ECIC 2013 Volume One Proceedings of The 5th European Conference on Intellectual Capital by ACPIL

Proceedings of the 5th European Conference on Intellectual Capital VOLUME ONE University y of the Basque q Country, y, Bilbao, Spain 11-12 April 2013

Edited by Lidia Garcia, Arturo Rodriguez-Castellanos and Jon Barrutia-Guenaga University of the Basque Country

A conference managed by ACPIL, UK

Proceedings of The 5th European Conference on Intellectual Capital University of the Basque Country Bilbao, Spain 11-12 April 2013 Edited by Lidia Garcia, Arturo Rodriguez-Castellanos and Jon Barrutia-Guenaga University of the Basque Country Bilbao, Spain

VOLUME ONE

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. Further copies of this book and previous yearâ&#x20AC;&#x2122;s proceedings can be purchased from http://academic-bookshop.com E-Book ISBN: 978-1-909507-15-9 E-Book ISSN: 2049-0941 Book version ISBN: 978-1-909507-13-5 Book Version ISSN: 2049-0933 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 search for the conference name. 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

viii

The Effect of Transformational Leadership on Product and Process Innovation in Higher Education: An Empirical Study in Iraq

Sawasn Al-Husseini, Ibrahim Elbeltagi and Talib Dosa

From Taxonomic to Networked Models of Intellectual Capital and its Development

Eckhard Ammann

Structural Capital, Innovation Capability, and Company Performance in Technology-Based Colombian Firms

Nekane Aramburu, Josune Sáenz and Carlos Blanco

A Structural Model for Organizational Justice in Universities Based on Intellectual Capital

Azizi Balvand, Fattah Nazem, Alireza Chenar and Omalbanin Sadeghi

What Makes an Enterprise Sustainable? Or: is “Green” Really “Green”?

Dina Barbian

Creating Virtual Mentoring Programs for Developing Intellectual Capital

Bob Barrett

The Impact of Intangibles on Value Creation: Comparative Analysis of the Gu&Lev Methodology for the United States Software and Hardware Sector

Leonardo Basso, Herbert Kimura, Juliana Saliba and Erica Braune¹

The Existence and Disclosure of Intangibles versus Corporate Financial Performance in France

Leonardo Fernando Cruz Basso, Evelyn Seligmann-Feitosa, Diógenes Bido and Herbert Kimura

The Influence of the Process of Measuring IC on Performance

Donley Carrington

The Distinctiveness of Knowledge Sharing Processes Within Multinational Companies

Vincenzo Cavaliere and Sara Lombardi

The Influence of Relational Capital on Product Innovation Performance at Innovative SMEs

Pedro Figueroa Dorrego, Ricardo Costa and Carlos Fernández-Jardon Fernández

The Role of ISO 14001 in Sustainable Enterprise Excellence

Tijana Durdevic, Cory Searcy and Stanislav Karapetrovic

Measuring the Impact of Services Innovation: What do we Know?

Susanne Durst and Anne-Laure Mention

108

Socio-Ecological Innovation: Strategic Integration of Innovation for Sustainability and Sustainable Innovation

Rick Edgeman and Jacob Eskildsen

114

Sustainable Enterprise Excellence: The Springboard Model and Assessment

Rick Edgeman and Jacob Eskildsen

123

Coupling with Standardisation and Diversity: Intellectual Capital Reporting Guidelines for European Universities

Susana Elena and Karl-Heinz Leitner

132

IC Management in Universities: Where is Teaching?

Susana Elena and Katja Pook

142

Paper Title

Author(s)

Page No.

The Role of Human Capital and Customer Capital in Supporting Product Innovation

Ahmed Elsetouhi and Ibrahim Elbeltagi

154

Effect of Investments on Training and Advertising on the Market Value Relevance of Intangibles

Lidia Garcia-Zambrano, Arturo RodriguezCastellanos and Jose Domingo Garcia-Merino

164

Intellectual Capital: An Accounting Change Perspective

Marco Giuliani

174

The Five Cs of Intellectual Capital: Two Additional Dimensions of Assessment

Uma Gupta and Joseph Azzopardi

182

Developing & Measuring Intellectual Capital: A Conceptual Model for High Technology Companies

Harold Harlow

187

The Impact of Gender and age on Knowledge Absorption: An Empirical Study on NGO Beneficiaries in Bangladesh

Sheikh Shamim Hasnain

195

Intellectual Capital in Developing Micro-States: The Case of Caribbean SMEs

Lennox Henry and David Watkins

204

Towards a Model for Measuring University Sustainability

Raine Isaksson, Mikael Johnson and Rickard Garvare

213

Architecting the Dynamics of Innovation

Ton Jörg and Stephanie Akkaoui Hughes

222

The Identification of Polish Banks Intangibles’ Significance and Efficiency

Monika Klimontowicz and Janina Harasim

231

A Structural Model for Social Capital in Banks based on Quality of Work Life

Anahita Madankar and Fattah Nazem

241

The Effect of Intellectual Assets and Intellectual Liabilities Disclosure on Financial Performance: An Empirical Analysis of Publicly Listed Companies in the United Arab Emirates

George Majdalany and Jeffrey Henderson

248

Intellectual Capital Development in Business Schools: The Role of “Soft Skills” in Italian Business Schools

Maurizio Massaro, Roland Bardy, Maria Teresa Lepeley and Francesca Dal Mas

259

Intellectual Capital Management: From Theoretical Model to a Practice Model

Florinda Matos

266

What is Intellectual Capital Management Accreditation?

Florinda Matos, Albino Lopes and Nuno Matos

279

Intellectual Capital and the System of Organisational Management

Ludmila Mládková

290

Validation Scale for Measuring Social Capital in Higher Education Institutions

Fattah Nazem and Madankar Anahita

297

Intellectual Capital’s Leverage on Shareholder Value Growth: A Lesson for Developing Economies

Bongani Ngwenya

303

Managing Intellectual Capital in the Information and Communication Industry: The Spanish Case

Maria Obeso, Maria Jesus Luengo and Maria Angeles Intxausti

314

Towards Corporate Sustainability – a Small and Medium-Sized Enterprise Perspective

Ronald Orth and Holger Kohl

323

Intellectual Capital Growth Model: Using IC Measurement Logic on AK Endogenous Model

Stevo Pucar

333

Paper Title

Author(s)

Page No.

How to Build Innovative Knowledge High-Tech Companies: An Exploratory Analysis of 22@ Companies

Maria Pujol-Jover and Enric Serradell-Lopez

344

The Impact of Corporate Governance Indicators on Intellectual Capital Disclosure: An Empirical Analysis From the Banking Sector in the United Arab Emirates

Muhieddine Ramadan and George Majdalany

351

Knowledge Management Practices as the Basis of Innovation: An Integrated Perspective

Maria João Nicolau Santos and Raky Martins Wane

362

Product Innovation Building, the Relevance of Human Capital: A Case Study

Helena Santos-Rodrigues, Luis Lousinha and Desireé Cranfield

371

Intellectual Capital and Innovation: A Hospital Case Study

Helena Santos-Rodrigues, João Faria, Carminda Morais and Desireé Cranfield

376

Human Capital and Financial Results: A Case Study

Helena Santos-Rodrigues, Guiomar PereiraRodrigues and Desireé Cranfield

384

Measurement of Intellectual Capital for Innovation

Sabina Scarpellini, Miguel Marco, Alfonso Aranda and Estrella Bernal

389

Intellectual Capital Formation in EU Cross Border Regions: Theory and Application

Klaus Bruno Schebesch and Eduardo Tomé

398

Intellectual Capital Factors as the Basis for a Brazilian Competitive Intelligence System

Camilo Augusto Sequeira, Markus Will, Eloi Fernández y Fernández, Holger Kohl and Adeline Du Toit

409

Disclosing Intellectual Capital in Tertiary Education: From Necessity to Reality

Marta-Christina Suciu, Luciana Picioruş and Cosmin Ionuţ Imbrişcă

419

Specificity of Corporate Value Creation in Different Types of Companies

Grigorii Teplykh

428

Millionaires and Intellectual Capital: An Empirical Study

Eduardo Tomé, Luliia Naidenova and Marina Oskolkova

436

ICBS Intellectual Capital Benchmarking System: A Practical Methodology for Successful Strategy Formulation in the Knowledge Economy

José Viedma Marti and Maria do Rosário Cabrita

445

Intellectual Capital (IC) in Social Media Companies: Its Positive and Negative Outcomes

Piotr Wiśniewski

455

Revived Brands as Intangible Assets: Two Qualitative Case Studies

Aleksandra Zaleśna

464

Building Intellectual Capital by Using Computer Technology for Vernacular Creativity and Well Being in Nursing Home Residents: An Action Learning Approach

John Zanetich

471

Human Capital Intangibles in Family Firms: Identification and Measurement

Patrocinio Zaragoza-Sáez, Enrique ClaverCortés and Hipólito Molina-Manchón

477

PHD Papers

485

Does National Culture Affect Intercultural Knowledge Transfer?

Dolores Bengoa

487

Intellectual Capital in the Higher Education Institutions of Latvia in the Context of International Trade

Airita Brenča and Rasma Garleja

495

iii

Paper Title

Author(s)

Page No.

The Impact of Customer Knowledge Management Process on Service Recovery Performance

Nehal El-Helaly, Ahmad Ebeid and Azza ElMenbawey

506

Questioning Prevailing Methodologies on IC, Knowledge-Intensity and Knowledge Creation

Yasmina Khadir-Poggi and Mary Keating

516

Innovation and Earnings for SMEs

José Manuel López Fernández, Francisco Manuel Somohano Rodríguez and Francisco Javier Martínez García

523

The Quality of Information in Project Management

Jana Malá, Ľubica Černá and Dagmar Rusková

532

Relational Capital: The Role of Sustainability in Developing Corporate Reputation

Patricia Martínez García de Leaniz and Ignacio Rodríguez del Bosque

539

Adopting a Trust-Based Framework to Generate Social Capital: Espousing Social Learning and Social Capital for Enhanced Innovation, Improved Performance and Competitive Advantage

Athar Mahmood Ahmed Qureshi and Nina Evans

548

Intellectual Capital Evaluation: Return on Assets Methods Versus Market Capitalization Methods

Agne Ramanauskaite and Kristina Rudzioniene

557

University Missions: Compatible and Complementary? Theory and Empirical Analysis Through Indicators

Mabel Sánchez-Barrioluengo

564

A Conceptualization Linking Intellectual Capital, Dynamic Capabilities and Performance of Knowledge-Intensive Service Firms

Corentin Vermeulen

573

Intellectual Capital Information in Organizations Prevalence and Correlations With Organizational Performance

Janet Wee and Alton Chua

581

WIP papers

591

Particular Aspects in the Intellectual Capital Management of the Romanian SMEs

Roxana Mironescu, Andreea Feraru and Catalin Drob

593

The Role of Intellectual Capital in the Entrepreneurial Firm Innovation

Helena Santos-Rodrigues and Liliana Alves

597

Non Academic The Aleatoric leadership role - The choreography of intellectual capital in the NGO (non-profit organization)

