aim to provide uniform and relevant information about BankInter from a strategic point of view. The report offers the comparable data available to shareholders, customers and society. It is useful for determining the Bank’s current value in this respect. BankInter also used only IC metrics with the definition of main IC elements. BankInter claims that the IC framework not only provides useful information for assessing

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Linlin Cai, Eric. Tsui and Benny Cheung its ability to create value but also a management tool for optimizing the contribution made by the Bank’s intangible assets to its business strategy. As shown in Figure 4, it is found that the number of BankInter’s IC metrics is relatively stable. There is also no sign if the IC reports will be further issued or not. The narratives of BankInter’s IC reports serve the purpose of introducing the definition of IC.

Figure 3: BBVA IC metrics

Figure 4: BankInter IC metrics OeNB is the central bank of the Republic of Austria and an integral part of both the European System of Central Banks and the Eurozone. For the sake of public interest, the OeNB also contributes to monetary and economic policy decision making in Austria and in the Euro area. To fulfill its commitment to stability in a dynamically changing environment, managing intellectual capital has been the special strategy. The first IC report of OeNB was released in 2003. OeNB has been producing IC reports for nine years. The main components of OeNB’s IC report were also using narratives (Dumay and Cuganesan, 2011)to describe the IC activities behind the IC metrics. Figure 5 shows that innovation capital metrics were not used in 2011. The other 3 metrics are still used and kept relatively stable. Compared with other companies’ IC reports, the narratives in the OeNB are more important. They not only introduce the purpose of IC reports and the IC models, but also try to provide more description about the case‐and‐effect of different activities. IC metrics become easier to be understood in the context.

3. Discussion The goal of IC reports shows that they function as external communication and internal management tools as well as a comparison tool. As one of the external communications tools and often shown to media, almost every IC report depicts a “positive picture” for the company. For example, when ATP’s staff satisfaction dropped significantly in 2005, it was ascribed to the uncertainty brought about by the major in‐house changes, including organizational restructuring, staff reductions and IT outsourcing. On another occasion, even though staff satisfaction declined nearly 10%, good progress was achieved in a number of specific areas, including the employee’s assessment of their immediate superior. In 2006, the staff satisfaction increased significantly as expected (which was stated in an earlier year report). Such increase was ascribed to be the improvement to

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Linlin Cai, Eric. Tsui and Benny Cheung mental health and safety at work for staff. Actually, altogether three reasons mentioned in 2005, but only one explanation was given for such increase. One wonders if this is the true reason behind the fluctuations. If the in‐house changes and IT outsourcing also result in the lowering staff satisfaction, can one interpret that the increase of staff satisfaction to mean that these changes are accepted successfully? Meanwhile, employee’s satisfaction about their immediate superior has increased. Can the ATP guarantee there is no power‐distance stress when staff completed the survey? Actually, people do not like to be measured (Sveiby, 2010). The content is indeed positive and beautiful enough for promoting, but in terms of internal management and external communicating, it will also bring troubles and confusion. Moreover, the biased disclosure just contains positive information may also bring disaster just as the negative news does(Dumay, 2012). The independent materials being used to disclose IC information in terms of positive, negative and neutral aspects also suggests that conventional internally‐derived IC reports have lost their credibility and relevance(Lee and Guthrie, 2010). Another goal of these IC reports is the comparative function, which is mentioned by BankInter. For BankInter, the IC indicators have remained the same over the year. As a result, it is possible to do the internal benchmarking in different years. However, the problems are that it is very difficult to carry out any comparison with other four banks that are also reporting IC. The data show that different companies have its own IC metrics, and the IC indicators even vary inside the organizations. As a result, few indicators are the same with other organizations, which also make the external comparison impossible.

Figure 5:.OeNB IC metrics Since its original development, IC should be managed though models (Edvinsson and Sullivan, 1996). Another most important element in the IC reports is the IC management models. The most fundamental framework is the conventional IC classification used by BBVA and BankInter. ATP’s Balanced Scored (BSC) shows how to use the existing model to develop IC reporting. Skandia and OeNB illustrate the good examples of creating frameworks with their own reports. This is particularly true for OeNB, its process‐oriented model focuses not only on the organization’s strategy, but also on the internal business process. It should be recognized that various frameworks serve to produce IC reports successfully. They serve to describe what kind of IC that every organization should pay attention to. However, these frameworks also have limitations in terms of their internal management. Compared with other frameworks, the OeNB’s process‐oriented framework is seen to be superior one that bridges the IC, organization strategy as well as business process, which show a holistic picture of how IC is mobilized inside the organization through putting IC into the context of internal management process(Sveiby, 2010, Edvinsson and Sullivan, 1996). However, OeNB just focuses on the cause‐ and‐effect links between different IC components and strategy in terms of value creation. Both direct and indirect dependencies between the IC elements are found through drawing the value creation map (Dumay and Cuganesan, 2011, Mouritsen, 2006). It is obvious that the process‐oriented framework ignores the interaction among IC. Compared with OeNB’s process‐oriented model, Skandia Navigator and ATP’s BSC even do not weave in the actual business process. Moreover, this model involves few ATP’s business processes, which make how IC mobilizes inside organization ambiguous(Bontis, 2001, Dumay, 2008). Meanwhile, the critical study about the BSC and Skandia shows that the use of these ever classical frameworks to manage and report IC hides the actual process of how IC creates values(Dumay, 2008). What the ICMM expresses are even less. They can just be used to show the basic IC indicators.

