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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