601 Paulina Święcańska

603

Preface These proceedings represent the work of presenters at the 5th European Conference on Intellectual Capital (ECIC 2013). The Conference is hosted this year by the University of the Basque Country, Bilbao, Spain on 11-12 April 2013. The Conference Chair Professor Jon Barrutia and the Programme Chair is Lidia Garcia, both from the University of the Basque Country in Bilbao. The opening keynote address is given by Philippe Leliaert from the Maastricht School of Management, United International Business Schools, University of Brussels, Belgium and Philippe will be talking about "Reputation: Currency in the Knowledge Economy". The second day of the conference will be opened Dr Eduardo Bueno Campos from the Faculty of Economics, Universidad Aut贸noma de Madrid, Spain. Eduardo will address the issue of Dynamic analysis of The Intellectual Capital: The role of The Entrepreneurship & Innovation Capital A third keynote will be presented by Dr Jos茅 M. Viedma from UPC Polytechnic University of Catalonia, Barcelona, Spain on the subject of "Wealth creation in the knowledge economy: The microeconomic dimension" A primary aim of this conference is to contribute to the further advancement of intellectual capital theory and practice. The conference provides a platform for presenting findings and ideas for the intellectual capital community and associated fields. The range of people, issues, and the mix of approaches followed will ensure an interesting two days. 141 abstracts were received for this conference. After the double blind, peer review process there are 45 academic papers, 12 PhD papers and 2 work-in-progress papers published in these Conference Proceedings. These papers represent truly global research from some 27 different countries, including Australia, Barbados, Belgium, Boznia and Herzegovina, Canada, Czech Republic, Finland, Germany, India, Indonesia, Iran, Ireland, Italy, Luxembourg, Poland, Portugal, Romania, Russia, Serbia,, Slovak Republic, Spain, Sweden, Switzerland, Turkey, United Arab Emirates, United Kingdom and the USA. We hope that you have an enjoyable conference. Lidia Garcia Programme Chair April 2013

Conference Committee Conference Executive Lidia Garcia, University of the Basque Country, Spain Dr Jan-Erik Krusberg, Arcada University of Applied Sciences. Helsinki, Finlnd Dr Jukka Surakka, Arcada University of Applied Sciences. Helsinki, Finland Arturo Rodriguez-Castellanos, University of the Basque Country, Spain Jon Barrutia-Güenaga, University of the Basque Country, Spain Min Track Chairs Dr. Anne-Laure Mention, Public Research Centre Henri Tudor, Luxembourg Dr. Bob Barrett, American Public University, USA Dr Karl-Heinz Leitner, Austrian Institute of Technology, Vienna, Austria Dr Susana Elena-Pérez, Institute for Prospective and Technological Studies (IPTS), European Commission Florinda Matos, School of Management and Technology – Polytechnic Institute of Leiria, Portugal and ICAA - Intellectual Capital Accreditation Association Rick Edgeman, Aarhus University, Denmark Dr Piotr Wiśniewski, Warsaw School of Economics, Warsaw, Poland Dr Helena Santos Rodrigues, School of Technology and Management, Viana do Castelo Polytechnic Institute (IPVC), Portugal Dr Kent V Rondeau, University of Alberta, Edmonton, Canada

Conference committee The conference programme committee consists of key individuals from countries around the world working and researching in the intellectual capital community. The following have confirmed their participation: Dr Carl Adams (University of Portsmouth, UK); Dr. Jose-Luis Alfaro Navarro (Universidad De Castilla-La Mancha, Spain); Prof. Dr. Eckhard Ammann(Reutlingen University, Germany); Dr Heli Aramo-Immonen (Tampere university of technology, Finland); Dr Derek Asoh (Ministry of Government Services, Ontario , Canada); Dr Bob Barrett (American Public University, USA); DR Denise Bedford (Kent State University, USA); Prof Luis Borges Gouveia(University Fernando Pessoa, Portugal); Dr Ahmed Bounfour (University Paris-Sud, France); Prof/Dr Constantin Bratianu (Academy of Economic Studies, Bucharest, Romania); Dr Edgardo Bucciarelli (University of ChietiPescara, Italy); Dr Sheryl Buckley (Unisa, South Africa); Dr Acma Bulent (Anadolu University, Eskisehir, Turkey); Dr Sladjana Čabrilo (University Educons, Sremska Kamenica, Serbia); Assoc. prof. Dagmar Caganova (Faculty of Materials Science and Technology, Slovak University of Technology, Slovakia); Assoc. prof. Milos Cambal (Faculty of Materials Science and Technology, Slovak University of Technology, Slovakia); Prof. Leonor Cardoso (University of Coimbra, Portugal); Dr Daniela Carlucci (University of Basilicata, Potenza, Italy); DrDonley Carrington (University of the West Indies, Barbados ); Dr. Shulien Chang (Ming-Chuan University, Taipei, Taiwan); Dr Yuan-Chieh Chang (National Tsing Hua University, Hsinchu, Taiwan); Dr Eggert Claessens (Reykjavik University, Iceland); Prof Magdolna Csath (Kodolanyi Janos University of Applied Sciences, Budapest, Hungary); Prof. Dr. Robert De Hoog (University of Twente, The Netherlands); Dr Maria de Lourdes Machado-Taylor (CIPES, Portugal); Dr Izabela Dembinska (University of Szczecin, Poland): Associate professor Mihaela Alina Dima (Bucharest University of Economic Studies, Romania); Dr John Dumay (The University of Sydney, Australia); Dr Magdi El-Bannany (University of sharjah - College of Business Administration, United Arab Emirates); Dr. Ibrahim M. Elbeltagi (Plymouth University, UK); Dr Susana Elena-Perez (Institute for Prospective Technological Studies (IPTS) and European Commission - Joint Research Centre, Spain); Dr Scott Erickson (Ithaca College, USA); Dr Olusegun Folorunso (University of Agriculture, Nigeria); Libor Friedel (NOVATIO Consulting, Czech Republic); Dr. Albrecht Fritzsche (Capgemini, Germany); Dr Tatiana Garanina (Graduate School of Management St. Petersburg State University, Russia); Lidia Garcia (University of the Basque Country, Spain); Dr. Santanu Ghosh (University of Burdwan, India); Dr Marco Giuliani (University of The Marche, Ancona, Italy); Dr. Valerie Priscilla Goby (Zayed University, United Arab Emirates); Gerald Guan Gan Goh (Multimedia University, Melaka, Malaysia); Dr Jorge F. S. G. Gomes (ISEG-UTL and CIS/ISCTE-LUI, Portugal); Dr Miguel González-Loureiro(University of Vigo, Spain); Dr Annie Green (George Washington University , USA); Dr Tuulikki Haaranen ( Arcada University of Applied Sciences. Helsinki, Finland); Dr Markus Hagemeister (Institute of Applied Business Economics, Spain); Aki Jääskeläinen (Tampere University of Technology, Finland); Ivan Janeš (Koncar - Power Plant and Electric Traction Engineering Inc. Zagreb, Croatia); Dr Aino Kianto (Lappeenranta University of Technology, Finland); Gan Kin (MARA University of Technology, Malacca, Malaysia); Mart Kivikas (Clausthal University of Technology , Germany); Prof. DI Guenter Koch(Execupery, Vienna, Austria); Dr Jan-Erik Krusberg ( Arcada University of Applied Sciences. Helsinki, Finland); Josephine Lappia (Hogeschool Rotterdam, The Netherlands ); Prof Rongbin Lee (The Hong Kong Polytechnic University, Hong Kong); Prof. João Leitão (Polytechnic Institute of Portalegre, Portugal); Karl-Heinz Leitner (Austrian Reseach Centers, Austria); Phillipe Leliaert (Maastricht School of Management, The Netherlands); Dr Antti Lönnqvist(Tampere University of Technology, vi

Finland); DR Victor Raul Lopez (University Of Castilla La Mancha, Spain); Dr Soulla Louca (Department of Management and MIS, School of Business, University of Nicosia, Cyprus );Prof Eugenio Lucas (Instituto politcnico de leiria, Portugal); Paul Lumbantobing (PT. Telekomunikasi Indonesia, Tbk, Indonesia); Dr Agnes Maciocha (Institute of Art Design and Technology, Ireland); Prof Maurizio Massaro (University of Udine, Italy); Florinda Matos (ISCTE-IUL, Lisbon, Portugal, Portugal); Dr Gordon McConnachie (Asia Pacific IC Centre, Hong Kong, Hong Kong); Dr Anne-Laure Mention (Centre de recherche public Henri Tudor, Luxembourg); Prof. Dr. Kai Mertins (Fraunhofer IPK, Berlin, Germany); Dr Clemente Minnone(Department of General Management, School of Management and Law, Zurich University of Applied Sciences, Switzerland); Sue Molesworth (Management Suite Harplands Hospital, UK); Maria Cristina Morariu (The Academy of Economic Studies, Romania); Dr Arturo MoraSoto (Carlos III University of Madrid, Leganes, Spain); Dr Kavida Mourouganandane (Pondicherry University, India); Dr Birasnav Muthuraj (New York Institute of Technology, Bahrain); Dr Domingo Nevado Peña (Facultad de Derecho y Cien, Spain); Dr Emanuela-Alisa Nica (Center for Ethics and Health Policy (CEPS) and University "Petre Andrei" Iasi, Romania); PhD Bibiana Njogo (Unversity of Nigeria Nsukka. Enugu Campus, Nigeria); Dr Jussi Okkonen (Tampere University of Technology, Finland); Dr Abdelnaser Omran (School of Housing, Building and Planning, Universiti Sains Malaysia, Malaysia); Dr Pavlos Pavlou (Department of Management and MIS, School of Business, University of Cyprus , Cyprus ); Dr Kalin Penev (Southampton Solent University, UK); Dr. Milly Perry (The Open University of Israel, Israel); Dr Stephen Pike (Intellectual Capital Services Ltd, London, UK); Dr Michael Pitts (Virginia Commonwealth University, USA); Roman Povalej (JPS Software GmbH, Germany); Dr Agnieta Pretorius (Tshwane University of Technology (TUT), South Africa); Visisting Prof. ludo Pyis(Hong Kong, Hong Kong); Prof Thurasamy Ramayah (Universiti Sains Malaysia, Malaysia); Dr Susana Rodrigues (Polytechnic University of Leiria, Portugal); Dr Kent Rondeau (School of Public Health, University of Alberta, Canada); Mukta Samtani (University of Pune, India); Dr María-Isabel Sanchez-Segura (Carlos III University of Madrid, Spain); Prof Helena Santos-Rodrigues (IPVC, Portugal); Dr Charles Savage (FOM Fachhochschule für Ökonomie und Management, Germany); Prof Dr Klaus Bruno Schebesch (Vasile Goldis Western University Arad, Romania); Prof Georg Simet (Neuss University for International Business, Germany); Dr Vinod Singh (Gurukul Kangri University Haridwar , India); Dr Christiaan Stam (INHolland University of Applied Sciences, The Netherlands); Constantinos Stavropoulos (InnoValue, Greece); Prof.Dr. Marta-Christina Suciu (Academy Of Economic Studies Bucharest, Romania); PhD. Jukka Surakka (Arcada-University of Applied Science, Helsinki, Finland); Dr Marzena Swigon (University of Warmia and Mazury, Poland); Christine Nya-Ling Tan (Multimedia University, Melaka, Malaysia); Dr. Eduardo Tomé (Universidade Lusíada, Famalicão, Portugal); Dr Mihaela Tudor(University Paul Valery of Montpellier 3, France); Ann Turner (Queen Margaret University, Edinburgh, UK); Geoff Turner (University of Nicosia, Cyprus); Dr Belén Vallejo (University of the Basque Country, Bilbao, Spain); Professor Jose Maria Viedma (Polytechnic University of Catalonia, Spain); Dr Orestes Vlismas ( Athens University of Economics and Business (AUEB), Greece); Vilma Vuori (Tampere University of Technology, Finland); Dr Jui Chi Wang(Hsing Wu College, Taipei County , Taiwan); Maria Weir (Independent Consultant, Italy); Dr. Piotr Wisniewski (Warsaw School of Economics, Poland); Prof Inge Wulf (Clausthal University of Technolog , Germany); Dr Malgorzata Zieba (Gdansk University of Technology, Poland); Dr Mahmoud Hassanin (Pharos University,Alexandria, Eygpt); Dr Amrizah Kamaluddin (Universiti Teknologi MARA, Malaysia);

vii

Biographies Conference Chair Jon Barrutia is a Professor of Management and Business Economics at the UPV/EHU (University of the Basque Country). At the same time, on the one hand, he is a researcher of Business and Economy Research Institute of the Basque Country University since 1985, and the head of management and Business Economics department of the UPV since 2009; on the other hand, he teaches in courses related to the subject matter of Organizational Theory and Public Management, and the Undergraduate, MBA, Doctoral courses and the Executive Education.