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Linlin Cai, Eric. Tsui and Benny Cheung From the statistics, it is clear to see that IC metrics are one of the most important elements in the IC statements frameworks. The fates of IC reports and IC metrics have close relationship in some companies. In Skandia, during the 1999, 2000 and 2001, the IC metrics determine the fate of IC reports. In 2010, structural capital metrics in ATP’s IC reports disappeared. The IC reports also stop to be produced after one year. Instead, three human capital metrics were used in the responsibility report. In this kind of new report, IC metrics seem not treated as enthusiastic as before. For BBVA, there were only few relational capital metrics left in 2008, and no more IC report were issued after that. Then a very interesting phenomenon occurred. Some IC metrics were reused in the sustainability reports. BankInter and BBVA use the same method to construct their IC reports. However, the fate of them is quite different. BankInter’s IC metrics seems to be stable, there appears to have no sign of predicting whether BankInter will continue to release IC reports or not. From these figures, it is interesting to note that the dominate part of these IC reports are IC metrics. In these reports, most of the indicators are represented by “numbers”. It means that the significant IC reports’ goals need “numbers” to fulfill, especially for BankInter and BBVA. In analyzing the goal the report, it is clear that the IC metrics is also responsible for “formulating their strategy”, “assessing strategy execution”, “assisting in diversification and expansion decisions”, “and used as a basis for compensation” as well as “communication measures to external stakeholders”(Bukh, 2003). It is conceived that the use of numbers to measure IC can make management to be comfortable and it also offers a clear direction of action (Dumay, 2009). However, managing IC metrics does not bring the same comfort for employees. Even measurement may cause big trouble for the organizations(Sveiby, 2010). Moreover, the relevance of “numbers” in terms of organization strategy and internal management has been constantly questioned. It is argued that figures are unable to represent the initiatives directly and a piece of IC balance sheet cannot describe the accurate picture about the management behaviors(Mouritsen, 2006). The difficulty of integrating numbers into the context makes activities that related to the strategy unclear(Sveiby, 2010). What should be clear is that “Who are the customers?”, “What do they need?”, and “How value is created for the customers to obtain competitive advantage”(Bukh, 2003, Abeysekera, 2006) rather than static “snapshots” of customer satisfaction(Bukh, 2003), which is used by 4 out of the 5 sample companies except OeNB. It is already justified that merely obeying the fundamental discipline “What you measure is what you get!” indeed causes troubles in IC management (Dumay and Rooney, 2011). The narratives demonstrate a very important function in these reports in term of the introduction and description(Mouritsen et al., 2001). ATP and OeNB’s IC reports show that narratives can communicate the IC metrics in a practical context. This process helps to understand IC metrics in a clearer way(Sveiby, 2010, Mouritsen, 2006). Especially, the more efforts are put on the narrative by OeNB’s IC, which make the internal management process more transparent. However, not all IC reports use them in an effective way. BBVA and BankInter only use the narratives as the introduction of basic concepts and in a simple context.

4. Conclusion This paper presents a study of the IC reports in banking industry from 1994 to 2011. From the various frameworks that assist to report IC, it can be found that the initial barriers for reporting IC is not due to a lack of framework(Goh and Lim, 2004, Guthrie and Petty, 2000, Nielsen et al., 2006, Yi and Davey, 2010), what they lack is the insight into their internal organization. However, this argument doesn’t encourage organization to use the existing framework nor create their own IC reporting frameworks, which aim at enabling the organizations to study how IC works in the organization rather than just stop at the surface of IC concepts (Mouritsen, 2006, Dumay and Garanina, 2013). The process of implementing IC framework in practice which is described shows these aspects clearly(Demarrini and Paoloni, 2013). The goals of IC reports are magnificent, but the IC metrics and IC framework are ineffective for solving the problems associated with compiling an IC report. In some organization, when IC metrics disappear, IC reports also disappear. In some organization, they still put their efforts to develop the one page metrics regardless if they really works and deserve so much effort. The series of study that help to bridge the management and reporting gap by using narratives (Dumay and Cuganesan, 2011, Dumay and Rooney, 2011, Dumay, 2011). This shows that it is pressing for organization to cultivate the new skills to manage and report IC. The increasing domain that IC exists, the unstable and precarious of IC complexity woven in the stories as well as the bottom‐up methods demand more common employees involve in are all time‐consuming work (Demarrini and Paoloni, 2013, Dumay and Cuganesan, 2011). As a result, it is urgent to develop new tools to

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Linlin Cai, Eric. Tsui and Benny Cheung help facilitate the IC development. This commitment requires a multi‐disciplinary effort and should be given more attention in the future.