Programme Chair Lidia Garcia Zambrano is a Researcher and Teacher in the Department of Financial Economics at the University of the Basque Country. Her research activities are oriented towards the fields of the knowledge management: assessment and financial valuation of intangibles. She is co-author of various articles in international scientific magazines. She is working on her thesis about financial valuation of intangibles.

Keynote Speakers Philippe Leliaert has over twenty years of experience in critically assessing and improving business performance. He gives advice on the strategic opportunities and challenges of the Knowledge Economy, and helps implement related organisational development & change. He focuses in particular of processes of collaborationand knowledge sharing as critical sources of sustainable competitive advantage. He is a visiting lecturer at several Business Schools, spanning South America, Europe and South East Asia. He is a regular presenter at conferences and seminars on the identification, measurement and management of Intellectual Capital, and facilitator of workshops on Performance Management and Change. Philippe has a Master of Science in Electronic Engineering (Ghent) and in Opto-Electronic and LASER Devices (Heriot-Watt, Edinburgh), and obtained his MBA at INSEAD (France). He is Certified Internal Auditor (CIA) at the Institute of Internal Auditorssince 1988. He is presently enrolled in a DBA program at United International Business Schools, which he aims to complete by July 2013. Dr Eduardo Bueno Campos is Professor of Strategic Management in the Faculty of Economics at the Universidad Autónoma de Madrid. He is a Director in the Knowledge Management Research Area of the Scientific Park in Madrid and Chair of Business Administration and Director of the Knowledge Research Society . He is CEO at the University Institute of Research in knowledge and Innovation of Business Administration (IADE) and he is Vice President of the Spanish Association of Accountability and Business Administration (AECA). He is also CEO of the Iberoramerican Knowledge Network. Dr José María Viedma Marti is a Doctor of Industrial Engineering, a graduate in Economics and Professor at the U.P.C., Polytechnic University of Catalonia in Barcelona, Spain. He teaches on the subject of knowledge management, intellectual capital management and Knowledge-based development. He has held top executive positions in computer services and management consultancy firms. Actually he is the president of "Intellectual Capital Management Systems" and a founding partner of "M.A. Fusiones y Adquisiciones". He is an advisory board member of different journals. He is a regular speaker in international conferences and congresses, his current field of research and interest is focused on knowledge and intellectual capital management and he has consulted and developed management frameworks and systems worldwide on those matters.

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Biographies of Presenting Authors Ercilia Garcia Alvarez is a Professor at the Business Management Department at the Universitat Rovira i Virgili. Ercilia is the Principal researcher of the Qualitative Research in Leisure Markets and Organizations (Qualocio) research group and senior researcher of Consumption, Markets and Culture (CMC) research group of the Universitat Autònoma de Barcelona.President of the Spanish Association for the Advancement of Qualitative Research (Espacual). Prof. Dr. Eckhard Ammann is a professor for computer science at the Reutlingen University, Germany, since 1992. Before that, he spent 8 years with the IBM Company doing research and development in parallel systems and system structures. His research interests include knowledge management, intellectual capital, business process modeling, distributed systems, and virtual organisations. Nekane Aramburu is PhD in Economics and Business Administration and member of the Strategy and Information Systems Department of Deusto Business School (University of Deusto, Spain). She specializes in the fields of Organizational Learning, Change Management, and Business Organization. Her research focus is currently on Organizational Learning, Knowledge Management, and Innovation. Zeinab Azizi Blavand has eight years of research experience at university, authored international article and her study field of interest is intellectual capital. Dr Joseph Azzopardi BA(Hons), MSc(Manc), PhD(Manc) .Joseph is Head of the Department of Management at the University of Malta. His research interests include all aspects of Human Resource Management and Development with special focus on small enterprise, Action Learning and Action Research, business and community development, organisational learning and knowledge management. Dr. Dina Barbian is Research Assistant at the Chair for Information Systems, Friedrich Alexander University of ErlangenNuremberg (Germany). Her main research interest lies in Sustainable Development. She holds a Master’s Degree in Industrial Engineering and a Doctoral Degree from the Faculty of Economics, University of Kaiserslautern (Germany). She lectures on "Sustainable Development and Macroeconomics". Dr. Bob Barrett is a professor for the School of Business at the American Public University in Charles Town, West Virginia, USA. He lectures both nationally and internationally on the topics of Intellectual Capital, Knowledge Management, Disability in the Workplace, e-Portfolios, and e-Learning. Leonardo Basso Ph.D: New School for Social Research - New York. Professor of finance: Graduate program in Business Administration - Mackenzie Presbyterian University, São Paulo, Brazil. Dolores Bengoa Ass. Prof. International Business School at Vilnius University, Lithuania is an experienced international coach and lecturer since 1992 in the field of cross cultural management, communication and intercultural knowledge transfer. She has a doctoral degree from Leeds Metropolitan University (England) as a result of her degree uses innovative and proprietary tools and techniques to improve the transferability of knowledge in cross-border business co-operations. Carlos Blanco has a PhD in Economics and Business Administration. He has been Associate Professor in the Pontificia Universidad Javeriana (Bogotá, Colombia), in the fields of Knowledge Management and Innovation. Nowadays he is consultant in these domains, having worked for different organizations: Ministry of National Education of Colombia, TES-America, Universidad Eafit, etc. Airita Brenča is a third year PhD student at the University of Latvia; she worked at the private University, Higher School of Management and Social Work ‘Attīstība’ for 15 years as an academic and administrative staff and at present she is the Head of the Department of Development Planning and Project Management in Lielvarde Municipality. Maria Do Rosario Cabrita holds a PhD and is Assistant Professor and researcher at the Universidade Nova de Lisboa, Portugal, and teaches at the Portuguese Banking Management School in Lisbon. She has several years of experience in executive positions in international banks. Her current field of research is focused on intellectual capital, knowledge management and measuring intangibles. Donley Carrington is a Lecturer in Accounting, at the University of West Indies, Cavehill Campus, Barbados. Donley Graduated UWI, Iowa State University, USA, Institute of Management Accountants USA and University of Hull, UK. Donley’s PhD thesis “An exploratory study of Intellectual Capital in the hospitality industry in the Caribbean”. Donley researches Intellectual Capital, Strategic Cost and Management Accounting. ix

Vincenzo Cavaliere is 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). Ľubica Černá, PhD has been working since 2001 on MaterialsEngineering Faculty, STU, mainly dealing with business economics/business ethics, and social dialogue. Ľubica is an author of over 90 scientific publications/publications in field of economics, economic ethics and project management. Member of team of authors working on research projects in field ofproject management/economics. Ricardo V. Costa is an Auxiliary Professor at ISMAI - Instituto Superior da Maia and a researcher at UNICES. He graduated in Economics at Universidade do Porto, and received is Phd in Business Management from Universidade de Vigo, in Spain. He got an Executive MBA in Business Strategy from Escuela de Negocios Caixanova, in Vigo, and attended the “Program in International Management” at Georgetown University in Washington. Luís Mesquita Diniz, Master`s degree in Economics from the Lusíada University of Lisbon. Attending a PhD in Economics at the Lusíada University. The thematic research focuses on Intellectual Capital and Training. Author of a paper on UFHRD EUROPE 2012 entitled “The influence of training in labor productivity”. Catalin Drob, Phd. in Management is working as a lecturer, at the “Vasile Alecsandri” University of Bacau, Engineering Faculty. His main areas of interest in teaching and research are: investment, management, project management and financial management. Tijana Durdevic is a grad student of Mechanical Engineering at the University of Alberta in Edmonton, Canada. She holds a Master of Industrial Engineering (MSc) from the University of Belgrade in Belgrade, Serbia. Currently, her research areas are Management System Standards and Implementation, Integration, and Auditing of Management Systems. Susanne Durst is Researcher at the Center for Knowledge and Innovation Research (CKIR) at Aalto University School of Economics and Assistant Professor at the Chair in International Management, Institute for Entrepreneurship, at the University of Liechtenstein. Her research interests include small business management, SME succession/transfer, IC management, knowledge management, innovation and corporate governance. Rick Edgeman is Professor of Sustainability and Performance in the Interdisciplinary Centre for Organizational Architecture at Aarhus University. He has previously chaired the Statistical Science Department at the University of Idaho (USA) and was QUEST Professor & Executive Director of the QUEST Honors Program at the University of Maryland. He has authored approximately 175 publications. Ibrahim Elbeltagi. Is a Senior lecturer in information and knowledge management, School of Management, University of Plymouth. Publications largely related to electronic commerce, adoption of ICT, information systems in developing countries, social networking and knowledge management. I have more than 40 journal and conferences papers published or accepted for publication in many national and international journals and conferences. Nehal El-Helal received her Bachelor degree in business administration from the faculty of commerce-Mansoura University. She is currently working as demonstrator in business administration department in the faculty of commerce-Mansoura University and is preparing for her masters thesis in customer knowledge management topic. Dr. Susana Elena Perez is currently a Scientific Fellow at the IPTS (European Commission). Worked as Lecturer at the Pablo de Olavide University (Spain), member of the PRIME Network of Excellence and participated in various European projects. She holds a PhD in Economics and Management of Innovation. Research interests: universities, management and governance, intellectual capital, and science and technology policy. Mr Ahmed Elsetouhi is assistant lecturer at Faculty of commerce, Mansoura University, Egypt. In 2009 till now, he is a PhD student at Business Management – Plymouth University. His research interests focus on intellectual capital, innovation, knowledge management, e-commerce- ICT and SEMs. He has published a paper at Journal of Global Information Management (3 star), and two conference papers. Jacob Eskildsen is professor of business performance management at Aarhus University and a member of the Interdisciplinary Centre for Organizational Architecture. Before entering academia Jacob worked as quality manager in a large multinational company. He holds an MSc and a PhD from the Aarhus University and is the author of more than 100 publications.

Andreea Feraru, doctorate student in Business Administration, is working as a assistant, in the “Vasile Alecsandri” University of Bacau, Faculty of Economics. His areas of interest in teaching and research are: management, knowledge management, intellectual capital, small and medium enterprises management, projects and business plans, human resources. Eloi Fernández y Fernández is a Mechanical Engineer, and is Professor of Mechanical Engineering at PUC-Rio and General Director of ONIP (National Organization of the Petroleum Industry - Brazil). Fernandez was Director of ANP (National Petroleum Agency – Brazil), State Secretary for Science and Technology, and Manager Superintendent at FAPERJ (Foundation for Research Support – Rio de Janeiro). Lidia Garcia Zambrano is a Researcher and Teacher in Department of Financial Economics at University of Basque Country. Her research activities are oriented towards the fields of the knowledge management: assessment and financial valuation of intangibles. She is co-author of various articles in international scientific magazines. She is working on her thesis about financial valuation of intangibles. Rasma Garleja is an Emeritus professor, Dr.paed., Dr.oec. She has been working as a professor at the University of Latvia since 1990; an author of the 6 monographs and 260 publications; at the moment continues to provide contribution to science at the Department of Development and Planning, University of Latvia. Rickard Garvare, Professor of Quality Management at the Division of Business Administration and Industrial Engineering, Luleå University of Technology, Sweden. Present research efforts are focused on adoption and implementation of quality related methodologies, exploring transitions from knowledge and practice. Marco Giuliani is assistant professor of Accounting and Business Administration at the Università Politecnica delle Marche (Ancona - Italy). His main research interests are in financial accounting, Intellectual Capital accounting and time accounting. He is member of national and international research groups on Intellectual Capital, financial accounting and company valuation. Dr. Uma Gupta is Professor of Management. She served as President of the State University of New York, Dean of Technology at the University of Houston, and Endowed Chair at Creighton University. She holds an MBA and PhD from University of Central Florida. She has authored two text books and more than 65 publications. Dr. Harold D. Harlow has over twenty years of experience developing new products, managing emerging businesses in wireless , aerospace and communications working for leading technology companies such as QUALCOMM, IBM, GE and Rockwell Collins as a vice president, director and product manager. Dr. Harlow researches and publishes in the areas of intellectual property and technology. Dr Sheikh Shamim Hasnain MBA and a PhD in Business and Management. Research mainly focuses on Knowledge Management, NGOs, Strategic Management, Operations Management and Military Strategies. Published in various international peer-reviewed journals. Prior to becoming an academic worked as a commissioned military officer (Major) in the Army. He has also worked as a peace-keeper under the United Nations. Lennox J Henry is a PhD student at Southampton Solent University who is in the final stage of his research; an accountant by profession, he has great interest in academia and the operationalization of IC as a tool for strategic management within organisations. Maria Angeles Intxausti is an Assistant Professor at Department of Applied Economics V at the University of the Basque Country, Spain. She has contributed to scholar area with articles and books. Her current work focuses on intellectual capital and innovation in the regional areas and clusters. Raine Isaksson is a Senior University Lecturer in Gotland University. Research interest is focused on synergies between process management and sustainable development. Another area of interest is sustainability in building supply chains with focus on building activities in Sub Saharan Africa. Additional work as cement and process consultant Dr. Eisa Ali Johali is a Saudi Expert in Health Education and Health Sciences Education born 11 Jan 1959, holding PhD in Health Sciences Hill University, USA with long experiences in curriculum, teaching and learning of Allied and Applied Medical Professions (AMPs). He has wide interests on philosophy, science, ethics and quality of AMPs Ton Jorg’s profession ion has been educational research. During the last 15 years, I got interested in complexity and education. I wrote a book about new thinking in complexity for the social sciences and humanities. Now I am seeking applications of such thinking for learning, education, complex (learning) organizations and innovation. xi

Stanislav Karapetrovic is a Professor of Mechanical Engineering at the University of Alberta in Edmonton, Canada, where he leads the Auditing and Integration of Management Systems Research Laboratory. Stanislav is spending the 2012/2013 academic year on a sabbatical leave, currently in Cartagena, Spain.

Yasmina Khadir-Poggi is a Doctoral student in the School of Business Studies at Trinity College Dublin. Besides, she lectures on International Business at American College Dublin. Her research interests include knowledge intensity in organisation, knowledge workers management and the subsequent knowledge-based development. Monika Klimontowicz is lecturer and a Ph.D. student at University of Economics in Katowice. Her latest research focuses on the role of intangibles in the process of achieving banks’ competitive advantage. Her interests include business strategy, innovation, knowledge and intellectual capital. She has been working as a marketing manager and business consultant. Karl-Heinz Leitner is a Senior Scientist, at the Austrian Institute of Technology and visiting Research Scholar at Copenhagen Business School. He Teaches Innovation Management at Technical University of Vienna. Karl Researches R&D and innovation processes, strategic management, research policy and valuation of intellectual capital. He has helped develop Austrian Intellectual Capital Model. José Manuel López Fernández. Assistant Professor, Coordinator of the Cantabrian SME Observatory`s proyects, Department of Business Administration. Area of Finance and Accounting, University of Cantabria, Spain. Maria Jesus Luengo , PhD, is Associate Professor at Department of Evaluation of Management an Busines Innovation at the University of Basque Country, Spain. She has been part of the university management as director of student and EFQM evaluator. Her current work focuses on intellectual capital and innovation in the regional areas. Anahita Madankar MA in Educational Management, with two articles in the International Conference and She is the scope of social capital. George Majdalany has a PhD and DPhil in Finance and Accounting, UGSM Monarch Business School, Switzerland. George has a CMA from USA; MBA in Finance and Accounting. Career includes working in Lebanon, Jordan, and United Arab Emirates in regional managerial positions in Finance and Accounting since 2001. Associate faculty, Departments of Finance and Accounting in several universities in United Arab Emirates since 2009. Senior CMA instructor since 2008. Patricia Martinez is a Associate Professor in Marketing and Market Research at University of Cantabria (Spain). Research interests include corporate social responsibility, corporate image and reputation, and consumer behaviour. Patricia has published in international impact journals such as International Journal of Advances in Management and Economics, Service Business or Journal of Travel and Tourism Marketing. Evandro Francisco Marques Vargas is a Professor-tutor presence of Pedagogy . Consortium UNIRIO/CEDERJ/UAB- RJ / BRAZIL . Special student of the MA in Political Sociology. State University of North Fluminense (UENF) -RJ/BRAZIL Luiz Eduardo Marques da Silva has a Ph.D. in Educational Policy. Master of Education. Teacher's Degree in Pedagogy in acting Discipline of Public Policy in Education. Federal University of the State of Rio de Janeiro (UNIRIO/Brazil)), Coordinator of Special Projects in Education and Culture (NUPEC). Maurizio Massaro is a aggregate professor, Udine University since 2008, having worked as teacher at Udine University since 2001. Maurizio is a visiting scholar, at the Florida Gulf Coast University, Florida, USA, in 2010. Academic interests primarily in measurement of business performance, intangible assets and entrepreneurship. He wrote several publications on these topics, and has some more forthcoming. Florinda Matos is completing her PhD in the area of Social Sciences. She was awarded a master's degree in business sciences by ISCTE Business School. She lectures at ISCTE - IUL, at Polytechnic Institute of Leiria and at Polytechnic Institute of Santarém. Currently, she is Intellectual Capital Accreditation Association president. Anne-Laure Mention is leading a research unit focusing on innovation economics and management within Public Research Centre Henri Tudor (Luxembourg). She is actively involved in research projects, mainly focusing on innovation and performance measurement and management in financial and business to business services industries. Research interests mainly concentrate on open and collaborative innovation, intellectual capital measurement and management, innovation and technology management. xii

Dr. Roxana Mironescu doctorate in Management, is working as a Senior lecturer, in the “ Vasile Alecsandri” University of Bacau, Faculty of Economics. His areas of interest in teaching and research are: management, human resources management, organizational behaviour, communication and negotiation. She also collaborates with some other educational professional institutions in Romania. Ludmila Mládková works as an associate professor at the University of Economics Prague, Faculty of Business Administration, Department of Management. She specializes in knowledge management, management of knowledge workers and managerial leadership. Her activities involve lecturing, writing and work with Ph.D. students. 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 90 articles. He is Chief Executive of the Quarterly Journal of Educational Science. Bongani Ngwenya. Is the Dean at the Faculty of Business, MBA Thesis Defense Panel Chair, Lecturer/Master’s thesis supervisor at Solusi University, Zimbabwe. Many years work experience in public/private sectors. Studying PhD in Business Management and Administration, specialising in Strategic Management (Grounded Theory Research), with North West University, Mafeking Campus, South Africa. Research interests in Organisational Decision-Making Research and general business Maria Obeso, PhD, is Assistant Professor at Department of Business Administration at the University of Cantabria, Spain. She has been Visiting Scholar in Business School at the University of Bedfordshire, UK. Her current work focuses on knowledge management and organizational behaviour. Ronald Orth MBA degree from Free University of Berlin. He has gained practical experience in banking business and media industry. Since 2003 is working as senior researcher for Competence Centre Knowledge Management at Corporate Management Division of Fraunhofer IPK, Berlin, focusing on research in intellectual capital management and business processoriented knowledge management as well as sustainability management and benchmarking. Marina Oskolkova was educated at Higher School of Economics University in Perm (Russia) and became a member of Center of Applied Economics at 2008. Since 2010 became an assistant professor of Department of Financial management (Faculty of Economics). Research interests related with intellectual capital valuation, value-based management and capital structure. Dr. Katja Pook is currently managing a faculty at University of Koblenz-Landau. Katja Studies in psychology, PhD thesis on process-oriented knowledge management. Katja has worked in different companies of industry and service branches and three European projects. Since 2008 Katja has been a independent consultant. Certified Systemic Consultant, ICR facilitator. Areas of expertise: Human factors in organizational development, Knowledge Management, ICM Stevo Pucar is an Assistant Professor at the University of Banja Luka, Banja Luka, Bosnia and Herzegovina. His main teaching and research areas are economic development, economic growth theory, intellectual capital, knowledge economy. He has authored significant number of academic articles and papers and has presented worldwide. He also works as a consultant for business and in public sector. Dr. Maria Pujol-Jover has a PhD in Business Studies by the UB. She is an assistant Professor at UOC and Lecturer at UB. She is a researcher of the IN3 in the group Observatory of New Economy. During 2010/11 Maria was a co-director of the group (2009 SGR 513). Focus research: SMEs in knowledge economy and the impact of ICT in SMEs productivity and competitiveness. Athar Qureshi graduated with honours degree in computer sciences, Masters in ICT Management and is now pursuing his PhD in Knowledge Management. He started his academic career with research and teaching assistantships, lectureship and consultation. Along with his academic commitments, Athar also advise voluntarily to not-for-profit academic associations. Agne Ramanauskaite is a PhD student of economics at the Vilnius University, which is the biggest high school in Lithuania. Her PhD research interests are evaluation of the intellectual capital of an enterprise, its disclosure and analysis. Agne’s practical work is financial auditing – she holds an auditor's certificate. She also teaches audit and tax policy at the Vilnius University. Dr. Muhieddine Ramadan is an Assistant Professor at University of Wollongong in Dubai “Faculty of Finance and Accounting”. Extensive knowledge in business procedures with strong background in Finance, Accounting, Economics and Statistics. Years of multinational corporate experience within IT and Telecommunications industries. Research interests include Financial Institutions and Markets, Corporate Finance, Business Finance, International Finance and Corporate Governance.