Acknowledgements The work described in this paper was fully supported by a grant from the Research Grants Council of the Hong Kong Special Administrative Region, China (Project No. 529210). The authors gratefully thank the council for supporting this research.

References abeysekera, I. 2006. The Project Of Intellectual Capital Disclosure. Journal Of Intellectual Capital, 7, 61‐77. Bontis, N. 2001. Assessing Knowledge Assets: A Review Of The Models Used To Measure Intellectual Capital. International Journal Of Management Reviews, 3, 41‐60. Bukh, P. N. 2003. The Relevance Of Intellectual Capital Disclosure: A Paradox? Accounting, Auditing& Accountability, 16, 49‐56. Demarrini, P. & Paoloni, P. 2013. Implementing An Intellectual Capital Framework In Practice Journal Of Intellectual Capital, 14, 69‐83. Dumay, J. 2008. Intellectual Capital In Action: Australian Studies. Dumay, J. 2009. Intellectual Cpaital Measuremnet: A Critical Approach. Journal Of Intellectual Capital, 10, 190‐210. Dumay, J. 2011. Intellectual Capital And Strategy Development: An Interventionist Approach. Vine, 41, 449‐465. Dumay, J. 2012. Grand Theories As Barriers To Using Ic Concepts Journal Of Intellectual Capital 13, 4‐15. Dumay, J. 2013. The Third Stage Of Ic: Towards A New Ic Future And Beyond. Journal Of Intellectual Capital, 14. Dumay, J. & Cuganesan, S. 2011. Making Sense Of Intellectual Capital Complexity: Measuring Through Narrarive. Journal Of Human Resource Costing & Accounting, 15, 24‐49. Dumay, J. & Garanina, T. 2013. Intellectual Capital Research: A Critical Examination Of The Third Stage. Journal Of Intellectual Capital, 14, 10‐25. Dumay, J. & Rooney, J. 2011. “Measuring For Managing?” An Ic Practice Case Study. Journal Of Intellectual Capital, 12, 344‐ 355. Edvinsson, L. 1997. Developing Intellectual Capital At Skandia. Long Range Planning, 30, 366‐373. Edvinsson, L. & Malone, M. S. 1997. Intellectual Capital: Realizing Your Company's True Value By Finding Its Hidden Brainpower. Edvinsson, L. & Sullivan, P. 1996. Developing A Model For Managing Intellectual Capital. European Management Journal, 14, 356‐364. Goh, P. C. & Lim, K. P. 2004. Disclosing Intellecutal Capital In Company Annual Reports: Evidence From Malaysia. Journal Of Intellectual Capital, 5, 500‐510. Guthrie, J. & Petty, R. 2000. Intellectual Capital: Australian Annual Reporting Journal Of Intellectual Capital, 1, 241‐251. Guthrie, J., Ricceri, F. & Dumay, J. 2012. Reflections And Projections: A Decade Of Intellectual Capital Accounting Research. The British Accounting Review. Kaplan, R. S. & Norton, D. P. 1996. Using The Balanced Scorecard As A Strategic Management System. Harvard Business Review, 74, 75‐85. Lee, L. L. & Guthrie, J. 2010. Visualising And Measuring Intellectual Capital In Capital Markets: A Research Method. Journal Of Intellectual Capital, 11, 4‐22. Mourisen, J., Larsen, H. T. & Bukh, P. N. 2001. Valuing The Fuure: Intellectual Capital Supplements At Skandia. Accounting, Auditing& Accountability, 14, 399‐422. Mouritsen, J. 2006. Problematising Intellectual Capital Research: Ostensive Versus Performative Ic. Accounting, Auditing & Accountability Journal, 19, 820‐841. Mouritsen, J., Larsen, H. T. & Bukh, P. 2001. Intellectual Capital And The ‘Capable Firm’: Narrating, Visualising And Numbering For Managing Knowledge. Accounting, Organizations And Society, 26, 735‐762. Nielsen, C., Bukh, P. N., Mouritsen, J., Johansen, M. R. & Gormsen, P. 2006. Intellectual Capital Statements On Their Way To The Stock Exchange: Analyzing New Reporting Systems. Journal Of Intellectual Capital, 7, 221‐240. Roos, J., Roos, G., Dragonetti, N. C. & Edvinsson, L. 1997. Intellectual Capital, Macmillan Business. Stewart, T. & Ruckdeschel, C. 1998. Intellectual Capital: The New Wealth Of Organizations. Performance Improvement, 37, 56‐59. Sveiby, K.‐E. 2010. Methods For Measuring Intangible Assets [Online]. Available: Http://Www.Sveiby.Com/Articles/Intangiblemethods.Htm. Yi, A. & Davey, H. 2010. Intellecutal Capital Disclosure In Chinese (Mainland). Journal Of Intellectual Capital, 11, 326‐347.

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Research on Intellectual Capital Elements Synergy in Research Organizations Li Ya‐nan, Xiao Jian‐hua, Cao Liu and Zhu Lin‐lin University of Chinese Academy of Sciences, Beijing, China Lyn518@yeah.net Xiaojh@ucas.ac.cn Caoliu11@mails.ucas.ac.cn zhulinlin_0507@sina.com Abstract: Research organizations take the important task of producing knowledge for our society, and they are the main forces of national independent innovation. Nowadays, the competition in science and technology at home and abroad has become increasingly fierce, and how to improve the research organizations’ competitive advantage and enhance their performance levels have been the focuses of researchers and research managers. Fortunately, study shows that the intellectual capital (IC) is the key to competitive advantage of research organizations. And what’s more, the synergy of intellectual capital elements is the prerequisite for the long‐term performance. This paper finds that IC is the core of the research organizations and gets a clear understanding of the roles of three IC elements. According to the theory of knowledge production mode and the innovation‐driven factors, the research organizations are divided into three categories. Respectively, this paper pointed out the IC elements characteristics of each kind of research organizations. What’s more, it offers a theoretical support to classification management in research organizations. An empirical study was then handled on six research institutes of which got 343 valid questionnaires. The empirical test results show that Chinese scientific research organizations’ strategic goals and their individual researchers’ targets are in a relatively higher degree of synergy in Type 2 organizations. To realize different goals, human capital needs different characters; and the value of research organization's human capital is positively influenced by the synergy degree of its structural capital and relational capital. Keywords: research organizations, intellectual capital elements, synergy, knowledge production mode, classification management

1. Introduction We are moving towards a knowledge‐based economy where intangible assets and investments are seen as essential elements to value creation to economic wealth (Canibano et al., 2000; Sánchez & Elena, 2006). If a knowledge‐based economy is mainly characterized by the production, transmission and dissemination of knowledge, universities and research organizations are unique in all these processes, “due to the key role they play in the three fields of research and exploitation of its results” (European Commission, 2003, p. 2).It would be safe to say that, research organizations are main producers of knowledge in our society and they are irreplaceably professional teams for national innovation systems. Increasing researches see intellectual capital as the key to research organizations’ competitive advantage and high performance (Leitner,2004; Secundo et al., 2010; Elena‐Pérez et al.2011). However, the lasting IC value does not arise directly from any of the single factor, but only from the interaction between all of them (Edvinsson and Malone, 1997).The synergy of IC elements, including human capital, structural capital and relational capital, is a prerequisite for the durable high performance and competitive advantage of research organizations in the long‐term. Although available literatures have proven the importance of elements synergy of IC, they haven’t explain the synergy mechanism of the three elements and in what way they can work with each other. So it can’t be an available guide to the management of research organizations. Meanwhile, a very famous concept named “Mode 2” identifies a number of important trends in science systems (Hessels & Lente, 2008).A new knowledge production mode (KPM) influences not only what knowledge is produced but also how it is produced; the context in which it is pursued, the way it is organized, the reward systems it utilizes and the mechanisms that control the quality of that which is produced (Gibbons et al., 1994).So, research organizations under different knowledge production mode objectively need intellectual capital elements with different characters to realize their knowledge innovation goals. Thus in Section 2, it firstly defines the IC elements, then reclassify research organizations according to the KPM Theory. Section 3 points out the key links of IC elements synergy and analyze the IC elements characters

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Li Ya‐nan et al. of each type. Section 4 and 5 are the empirical test parts. Finally, some preliminary conclusions are drawn in Section 6.

2. Theoretical preparation 2.1 The definition of intellectual capital elements Currently much more researchers are studying on the theory of Intellectual Capital, which has been realized to be significant. Stewart (1997) defines intellectual capital as the intellectual material that has been formalized, captured, and leveraged to create wealth by producing a higher valued asset in his book Intellectual Capital: The New Wealth of Organizations. Even though until now, researches and studies on IC have been mainly developed and used by for‐profit organizations（Benevene & Cortini, 2010）, some scholars began to realize the importance of intellectual capital to the non‐profit organizations, especially to research organizations. The intellectual capital of research organizations is the key to organizational performance and competitive advantage as well as the sum of knowledge and skills related to achieve the knowledge production targets. Generally, intellectual capital is made up of human capital, structural capital and relational capital (Martínez‐Torres, 2006). Human capital of research organizations is defined as knowledge and skills owned by personnel to achieve organizational goals. Since knowledge production couldn’t made out without intelligence, so human capital is the core of intellectual capital which is the final source of intelligence. Structural capital of research organizations is the structured and institutionalized knowledge and capabilities embedded within the organizations. Structural capital, which supports and improves the action of human capital, can promote the value of the latter (Bontis, 1996). Relational capital of research organizations represents the knowledge and capabilities to build, maintain and expand the relationship networks established with external stakeholders, bringing resources and information superiority to organizations. Research organizations make the value of IC realized through interacting with external stakeholders. In a word, human capital can’t run without the support of structural capital and the socialization function of relational capital. Structural capital offers “hard environment” and “soft environment” to human capital, while relational capital provides convenient ways for obtaining resources, exchanging and transferring achievements in research organizations.