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Ignacio Rodriguez del Bosque Professor in Marketing and Market Research at University of Cantabria (Spain). Research interests include consumer behaviour, corporate image, ethics and corporate social responsibility. Published in international impact journals such as Tourism Management, European Journal of Marketing, Journal of Business Ethics, and International Journal of Research in Marketing, among others. Participates regularly in international conferences and colloquiums. Dr Dagmar Ruskova, PhD has been working at the Slovak University of Technology at the Faculty of Materials Science and Technology since 1990. She has been dealing mainly with teaching Russian and English languages, language projects and their presentations. She is the author of about 60 articles and she also translates the papers from management. Josune Sáenz is PhD in Economics and Business Administration and member of the Finance and Accounting department of Deusto Business School (DBS). She is also main researcher of the Innovation chair sponsored by BBVA at DBS. Her research focus is currently on Innovation, Intellectual Capital, and Knowledge Management. Bahram Salavati Ph.D. candidate in labor studies program at social and political sciences department, State University of Milan, Italy. Strong interest in high-skill labour markets, working on research themes related to high-skill workers mobility & migration, skill formation systems, human capital formation and development, education and vocational training systems, skill ecosystems and high-low skill equilibrium. Mabel Sánchez-Barrioluengo is a M. fellowship to develop her PhD at INGENIO (CSIC-UPV). Her research is based on economy of education: missions of the university, university-industry relationship and human capital. Simultaneously, she collaborates with medical researchers as a consultant in statistics, having published some scientific papers in this field. Maria Santos is an Assistant Professor at the School of Economics and Management, Technical University of Lisbon and Researcher at the SOCIUS/ ISEG. She received her Ph.D. in Economic Sociology and Organizations from the Technical University of Lisbon. Her research interests include Sustainable Development, Corporate Social Responsibility, Knowledge Management and Innovation. Helena Santos-Rodrigues is a European PhD in Business Management by the University of Vigo (Spain) and holds a MBA in International Marketing and Finances. Her research interests are: intellectual Capital, Knowledge Management and Innovation. She published many papers on intellectual capital, knowledge management and innovation issues. Dr. Sabina Scarpellini is Director of Socio-Economics Area at CIRCE and Part-time associate professor at the Department of Business of the University of Zaragoza (Faculty of Economy and Business). She is Masters in Energy Markets and PhD in Renewable Energy and Energy Efficiency (University of Zaragoza). She gained her professional experience in socioeconomics, management and energy at CIRCE Klaus Bruno Schebesch Professor of Marketing Research and Computational Management Science at Faculty of Economics and Faculty of Informatics, Vasile Goldiş Western University, Arad, Romania. PhD (1990) and post-doctoral Habilitation (2002), both from University of Bremen, Germany. Research interests: automated classification by statistical learning, knowledge and cultural features, e-learning and e-collaboration for new product design, innovation processes, social network dynamics. Cory Searcy is an Associate Professor and Director of Industrial Engineering at Ryerson University in Toronto, Canada. His current research interests are in sustainability indicators, sustainable supply chain management, and integrated management systems Camilo 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. Camilo has been top executive for multinational companies. He is currently a consultant and a researcher for the Institute of Energy of PUC-Rio. João José Soares Faria is a European Master in Management of Health Units by the Polytechnic Institute of Viana do Castelo (Portugal) and holds a Post Graduation in Management of Health Facilities and Social Institutions. His research interests are: Intellectual Capital, Knowledge Management and Innovation. He published a paper on intellectual capital, knowledge management and innovation issues. Prof. Ana Kerlly Souza da Costa Ma. in Public Policy and Human Formation. NUPEC - Center for Research and Special Projects in Education and Culture. University of the State of Rio de Janeiro - UERJ (Brazil) Professor Marta-Christina Suciu has a Phd in Economics. Graduate of Cybernetics Faculty, Academy of Economic Studies Bucharest (ASE), 1981. Research fellow, National Institute for Economic Research, Romanian Academy. Since 1993 teaching & xiv

research at ASE. Now full professor & PhD supervisor in Economics, ASE. Topic of interest: Knowledge-based society, intellectual capital, KM, creative economy, investing in people and skills. Paulina Święcańska Cultural event researcher, choreographer, performer, coach and manager educated at universities in Zielona Góra, Łódź and Warsaw (Poland). Professional experience through coaching programmes in most European countries, Israel, Brazil, India and Turkey. As culture leader and manager, co-organised Warsaw Independent Art Festival. Grigorii Teplykh has graduated from Perm State Technical University and National Research University Higher School of Economics (Perm, Russia). Since 2007 he works at Higher School of Economics. He is a professor of Department of Financial management and a researcher in Laboratory of investment analysis. His interests' area embraces corporate finance, innovation economy, intellectual capital and econometrics. EduardoTome is a Portuguese economist and made is PhD thesis on Vocational Training and the European Social Fund in the Institute for Economics and Management, Technical University in Lisbon (ISEG . UTL). Since then he pubilshed more than 20 papers in refereed Journals and more than 40 papers in Conferences Proceedings, He also organized MSKE 2009, ECKM 2010, MSKE 2010 and UFHRD Europe 2012 Conferences in Lusidada Famalicão University. His main interest is intangibles in any form: IC, KM, HRD, or even Social Policies and International Economics. Corentin Vermeulen is a PhD student at HEC Liège, ULg, Belgium and researcher at the Public Research Centre Henri Tudor in Luxembourg. Before starting his research career, he worked approximately two years at Ernst & Young, Luxembourg as a financial auditor. His research interests cover the link between intellectual capital and performance of service firms. Raky Wane is an Invited Researcher at the SOCIUS - ISEG (Research Center in Economic and Organizational Sociology) and Ph.D. student at the School of Economics and Management, Technical University of Lisbon (ISEG – UTL). Her research interests include Knowledge Management, Creativity and Innovation Processes. Janet CN Wee is currently a PhD student at Nanyang Technological University (NTU). Her research areas include knowledge management and intellectual capital information. Janet has worked for two international accounting firms in their knowledge management centers. She holds a MSc (Knowledge Management) (NTU) and a BSc (Mathematics) (National University of Singapore). Dr Piotr Wiśniewski is an Associate Professor, Corporate Finance, Warsaw School of Economics. Authored numerous publications focused on performance and socioeconomic ramifications of international collective investment schemes. Interested in intellectual capital centre on growth drivers existing within economic entities – particularly financial institutions. Executive experience in European financial services; chartered membership. Aleksandra Zalesna studied at Warsaw University of Technology at the Production Engineering Faculty. In 2005 I received a Ph.D. doctorate. My doctoral thesis was on the impact of motivation systems for managers on the business performance. Now I am interested in the field of intellectual capital. John Zanetich is a Full-time Professor with a BA in Psychology, MA in Clinical Psychology, MGA (Masters in Government Administration) , and a PhD in Organizational Sciences. I have worked in state, county and city government as Director of Mental Health Programs, Regional Director of Finance and Administration and Deputy Health Commissioner. Patrocinio Zaragoza-Sáez (PhD, University of Alicante, Spain) Associate Professor, Department of Management, University of Alicante, Spain. Researches knowledge management and intellectual capital in multinationals and family firms, and identification of intangible assets. Published in many Journals. Sawasn Al-Husseini is a PhD candidate, at Plymouth University School of Management, UK. Lecturer, Al-Mustansiriya University,Baghdad, Iraq. She has published five journal papers in innovation, leadership style, organizational loyalty, knowledge management, and sharing in Iraq. Recently she has published two papers on knowledge sharing in proceedings of the 10th ECEL, UK, and leadership and knowledge sharing in proceedings of the 4th ECIC in Finland.

The Effect of Transformational Leadership on Product and Process Innovation in Higher Education: An Empirical Study in Iraq Sawasn Al‐Husseini, Ibrahim Elbeltagi and Talib Dosa University of Plymouth, School of Management, Plymouth, UK sawasn.al‐husseini@plymouth.ac.uk i.elbeltagi@plymouth.ac.uk Talib_dosa@yahoo.com Abstract: Transformational leadership (TL) has been emphasized to be an important factor that affecting innovation because this style can elevate followers’ personal values and self‐concepts, Such leaders can promote both personal and organisational changes, spirit of trust, knowledge sharing and help them to exceed their performance expectations. With globalisation and a rapidly changing environment, the higher education sector in developing countries is facing challenges that require extraordinary leaders. Innovation is important for organisations particularly learning environments like universities. This research aims to examine the impact of TL (idealized influenced, inspirational motivation, intellectual stimulation, and individualized consideration) on product and process innovation. A questionnaire survey was used to collect data from public and private universities in Iraq. Total of (439) valid responses were collected. Data were analysed using structural equation modelling (SEM) with AMOS. The results support the importance of TL in enhancing innovation in the higher education sector. Within the public sector, individualised consideration increases the product and process innovation. While in the private sector, inspirational motivation was the strongest component. This research has developed some guidelines for researchers as well as leaders and provided evidence to support the use of TL to increase product and process innovation within higher education in developing countries particularly Iraq. The implications of the findings and suggestions for future research are discussed. Keywords: transformational, leadership, product, process, higher education, Iraq

1. Introduction Today the higher education sector faces a dynamic environment characterized by rapid technological change and increased demand for education quality (Mathew, 2012). As the world moves toward competition and innovation, leadership style has been identified as being the main driver of this innovation because the leaders can influence the introduction of new ideas and can create climate for innovation (Hislop, 2009). Albury (2005) describes innovation as the accepting, developing and implementing of new ideas concerned with product and process. The literature emphasizes that TL is the most important style in the field of leadership because this style accommodates emotion, values, attitude, and creativity in the followers and then develops innovation (Northouse, 2007). Within TL, the followers feel respect, trust, and loyalty toward the leader and they are thus willing to exceed their own personal expectations (Bass, 1985). Tichy and Devanna (1990) asserted that the power of TL lies in the visualization of the organisation. While Ismail et al. (2010) found that TL practices could lead to higher follower’s trust in the leader and this leads to improved individual performance. Betroci (2009) on the other hand believed that T leaders strengthen employees’ capacity to achieve by giving them the tools, knowledge, and resources to do the job. TL is fuel for innovation by promoting intellectual stimulation, providing inspirational motivation and self‐confidence among organisation members (Bass and Riggio, 2006). Higher education in developing countries like Iraq is facing rapidly changing challenges that require extraordinary leadership (Herbst and Conradie, 2011). Iraqi universities require unique leadership rather than traditional leaders who cannot be helpful in competing in the present educational environment. If the education in Iraq aims for a global reach, changes will be required in the following: systems, methods, curricula, approaches and specifically in the leadership style. Previous research asserted that TL is considered essential for innovation because it significantly affects the motivation of followers, it increases the determination to overcome crises and encourages the generation of new ideas, which are the core of innovation (Rafferty and Griffin, 2004). As TL is clearly important as shown by the research listed above its application in higher education in Iraq should be fruitful. However, it has rarely been examined in the education sectors in

Sawasn Al‐Husseini, Ibrahim Elbeltagi and Talib Dosa developing countries. Since innovation is important to both public and private organizations, particularly learning institutions like universities (Bodla and Nawaz, 2010). Thus, this research seeks to examine the impact of TL (idealized influenced, inspirational motivation, intellectual stimulation, and individualized consideration) on product and process innovation in Iraqi public and private higher education institutions. This topic is possibly most important and useful for leaders of higher education institutions in developing countries including Iraq. In the following sections the researchers will present an overview of the literature as the basis for the main and sub‐hypotheses. Then, the method used to analyse empirically the hypotheses, followed by the presentation of the results obtained. This will be followed by discussions of results of the research. After that, the contribution and the implication of the research will be presented. Finally, outline the main conclusions and points out some of the recommendations for future research and limitation of it.