2.2 Research organization classification model based on KPM In The New Production of Knowledge, it calls traditional mode of knowledge production as Mode 1 and the transformation emerging alongside traditional as Mode 2. Their view is that while Mode 2 may not be replacing Mode 1, Mode 2 is different from Mode 1 in nearly every respect (Gibbons et al., 1994). Van Aken (2001) mentioned research strategies can be described in terms of Mode 1 or Mode 2 knowledge production. Mode 1 knowledge production is dominated by an academic agenda, is largely executed inside academia, focused on analysis and on fundamental knowledge (as opposed to applied knowledge), has a preference for mono‐disciplinarity and its products are primarily shared with fellow researchers. Further dissemination occurs downstream of knowledge production and there is little interest in the exploitation of such knowledge by practitioners. If Mode I knowledge production is characterized by curiosity‐driven inquiry and a positivist epistemology, Mode 2 is in part at least, akin to (although not synonymous with) a problem‐solving epistemology (Estabrooks et al., 2008). The features of Mode 2 are as follows.

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Li Ya‐nan et al. First, Mode 2 knowledge is generated in a context of application. Of course, Mode 1 knowledge can also result in practical applications, but these are always separated from the actual knowledge production in space and time. This gap requires a so‐called knowledge transfer. In Mode 2, such a distinction does not exist. A second characteristic of Mode 2 is transdisciplinarity, which refers to the mobilization of a range of theoretical perspectives and practical methodologies to solve problems. Once theoretical consensus is attained, it cannot easily be reduced to discipline parts. Thirdly, Mode 2 knowledge is produced in a diverse variety of organizations, resulting in a very heterogeneous practice. The fourth attribute is reflexivity. Compared to Mode1, Mode 2 knowledge is rather a dialogic process, and has the capacity to incorporate multiple views. This relates to researchers becoming more aware of the societal consequences of their work (‘social accountability’). Novel forms of quality control constitute the fifth characteristic of the new production of knowledge. Traditional discipline‐based peer review systems are supplemented by additional criteria of economic, political, social or cultural nature. (Hessels &Van Lente, 2008) While, ‘This new mode – Mode 2 – is emerging alongside the traditional discipline structure of science and technology—Mode 1’ (Gibbons et al., 1994).Mode 2 is not believe to replace Mode 1, but supplement it. Discipline science supplies an inexhaustible deep well for future application. The two knowledge production modes correspond with two different innovation‐drive factors. Generated in a context of cognition, Mode 1 has a direct purpose of pushing forward discipline science. The innovation driven factor of researches under Mode 1 is discipline development; by pushing discipline science forward, it will promote social progress in the end. By contrast, generated in a context of application, Mode 2 has a final purpose of satisfying application demands. Researches under Mode 2 are driven by application demands, which can pull the whole innovation value chains. Based on the KPM theory and corresponded innovation drive factors, we classify research organizations into three types. The first type is called “discipline‐push type” (Type1), which is dominated by Mode 1 and with purpose of satisfying the objective of discipline development. The second type is called “application‐pull type” (Type2), and it is dominated by Mode 2 and undertaking research activities whose purposes are solving application problems raised from government agencies, industry communities or public welfare works. The third type of research organizations is engaged in both kinds of research activities mentioned above; it has double tasks, not only doing the discipline cutting‐edge researches but also solving problems raised from the practices. We name the third kind of research organizations “double‐drive type” (Type3).

Figure 1 Research organization classification model

3. The model development: Hypotheses Synergy is the interaction of multiple elements in a system to produce an effect different from or greater than the sum of their individual effects (Wikipedia，2013). Synergy relates to elements’ interaction and cooperation. In this paper, we mainly to explore what kinds of IC elements can cooperate with each other and how the synergy comes. The goal of research organizations is to produce knowledge. This target realized process can be divided to three parts. Firstly, because researchers are the basic members of the organizations, if the researchers’ goals

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Li Ya‐nan et al. are different from the organization’s goal, then it must be difficult for the organization to realize its goal; so it needs the harmonization of the organization’s goal and the member researchers’ goal. Secondly, as mentioned later, different type of research organizations needs researchers with different characters to do specific research work; so to realize the given goal, research organizations need human capital in coordination. Finally, human capital needs corresponding structural capital and relational capital to support it. This led us to three hypotheses to be examined in the study. Hypothesis 1: Compared to Type1 research organizations, the researchers’ individual research targets in Type2 research organizations are more cooperative with their organizations’ targets. Research organizations usually have their own strategic orientation and strategic target. Strategic orientation is an indication of the direction in which an organization wants to or should go in the future to realize its strategic target. The strategic orientation decides the scope and distribution concentration of the resources in research organizations. For the Type1 research organizations, their main research work is usual long‐term and task‐uncertain, which calls for the freedom of scientific exploration. While Type2 research organizations often has established goals and assured strategic orientation, so the researchers of Type2 organizations are used to have more concordant research goals with their institutions compared to their counterparts in Type 1. Hypothesis 2: human capital from Type1 and Type2 research organizations has different characters. Researchers in organizations of Type1 must possess profound knowledge of their related disciplines and have a deep understanding of research fields to grasp the development law of disciplines as well as contribute to frontier research. Besides, the period of research activities like this kind generally lasts long and with uncertain results. Researchers usually "dominated by curiosity to explore the unknown". What’s more, it allows the researchers to be open minded and divergent thinking, without sticking to the traditional mode of thinking or disturbed by the others. Only take courage to try the new ideas and methods, can they have more probability to success. Due to the researchers’ novel ideas and little communication to outside except academic exchange, it’s common to see they can hardly get understanding and support from peers in their circles. So, researchers working in the Disciplines‐push Type organizations always leave unsociable and maverick impression to others. Finally, they pay more attention to the academic value of the outputs rather than practical value of it. As for Type2 organizations, their characteristics of human capital are as follows. First of all, researchers should possess broad knowledge and transdisciplinary professional background because of blurred boundaries between disciplines and practical problems (Gibbons et al., 1994). Next, research activities are carried out in the form of application projects, scientists are motivated by the tasks and limited to the specified deadlines. Then, since there are always many researchers participating in the collaboration among different disciplines, researchers need to have the ability to communicate with the external from getting external knowledge to transferring the research results. In addition, pragmatism is essential to researchers since no matter what kind of research activities they engage in, theoretical or applied research, both under the guidance of the final application goal of knowledge. Based on the analyses above, we come to four sub‐hypothesis. Hypothesis 2.1: researchers doing type1 research have profound discipline background, while researchers doing type2 research tend to have trans‐discipline backgrounds. Hypothesis 2.2: researchers doing type1 research are interest‐driven, while researchers doing type2 research tend to be motivated by tasks. Hypothesis 2.3: researchers doing type1 research like to do independent thinking, while researchers doing type2 research are good at cooperation. Hypothesis 2.4: researchers doing type1 research think highly of academic value in the research outputs, while researchers doing type2 research tend to pursue the applied value. As to the relationship between human capital and structure capital or relational capital, we come to the hypothesis 3.

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Li Ya‐nan et al. Hypothesis 3: the human capital value (HCV) is positively affected by the synergy degree of structural capital and relational capital. From the Hypothesis 2, we can see there are big differences between the human capital characters from Type1 and Type2 research organizations. So we can say the structural capital and relational capital they need should be different too. If the structural capital is corresponding with the human capital, we say that the synergy degree of structural capital is high, thus it will give human capital more support than otherwise and increase the human capital value as well. It’s the same as to relational capital. We will then discuss several characters of structural capital and relational capital to see the differences. As to Type1 research organizations, “Skirmisher tactics” generalized by Chen‐Ning Yang, which is referred to infrastructure‐oriented strategy, focusing on creating a stable and free internal environment, involving appropriate hardware facilities, management system and operation mechanism. Therefore, “Skirmisher tactics” emphasize building structural capital, in the purpose of making individuals workers autonomy and developing their creativity to the most extent. Under the circumstance of free exploration, researchers always determine their areas and directions through their own interest and curiosity. Thus the organizations carry out bottom‐up decentralized Decision‐making mechanism. Scientists are used to lead their assistants working in small‐sized research groups, their results are reviewed by academic peers (Gibbons et al., 1994).In terms of external relationship, most of the research funding is supported by the government as research activities are usually separated from the actual production activities. Little active cooperation is found between the Disciplines‐push Type organizations and enterprises. Compared with the Disciplines‐push Type, most Application‐pull Type organizations run in the “problems‐solving tactics”. They build project platforms according to the dynamic demands. They usually conduct Top‐down Centralized Decision‐making mechanism, focusing on goals with applied prospects. Every member of the organizations commits to the required assignments through their efforts. Since most projects are massive, large platforms and teams are needed to achieve. The evaluation on the applicability and sociality of results is also particularly important besides the academic evaluations. When managing the research activities, Application‐pull Type organizations are more likely to depend on the demands of the knowledge market. Consider the aspects of relational capital, no longer just the government need knowledge production after the transition of knowledge production mode. Enterprises have become another important stakeholders and funding providers. The relationship among the government, enterprises and research organizations are much closer, the government and enterprises will propose requirements to research organizations due to their own needs, with knowledge production into the “custom trigger era”. As a result, Research needs to meet the multiple needs and recognition. According to the components of structural capital and relational capital (we will discuss later), we design six sub‐hypothesis to Hypothesis3. Hypothesis 3.1: HCV is positively affected by the synergy degree of research platforms. Hypothesis 3.2: HCV is positively affected by the synergy degree of organizational systems. Hypothesis 3.3: HCV is positively affected by the synergy degree of organizational culture. Hypothesis 3.4: HCV is positively affected by the synergy degree of relationship with government or enterprises. Hypothesis 3.5: HCV is positively affected by the synergy degree of relationship with academic peers. Hypothesis 3.6: HCV is positively affected by the synergy degree of relationship with the public.