2. Transformational leadership and product and process innovation Plessis (2007) described innovation as creation of new thoughts, knowledge, and ideas to make organisational outcomes possible. Since most organisations use the innovative activity and methods as a competitive weapon. Jaskyte (2004) focusing on the higher education sector distinguished that the innovation required to raise educational performance included both product and process. Accordingly, innovation in this research means accepting, implementing and developing educational products such as research projects, courses, teaching materials and curricula, and process innovation focused on service delivery to include new technology and performance related pay. Bass (1985) has characterized TL as encompassing four components. Firstly, idealized influence, which generates pride, faith, and respect because the leaders actively work to encourage their followers to have belief in themselves, and their organisation. Secondly, he describes Inspirational motivation, which involves stimulating followers by motivating them to be involved in the shared vision. Thirdly, he postulates that Intellectual stimulation, matters wherein leaders nurture followers to be more creative and innovative by making them think things out for themselves. Lastly, he suggested that Individualized consideration matters because leaders who pay personal attention and support followers, show confidence and appreciation of their work, and spend time listening to their individual needs generate loyalty. These behaviours of TL can play a vital role in helping followers to reassess their values, promote both personal and organisational changes, and help followers to exceed their initial performance expectations. Previous researchers suggested that TL is important for organisations because T leaders have an interactive vision and pay maximum attention to fostering effective communication and encouraging an appropriate environment for innovation (Avolio and Bass, 2002; Bass and Riggio, 2006; Morales et al., 2011). Similarly, Sosik et al. (1998) indicated that T leaders with inspirational motivation and intellectual stimulation can encourage ideas that promote product innovation. A survey of 78 leaders conducted by Howell and Avolio (1993) asserted that a significant relationship existed between intellectual stimulation and performance when there was a climate of support for innovation. Lee et al. (2006) on the other hand indicated that T leaders that using idealized influenced and inspirational motivation are more likely to enhance product innovation. While Chen et al. (2007) and De Jong (2007) claimed that TL can enhance creative thinking and intrinsic motivation of the followers. Furthermore (Chen and Lin 2012; Gumusluoglu and Ilsev 2009; Khan et al. 2009; Michaelis et al. 2010; Morales et al. 2011) findings suggested that T leaders that using individualized consideration is more likely to improve product innovation within organisation by motivating their followers and boost their self‐ esteem. Gunter (2001) argued that TL in higher education could facilitate the learning activities and creating an environment that enables and support knowledge. However, very little empirical research has examined the effect of TL (idealized influenced, inspirational motivation, intellectual stimulation, and individualized consideration) on product innovation in the education sector in developing countries like Iraq. Since innovation is important to both public and private organisations, particularly learning institutions like universities (Bodla & Nawaz, 2010). Thus, this research proposes the following hypotheses: H1: TL will positively affect product innovation in public and private Iraqi universities, this leads to the following sub‐hypotheses:

Sawasn Al‐Husseini, Ibrahim Elbeltagi and Talib Dosa H1a: idealized influenced will positively affect product innovation in public and private Iraqi universities. H1b: Inspirational motivation will positively affect product innovation in public and private Iraqi universities. H1c: Intellectual stimulation will positively affect product innovation in public and private Iraqi universities. H1d: Individualized consideration will positively affect product innovation in public and private Iraqi universities. Bass (1985) argued that leaders with intellectual stimulation can stimulate their followers to think about old problems in new ways and this should enhance process innovation. Jung et al., (2003) found a positive relationship between TL and process innovation. Khan et al., (2009) on the other hand indicated that T leaders that using idealized influenced and inspirational motivation can creates a culture of change that facilitates the adoption of process innovation. Thus, the leader that uses idealized influenced, inspirational motivation, intellectual stimulation, and individualized consideration is essential for innovation in higher education sector within developing countries. According to the argument above, this research proposes the following hypotheses: H2: TL will positively affect process innovation in public and private Iraqi universities, this leads to the sub‐ hypotheses the following hypotheses: H2a: idealized influenced will positively affect process innovation in public and private Iraqi universities. H2b: Inspirational motivation will positively affect process innovation in public and private Iraqi universities. H2c: Intellectual stimulation will positively affect process innovation in public and private Iraqi universities. H2d: Individualized consideration will positively affect process innovation in public and private Iraqi universities. Transformational Leadership

H1 H2

Idealized influenced

Product innovation

H1a H2a

Inspirational motivation H1b H2b Intellectual stimulation H1c

Process innovation

H2c

Individualized consideration H1d

H2d Figure 1: Suggested research model

3. Method This research used quantitative approach to explore the impact of TL on product and process innovation. A quantitative approach seeks to test theory so as to understand the measured phenomena (Saunders et al. 2009). A survey was conducted to test the research model, a self‐administrative questionnaire was developed, and pilot tested before the formal data collection. All independent and dependent variables were measured

Sawasn Al‐Husseini, Ibrahim Elbeltagi and Talib Dosa using 5‐point likert scale ranging from 1(strongly disagree) to 5 (strongly agree). The questionnaire was translated into the Arabic language using the back translation procedures. Some modifications were made to be suitable with Iraqi context, four Iraqi professors and two from UK examined face validity of the measurement items.

3.1 Measurements TL was measured using multi factor leadership questionnaire (MLQ) from 5x short (Bass and Avolio 2000). The MLQ has been extensively used in previous studies and has produced reliable results across difference cultures (Rohmann and Rowold 2009). 21 item including four constructs idealized influenced (7), inspirational motivation (5) items, intellectual stimulation (5) items and individualized consideration (4) items. Innovation was measured using 13 items: reflecting, accepting or developing new ideas concerned with product and process adopted from previous studies. Product innovation items were developed from (Perri 1993) and (Daft 1978), whilst process innovation was developed from work by (Tsai et al. 2001).

3.2 Questionnaire distribution The questionnaires were distributed by researchers using delivery and collection questionnaire to collect data. Members of staff from eight public and six private colleges in Iraq were randomly selected to receive the questionnaires. The population size is as follows; 4452 public and 992 private. 600 questionnaires were sent to the public colleges while 400 sent to the private colleges. 380 (63%) were received from public colleges and 291 (72.7%) from private. There were 224 usable questionnaires from public colleges and 215 usable questionnaires usable from private. The characteristics of the respondents, age, gender, tenure etc. are given in Table (1). The percentage of male respondents in private sector is 61.4% contrasted with 50.8% in the public sector. In terms of age, length of work experience and academic profession respondents were distributed across the different categories. For the academic qualifications, the majority of the respondents held masters or doctoral degrees in both sectors 92.1% in public and 95.3% in private. Table 1: Profiles of responding Characteristic Gender Male Female Age <29 30‐39 40‐49 50‐59 >60 Work experience <10 years 11‐15 16‐20 21‐25 >26 Academic qualifications Bachelor High diploma Master PhD Academic profession Assistant lecturer Lecturer Assistant professor Professor

Public N=224 Frequency

Percentage

Private N=215 Frequency Percentage

114 110

50.8 49.2

130 82

61.4 38.6

8 28 74 71 43

3.5 12.5 33.0 31.0 20.0

35 25 23 78 54

16.2 11.6 10.6 36.2 25.4

11 26 61 70 56

4.9 11.6 27.3 31.2 25

54 31 31 33 66

25.3 14.4 14.4 15.3 30.6

8 10 89 117

3.5 4.4 39.7 52.4

‐ 10.0 83.0 122

‐ 4.7 38.6 56.7

37 82 75 30

16.5 36.6 33.6 13.3

46 64 62 43

21.5 29.7 28.8 20.0

Sawasn Al‐Husseini, Ibrahim Elbeltagi and Talib Dosa

4. Results The causal relationship between TL and product and process innovation was identified. This research employed structural equation model (SEM) with AMOS.19 because it helps to build models, reflects complex relationships, and analyses direct effects (Hair et al. 2010). SEM consists of two steps; firstly a measurement model to evaluate the convergent validity of the constructs, and secondly a structural model to test and evaluate the causal relationships among factors.

4.1 Measurement model This research used firstly, exploratory factor analysis (EFA) by using principal component factor analysis method. This is known as the most commonly used approach in factor analysis with varimax rotation to produce factors, which are linearly independent (Field 2009). To evaluate the model convergent and discriminant validity were assessed through confirmatory factor analysis (CFA) using AMOS. The researchers tested convergent validity by investigating factor loadings which are significant in 0.5 or higher (Hair et al., 2010). Thus, the items with factor loadings less than 0.5 were deleted from the scale. Ten items did not make a good contributions of their predicted constructs, therefore were deleted from the scale to improve the model. Additionally, average variance extracted (AVE) measure was used to measure validity which should be significant at more than 0.5 (Fornell and Larcker 1981). Reliability was assessed separately for each dimension included in the model dependent on the rule of the Cronbach Alpha and composite reliability (CR) each of which should exceed 0.7 (Byrne 2010). The results shown in table (3) indicate that convergent validity and internal reliability are satisfactory. All factor loadings, CR and AVE were acceptable and significant. For instance, factor loadings ranging from (0.916) to (0.718), CR from (0.92) to (0.87) and AVE (0.68) from to (0.75), while internal reliability ranged from (0.87) to (0.90) for both sectors. Table 2: Results of CFA and internal reliability Constructs Idealized influenced

Inspirational motivation

Intellectual stimulation

Individualized consideration

Product

Process

Items ID12 ID2 ID6 ID17 INM5 INM9 INM10 INM11 ITS7 ITS14 ITS15 IST16 INC18 INC19 INC20 INC21 PRD23 PRD26 PRD34 PED28 PRC24 PRC30 PRC22 PRC32

Public N=224 loading AVE 0.807 0.72 0.837 0.910 0.850 0.754 0.70 0.898 0.875 0.859 0.762 0.74 0.882 0.890 0.872 0.867 0.71 0.874 0.813 0.826 0.912 0.75 0.950 0.826 0.718 0.916 0.68 0.804 0.843 0.749

CR 0.91

α 0.90

0.89

0.90

0.88

0.87

0.92

0.87

0.88

Loading 0.817 0.861 0.906 0.858 0.815 0.901 0.874 0.859 0.765 0.846 0.883 0.840 0.878 0.869 0.828 0.833 0.992 0.836 0.583 0.953 0.719 0.808 0.879 0.863

Private N=215 AVE CR 0.74 0.92

α 0.91

0.72

0.90

0.69

0.88

0.87

0.71

0.91

0.73

0.90

0.67

0.89

Not: AVE = average variance extracted, CR = composite reliability, α= Cronbach Alpha Discriminant validity is the extent to which a construct is truly distinct from other constructs. Discriminant validity is a measure of internal consistency (Byrne, 2010). This research assessed discriminant validity by using

Sawasn Al‐Husseini, Ibrahim Elbeltagi and Talib Dosa the criteria established by Fornall & Larcker (1981). According to them, the AVE should be greater than the squared correlation between two constructs. In this research, the constructs were empirically distinct and the discriminant validity was statistically confirmed. Table (3) displays the mean and standard deviation. Additionally it shows that the variances extracted by constructs were greater than any squared correlation amongst items. Table 3: Means, standard deviation and discriminant validity analysis Constructs Public N=224 1‐ Idealized 2‐inspirational 3‐ intellectual 4‐ individualized 5‐Product 6‐process 7 ‐TL

Mean 3.439 3.376 3.501 3.520 3.404 3.326 3.460

S.D .911 .884 .868 .924 .800 .892 .841

1 0.72 0.087 0.115 0.240 0.160 0.127

Private N=215 1‐ Idealized 2‐inspirational 3‐ intellectual 4‐ individualized 5‐Product 6‐process 7 ‐ TL

3.410 3.440 3.436 3.310 3.342 3.147 3.412

.9111 .895 .865 .892 .874 .774 .874

0.74 0.140 0.126 0.017 0.211 0.160

0.70 0.015 0.136 0.140 0.121

0.74 0.191 0.235 0.207

0.71 0.243 0.218

0.75 0.323 0.462

0.68 0.413

0.71

0.72 0.123 0.150 0.317 0.192

0.69 0.098 0.141 0.135

0.71 0.167 0.108

0.73 0.319 0.440

0.67 0.373

0.70

Not: S.D = standard deviation The research evaluated the measurement model by using fitness of fit indices as shown in table (4). These consist of; Fit index, which includes, X², X²/df, goodness of fit index (GFI), and root mean square error of approximation (RMSEA). Incremental fit measurement, which is a normed fit index (NFI), and a comparative fit index (CFI). Parsimonious fit measures by normed fit index (PNFI). Table (4) shows all the measures are acceptable. This confirmed that the model fit to the data sample in both sectors was acceptable. Thus, the model used is able to test the research hypotheses. Table 4: Overall fit indices of the CFA of the model Fit index TL X²/df GFI RMSEA NFI CFI PNFI

Public Innovation

1.332 0.936 0.039 0.949 0.987 0.862

1.388 0.971 0.042 0.982 0.995 0.859

TL 1.22 0.940 0.032 0.951 0.991 0.870

Private Innovation 1.995 0.959 0.041 0.971 0.984 0.852

Recommended criteria

Source

≤ 2‐ 5 ≥ 0.90 < 0.05 – 0.08 ≥ 0.90 ≥ 0.90 The higher the better

Hair et al. (2010)

4.2 Structural model This research seeks to examine the impact of the four components of TL on product and process innovation. In table 5 the effect of TL (idealized influenced, inspirational motivation, intellectual stimulation and individualized consideration) on product and process innovation is shown to be significant in both sectors. The structural model was evaluated using SEM, which was applied using maximum likelihood (ML). The goodness of fit indices demonstrated adequate levels of fit for the model in both sectors as shown in table (5). The path coefficients were confirmatory at these levels: (.291), (.298), (.320), (3.50) respectively on product and (.289), (.282), (309), (3.492) respectively on process innovation in the public sector. Therefore, the hypotheses: H1 (a, b, c, d), H2 (a, b, c, and, d) are supported. Path coefficients for the private sector were: (.264), (.317), (.304), (.232) respectively on product and (.253), (.310), (.300) and (.211) on process innovation. Hence, the hypotheses: H1 (a, b, c, and d) and H2 (a, b, c, and d) for the private sector were confirmed.

Sawasn Al‐Husseini, Ibrahim Elbeltagi and Talib Dosa Table 5: Standardized path coefficients Hypothesis

Fit index

H1a H1b H1c H1d H1 H2a H2b H2c H2d H2 Public Private

Hypothesis path

Public Path coefficient 0.291* 0.298* 0.320** 0.353**

Private Path coefficient 0.264** 0.317** 0.304**

Results Public

Private

Idealized → product supported Supported Inspirational → product supported Supported Intellectual → product supported Supported Individualized → Supported Supported 0.232* product TL → product 0.498*** 0.426*** Supported Supported Idealized → process 0.289* Supported Supported 0.253* Inspirational → process 0.822* 0.310** Supported Supported Intellectual → process 0.309* 0.300* Supported Supported Individualized → 0.349** Supported Supported 0.211* process TL → Process 0.582** 0.563*** Supported Supported X²/df =1.262, GFI= 0.904, RMSEA = 0.034, NFI= 0.930, CFI=0.985, PNFI= 0.874 X² / df =1.234. GFI = 0.911, RMSEA= 0.043, NFI= 0.927, CFI = 0.989, PNFI = 0.883

Note: p*<0.05, p**< 0.01, p***< 0.001

5. Discussion of results This research has proposed a model of the four components for transformational leadership that affect innovation in public and private Iraqi higher education institutions. The results of SEM supported the proposed relationships. Firstly, idealized influenced has been found to be positively related to product and process innovation in both sectors. The results of the current research indicated that members of staff recognize that their leaders possess the quality of idealized influenced because they hold their respect, trust, and faith. Members of staff are more innovative through doing and developing courses, research projects, curricula, training programs, and adoption new technology when their leaders trust and create pride among them. As TL in an educational environment is essential to encourage trust, respect members of staff towards improvements and creates a culture of change that facilitates the adoption of innovation (Barnett et al. 2001; Damanpour and Schneider 2006). Inspirational motivation is the second component of TL, which is a focus of this research. Leaders with this style encourage communication processes and organizational learning that enable the organization to be more innovative (Bass and Avolio, 2000). The findings of this research suggested that the members of staff in public and private sectors prefer leaders who have the vision. This style assists their universities to go through any destabilising phases that are part of the change process towards the long‐term goals. Such leaders have the skills needed to make members of staff feel valued and to help them to realize the importance of the work they do. The results of this research support the work of (Morales et al., 2008) who asserted that a leader with vision creates environment of sharing knowledge that promotes product and process innovation. Regarding Intellectual stimulation, which is the third component studied, previous research argued that T leaders that using intellectual stimulation are most likely to enhance generative ideas and exploring thinking (Bass and Riggio, 2006). This research suggested that members of staff in both sectors feel that their leaders intellectually stimulate their creative thinking. Thus, they are encouraged to look at old problems in new ways and are made to feel that their contributions are valued, to freely discuss and try out innovative ideas and approaches such as doing courses, research projects, curricula, doing training programs, and adoption new technology. These results are supporting the idea that TL is a key driver for university success. Last but not least is the Individualized consideration component, by encouraging consideration of each members of staff’s ideas this style leads to an expanded source of knowledge for them to use in solving problems (Bass, 1985). The results indicated that T leaders with individualized consideration raise morale and provide members of staff with apt teaching and coaching that will enable them to come up with innovative ideas in public and private sectors

Sawasn Al‐Husseini, Ibrahim Elbeltagi and Talib Dosa The strong relationship suggests that the practice of TL increases product and process innovation. Thus according to table (5) individualized consideration is the strongest component affecting product and process innovation in public sector. Whilst in the private sector inspirational motivation was the strongest component, which implies that the more the college leaders communicate the higher product and process innovation. This phenomenon might be to the fact that the T leaders and members of staff in public institutions prefer higher and closer relationships between them than private sector. Public institutions today provide stability and benefits for the leaders and their staff, as these incentives increase the levels of commitment of the employees. When employees’ level of commitment are high they are more willing to work effectively for the organization and are less likely to leave (Alam et al. 2009). As Moore (2000) indicated that the competency of the T leaders may be more important for the public sector because the individualized relationships and social purpose of these organizations is more salient. While in private sector, members of staff are more concerned about salaries and stability as a result the leaders focus on shared vision of the future, inspire, and motivate their members of staff to work toward achieving that. This finding might also be due to the orientations of the ministry in the development of the public education system, where it puts competitive criteria for the effective selection of academic leaders such as the development and the performance of the institute in specified time. It is believed that the success of each higher education institution is measured by the success of its leaders. Deans, deputies of dean and heads of department are the first line leaders who directly influence the quality of their institutions (Bowman 2002), therefore the leaders in public institutions tends to build interactive and participative individualized relationships with members of staff and try to satisfy their higher needs. These results are somewhat surprising giving the change in the systems of higher education in Iraqi public universities and suggest there is a scope for improvement in the ministry policies trying to develop product and process innovation. According to the discussion above and the results as shown in table (5) the findings gave support that TL has impact on process innovation more than on product innovation in both sectors. Previous research argued that leaders could be the primary driving forces that introduce new programs (Bodla &Nawaz, 2010). Therefore, T leaders that use idealized influenced, inspirational motivation, intellectual stimulation and individualized consideration stimulate creativity by encouraging members of staff to develop. This involves doing research projects, developing teaching materials and methods, and introducing new programs. In terms of effects of TL on process innovation, the results supported the relationship in both sectors. TL is defined as a process that transforms followers by making them more aware of how task outcomes are important encouraging them to be more interested for the sake of the organization and activating their higher order needs (Rafferty and Griffin 2004). Hence, the adoption of such innovation as new training programs (seminars, sessions, and workshops), new technology, adoption of new equipment, implementing incentives (financial incentives), and reward systems (promotion, recognition, scholarship) would motivate members of staff to become better performers. This result clearly supports the earlier qualitative phase of this research (Al‐husseini and Elbeltagi, 2012) and is consistent with proposals from (Jaskyte 2011) who suggested that the introduction of process innovation may have contributed to building more competent and motivated administrative core that support the aspirations of T leaders. To sum up, these results provide added support for earlier research that has emphasized the important of TL as a dominant practice in public and private sectors which were conducted in a variety of geographical locations like (Sosik et al., 1998; Lee et al., 2006; Chen et al., 2007; Morales et al., 2008; Khan et al., 2009; Michaelis et al., 2010; Morales et al, 2011, Chen et al., 2012) including educational entities.

6. Contribution and implications of the research This research added a theoretical and empirical contribution to the extant leadership and innovation literature in higher education institutions in developing countries, particularly in Iraq. In the line with prior researches as mentioned in the previous section, TL was found to have a significant positive influence on innovation, this results is important because it supports the aspects of TL as more concerned with generative of capabilities than is traditional leadership which focuses more on up to down the development of product and process innovation. From methodological perspective, the research supported and achieved the validity and the reliability of the MLQ and innovation scales, which gave a greater accuracy for the results in Iraqi higher education environment.