4. Research methods 4.1 Sample and data We choose typical institutions to make a questionnaire survey, based on research organizations classification model previously constructed. The institutions we choose are independent research units on strategy and systems, with clear organizational strategy, rules and norms. And the sample institutions are typical ones in Type1 or Type2, which has obvious tendency to research direction and strategy position. Six research institutes

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Li Ya‐nan et al. were chosen, three of Type1and three of Type2. The survey questionnaires were completed by the researchers. The data were collected mainly from three aspects as follows, Statistical Yearbook of Chinese Academy of Sciences and internal statistics, websites of institutions as well as questionnaires, which is the major source of the data. We sent the questionnaires to the formal researchers in those institutions (including postdoctoral). We’ve sent a total of 1000 questionnaires and received 355 feedbacks, and the recovery rate is about 35.5%. After excluding the invalid ones, we got 343 valid questionnaires. Then we used SPSS software to calculate Cronbach's Alpha coefficient to take questionnaire scale reliability analysis. The Cronbach's Alpha coefficient is 0.828, much higher than the acceptable value of 0.7 (Lee Cronbach, 1951), which means the designed questionnaire is reliable.

4.2 Measures We established the indicators of the degree of intellectual capital synergy based on extensive literature and research achievements in related areas, such as Bontis(1996),Spencer(1994),Shih et al.(2010),García‐Álvarez (2011),Xiao Jian‐hua & Niu Yu‐ying(2012),etc. Human capital mainly embodied in researchers' knowledge, abilities and attitudes, leading to the human capital synergy measured by these three dimensions. Structural capital consists of organizational research platforms, systems and culture and so on. Research platforms are essential support, including laboratory equipment and databases, as well as knowledge‐sharing channels; institutions contain strategy decision‐making mechanism, organizational structure and incentive evaluation mechanisms; culture is expressed by innovation atmosphere and relationship among personnel in research organizations. Relational capital is the network established between research organizations and external stakeholders. The stakeholders mainly include the government, academic peers, industry and the public.

5. Results and discussions Hypothesis 1 (H1) Table 1 shows the some descriptive statistics of the variable “strategic synergy degree”.To examine the difference of strategic synergy degree between the two types, independent‐samples T Test was used. Table 1: Descriptive statistics of the variable “strategic synergy degree” strategic synergy degree Type1 Type2 Total Mean score a 3.53 3.73 3.63 Std. Error 0.827 1.049 0.91 a

Mean scores based on a scale 1=very independent to 5= very synergetic. The independent samples t‐test (t=‐2.025, df=330, p=0.044<0.05) shows that there is a significant difference between the two groups. The mean score of Type2 is higher than it from Type1, the mean difference is 0.206. This is mainly because of the different culture atmosphere between the two. Hypothesis 1 is proven. Meanwhile, the average score of strategic synergy degree for the total samples is 3.63 out of 5, which is not a very high score. This is probably because the strategic adjustment of the Chinese research institutes recently and the individual researcher’s academic goal is lagged from the change. Hypothesis 2 (H2) For examining the difference of human capital’s characters between the two types, chi‐square test was used. Table 2 shows the test result.”Cha1” means character1, matching to Hypothesis 2.1, and so on. The results show that human capital from the two type organizations has a significant difference in Character2 and Character4 (p<0.05). Researchers from Type1 organizations trend to pursue research freedom and think highly of the academic value of the outputs, while their counterparts are more used to the given project tasks and take the applied value seriously.

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Li Ya‐nan et al. Table 2: Chi‐square tests Cha1

Cha2

Items

value

Sig. (2‐sides)

Pearson chi‐square

1.497

0.827

Likelihood Ratio

1.869

0.76

Pearson chi‐square

8.442

0.038

Likelihood Ratio

8.53

0.036

Cha3

Cha4

Items

value

Sig. (2‐sides)

Pearson chi‐square

3.673

0.452

Likelihood Ratio

4.061

0.398

Pearson chi‐square

31.81

0.000

Likelihood Ratio

32.50

0.000

Samples don’t imply the difference in Character1and Character3, and the reasons maybe: for Character1, which says there is a difference in discipline background of human capital in the two types, however, the objects of our questionnaire survey are individual researchers, and this cannot give an overall picture of the discipline background character of the organizations’ human capital. That is to say, to individual researchers, there is no difference in the number of subjects they are good at, but it doesn’t mean there is no difference between the subjects involved for the whole organizations. As to the tendency of cooperation, it partly because of the nowadays scientific research project system. This system objectively needs the cooperative abilities of the researchers, but our survey finds that there still exists team scale difference between the two groups, the former group tends to do researches in the form of individuals or small research groups, and the later tends to have bigger research teams. Hypothesis 3 (H3) To examine the Hypothesis 3, Person’s coefficient and linear regression was used. According to the results in Table 3, both of the synergy degree of structural capital and relational capital has a meaningful relationship with the value of human capital. Table 3: Cross‐correlation matrix HCV Synergic degree of SC Synergic degree of RC HCV 1 Synergy degree of SC .351** 1 Synergy degree of RC .507** .207** 1

Note: Significant at p=0.01 According to the results in Table 4, except of the variable “research platform” and “public relationship”, the other four variables all have a significant positive effect on the dependent variable “the value of human capital”(p<0.01). The synergy degree of structural capital, including organizational systems and organizational culture, can predict 16% of the variation in human capital value. And the synergy degree of relationship capital, including the relationship with government or enterprises and relationship with academic peers, can predict 28.9% of the variation in human capital value. Research platforms do not have remarkable statistics meaning as predicted. This maybe because that nowadays researchers pay more attention to the soft environment such as organizational systems and culture, other than the hard environment such as the experimental equipment. The reason for that public relationship does not influence human capital value as expected maybe is the researchers were still working in the ivory tower and ignoring the demands of the society. Table 4 Linear regressions coefficient and t‐statistic value (in the parentheses below the coefficient). Variable Constant

HC 2.378 (13.266)*** Research platforms 0.016 (0.612) Organizational systems 0.164 (5.260)*** Organizational culture 0.190 (5.048)*** R2 0.160

Variable HC Constant 3.175 (32.374)*** Government or enterprises relationship 0.107 (7.252)*** Academic peers relationship 0.097 (6.505)*** Public relationship 0.005 (0.222) R2 0.289

Note: Significant at p=0.01

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6. Conclusions: Classification management Most results from the study are as expected and significantly supportive to the hypotheses developed. The empirical analysis results show that: compared with the discipline‐push research organizations, the individual goals of personnel in the application‐pull research organizations are more collaborative to organization's strategic orientation (H1). Human capital corresponding to different types of research activities has different characteristic(H2), and human capital corresponding to discipline‐push research activity owns the feature of willing to do science exploration freely, think highly of the academic value of research achievements; and human capital corresponding to application‐pull scientific research activities has the characteristic of willing to accept challenge tasks, pay more attention to the application value of scientific research achievements (H2.2&H2.4). Moreover, the value of human capital has positively affected by the synergy degree of the structural capital and relational capital (H3), especially the synergy degree of the systems of organization, culture, and the relationship with the government and enterprises, as well as academic peers (H3.2‐H3.5). In reality, it’s hard to find a research organization under single driving forces, and almost all research organizations belong to Type3. The characteristics of Double‐drive Type organizations are aggregates of Type1 and Type2. In terms of human capital, researchers could work with interest as well as complete required tasks; they have not only solid discipline background but also trans‐discipline background; they possess divergent thinking, creativity, maverick and pragmatism, coordination, communication and cooperation capacity. In the aspect of structural capital, Double‐drive Type organizations are supported not only by stability platforms but also project platforms; there exist both Bottom‐up Democratic and Top‐down Centralized Decision‐making mechanism; their research units vary from small research groups to large and medium‐sized teams; they are evaluated by peer review and multidimensional appraisal; there’s not only free atmosphere but also market‐oriented culture. Above all, any single system or culture would not accommodate both opposed needs of the research activities. Only multi‐culture and classification management can make these two research activities keep pace without interference. Lastly, the activities in Double‐drive Type organizations are connected to different stakeholders, so they should develop relationships with them separately. This study also has its limitations. The data in this paper were relatively subjective coming from questionnaire survey. Six typical research organizations were chosen, even though the samples are representative, but it may not valid for research organizations in other disciplines. Lastly, a further case study could examine the IC elements synergistic effect closely and deeply.

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Sydney, Australia

11th International Conference on Intellectual Capital Capital, Knowledge Management and Organisational Learning University of Sydney Business School The University Of Sydney Sydney, Australia

7-8 November 2014 For further information contact info@academic-conferences.org or telephone +44-(0)-118-972-4148