Sawasn Al‐Husseini, Ibrahim Elbeltagi and Talib Dosa The results clarify different effects of the TL on members of staff’s innovative performance and provide leaders with ideas for enhancing innovation, through effects analysis the study provide a possible mechanism by which TL styles can enhance innovation. Furthermore, the research provides for the first time information about the effects of TL separately for the public and private universities by identifying the prevalence of certain types of support policies in the two sectors, which extends our knowledge about TL and innovation in public and private institutions. Although past literature described that public universities system as being bureaucratic which means that they would follow the directive and authoritative leadership style (Morshidi, 2006) the results of the current study does provide some evidence that the leaders from the universities in Iraq were moving towards more of TL. And suggests that leaders should engage in TL behaviours to promote product and process innovation by building individualized relationships with members of staff and consider their needs, aspirations, and skills. Leadership skills are TL when they stimulate the staff intellectually by broadening their interests and encouraging them to think about old problems in new ways. In the public sector, individualized consideration was found to be the most significant predictor of product and process innovation; therefore, the leaders should provide support, encouragement, and coaching to members of staff. In contrast, in the private sector, inspirational motivation was the strongest predictors of innovation. Thus, the leaders should utilize the aspects of inspirational motivation. In general, since this research showed that TL is an important determinant of product and process innovation in higher education institutions therefore TL should be as a subject of management training and development.

7. Conclusion, limitation and future research Organizations need TL to develop and enhance innovation. This research examined four components of TL on product and process innovation in public and private Iraqi universities. In the line with previous studies, this research found that both public and private leaders possess TL behaviour in the eyes of subordinates and the four components of TL had a positive influence on product and process innovation. The results indicated that regardless of Western or Eastern context TL, plays an important role in enhancing innovation and suggested that TL is effective in the public and private Iraqi higher education institutions. Within the public sector, the findings showed that individualized consideration increases product and process innovation. When the leaders listen to the ideas of members of staff and are continuously involved in the process of coaching it is likely that new approaches can be explored and this can enhance innovation. Whilst, in the private education sector the inspirational motivation was the strongest component affecting innovation, which indicated that the more the college leaders communicate the higher product and process innovation This research conducted only in higher education sector, while there is also need to explore such relationship in other sectors like manufacturing. The model examined in developing countries for future research can extend the understanding of the model in countries that share similar structures, culture, and context with Iraq. Finally, the research analyzed the impact of TL on product and process innovation. Although the four components of TL explain an acceptable amount of variance product and process innovation. Future research could analyze intermediate constructs such as shared vision and knowledge sharing may be get a good results for developing innovation.

References AL‐husseini, S. and Elbeltagi, I. (2012) The Impact of Leadership style and Knowledge Sharing on Innovation in Iraqi Higher education Institutions, Proceedings of the 4th European Conference on Intellectual Capital ,Helsinki, Finland, Arcada University of Applied Sciences. Alam, S., Abdullah, Z., Ishak, N. and ZAIN, Z. (2009) “ Assessing Knowledge Sharing Behaviour among Employees in SMEs: An Empirical Study”, International Business Research, Vol. 2, No.2, pp. 15 ‐ 122. Albury, D. (2005) “Fostering innovation in public services”, Public money and management Journal, pp.51‐57. Avolio, B. and Bass, B. (2002) Developing potential across a full range of leadership ‐ cases on transactional and transformational leadership, Lawrence Erlbaum Associates, New York. Barnett, K., Mccormick, J. and Conners, R. (2001) “Transformational leadership in schools: Panacea, placebo?”, Journal of Education Administration, Vol. 39, No. 1, pp. 24‐26. Bass, B. (1985) leadership and performance beyond expectations, free press, New York Bass, B., and Avolio, B. (2000) The multifactor leadership questionnaire‐5x short form, Redwood city, CA: Mind Garden. Bass, B. and Riggio, R. (2006) Transformational leadership, 2nd ed., Lawrence Erlbaum associates, Inc. US.

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From Taxonomic to Networked Models of Intellectual Capital and its Development Eckhard Ammann School of Informatics, Reutlingen University, Germany Eckhard.Ammann@Reutlingen‐University.de Abstract: Intellectual capital models normally provide a taxonomic view. The overall view is broken down into domains, mostly into human capital, structural capital and relational capital domains. The well‐known Intellectus model further distinguishes organisational and technological capital inside the structural capital and also business and social capital inside the relational capital. These domains are further refined into elements and variables. For example, the human capital domain in Intellectus contains ‐ besides others ‐ the element ‘capacities’, which again contains the variable ‘leadership’. While this may suffice for a static intellectual capital (IC) statement of an organization, it shows two major drawbacks. Firstly, it is often fragmentary in the sense, that it does not show existing inter‐dependencies and correlations between different branches of this taxonomic view. Secondly, it clearly does not help for the dynamic perspective of intellectual capital development. In this paper, a networked model of intellectual capital and its development is proposed. It extends a taxonomic model like the Intellectus model by links between the different branches of the model. As a result, the links in this networked model then serve as basis for intellectual capital dynamics, because inter‐dependent and correlated assets must be recognised in development activities. This can be modeled utilising a hierarchical modeling approach for IC and knowledge dynamics, reaching from IC domain transformations to detailed knowledge conversions as drivers for IC and knowledge development. Keywords: intellectual capital models, domain interdependencies, hierarchical modeling of IC dynamics

1. Introduction Intellectual capital of a company is defined as all non‐monetary and non‐physical resources that are fully or partly controlled by the organization and that contribute to the value creation of the organization (Roos 2005). Several domains of intellectual capital are distinguished. One often used distinction is between three domains, namely relational structure (as the family of intangible relationships with customers and suppliers), internal structure (including patents, concepts, models, IT systems and processes) and human competence of the employees (see Andriessen 2004, Sveiby 2001, and Roos 2005). The Intellectus Model (IADE_CIC 2005, Bueno et al. 2006) further divides relational structure (into a business and a social domain) and internal structure (into an organizational and a technological domain). The model in (Sánchez‐Canizares et al. 2007) especially focuses on organizational culture as additional domain. In Europe, the Intellectual Capital Statement (InCaS, see European Commission 2008) allows organizations to establish their intangible balance sheet. In Germany, the so‐called ‘Wissensbilanz’ is a structural and procedural model, which enables SMEs to state their intellectual capital assets (see Alwert et al. 2008). All those models provide a taxonomic view. The overall view is broken down into domains, which again are broken down into parts and elements. The well‐known Intellectus model breaks down its five top‐level domains into elements and variables. For example, the human capital domain in Intellectus contains ‐ besides others ‐ the element ‘capacities’, which again contains the variable ‘leadership’. Furthermore, indicators are given; e.g. ‘percentage of people involved in corporate improvement activities’ is an indicator for the ‘leadership’ variable. While this may suffice for a static intellectual capital (IC) view and statement of an organization, it shows two major drawbacks. Firstly, it is often fragmentary in the sense, that it does not show existing inter‐dependencies and correlations between different branches of this taxonomic view. Secondly, it clearly does not help for the dynamic perspective of intellectual capital development. Here, you have to go into the details and to provide a model of the dynamics, i.e. about acquisition, conversion, transfer and development of the IC assets. Again, domains, elements and variables of different branches have to be brought into relation here. That means to enable personal development and knowledge adaptation of individual employees in order to improve business processes or customer relationships – just to name an example. See also (Mertins et al. 2010) for an argument along this line.

Eckhard Ammann In this paper, a networked model of intellectual capital and its development is proposed. It extends a taxonomic model like the Intellectus model by links between the different branches of the model, which indicate their inter‐dependencies and correlations. Because of the taxonomic nature of Intellectus, high‐level links on the domain level are as well applied as lower level links, e.g. on the variable level of the model. Those links can be identified by an investigation into the structure and semantics of the Intellectus model. As a result, the links in this networked model then serve as basis for intellectual capital dynamics, because inter‐dependent and correlated assets must be recognised in development activities. The different levels of the links can be seen as template for a hierarchical IC development approach. Higher‐level links and corresponding domain‐layered development activities may be broken down to lower‐level links with adequate and more concrete development activities. Linked parts of the models are together involved in development activities. Links are not necessarily interpreted as 2‐way links. For example, three elements pairwise connected by a link may form the ingredients for a development activity. This will be the base for a hierarchical modeling approach for the dynamics of IC and knowledge, reaching from IC domain transformations to detailed knowledge conversions as drivers for IC and knowledge development. Another and somehow related approach for hierarchical IC development modeling has been presented in (Ammann, 2010). A major weakness of this approach could be, that via IC transformations and knowledge conversions only a mechanism for IC development was given, but no concrete scenarios. Now, by exploiting the networked model and its links, the relevant assets and elements, which take part in development activities, are clearly identified. I.e. meaningful data (inputs and outputs) for the IC development activities is given. To indicate the validity of the networked modelling approach and its applicability to IC development, examples are provided in the paper. As underlying reference model for knowledge and knowledge dynamics the approach introduced by Ammann (Ammann 2009) is used. The structure of the paper is as follows. After the introduction, section 2 briefly describes the Intellectus Model as base and reference model for our networked modelling approach. The following section 3 introduces the concept of links in taxonomic IC models and identifies the links in the Intellectus model. Section 4 discusses the potential for IC development by using this networked modelling approach. Section 5 explains hierarchical IC transformations as drivers for IC development and how this is supported by the identified links. Example scenarios are given also. A summary concludes the paper.

2. The intellectus model as IC reference model As one example of taxonomic IC models, the focus in this paper is on the Intellectus model. In section 3, inter‐ dependencies and correlations of branches in this model will be lookup up in order to establish new additional links. Note, that instead of the Intellectus model, each taxonomic IC model could serve the purposes of this paper, namely to identify links as basis for IC development activities. The Intellectus model is designed for measurement and management of intangible values, which compose the concept of Intellectual Capital (IADE‐ CIC 2003). It comes with a hierarchical structure to clarify the relationships between the different intangible assets of the organization. See Figure 1 for the overall structure of the model. Five components expand the three well‐known domains (Human, Structural and Relational capital) into Human, Organizational, Technological, Business and Social Capital. Organizational Capital is the combination of intangibles that structure and develop the organizational activity, while Technological Capital refers to intangibles linked to the activities and functions of the technical system of operations of the organization. Both sum up to the Structural Capital domain. The Relational Capital domain is divided into the Business Capital component (referring to the value of relationships with the main agents of the business) and Social Capital (referring to the relationships with social agents in its surroundings). They group intangible assets according their nature. Each component splits into elements, which again integrate variables. Indicators for variables help for the valuation of intangible assets. To give an example, the human capital component contains an element ‘aptitudes’, which again contains four variables (formal education, specialised training, experience, and personal development). Three indicators are given for ‘personal development’; one of them is ‘percentage of people with socio‐cultural activities outside their professional life’.

Eckhard Ammann

Figure 1: The Intellectus model Alternative IC reference models include the 3‐domain model by Sveiby (Sveiby 2001), the approach using a resource distinction tree (see Roos, Pike, Fernström 2005), and the approach in (Sánchez‐Canizares et al. 2007).

3. From taxonomic to networked models 3.1 The method Intellectual capital models mostly show up a taxonomic structure. The Intellectus model as shortly introduced in section 2 is a prominent example. The overall view is broken down into domains, which again are broken down into parts and elements. The Intellectus model specifically breaks down its five top‐level domains into elements and variables. For example, the human capital domain in Intellectus contains ‐ besides others ‐ the element ‘capacities’, which again contains the variable ‘leadership’. Furthermore, indicators are given; e.g. ‘percentage of people involved in corporate improvement activities” is an indicator for the “leadership’ variable. While this may suffice for a static intellectual capital view and statement of an organization, it shows two major drawbacks. Firstly, it is often fragmentary in the sense, that it does not show existing inter‐dependencies and correlations between different branches of this taxonomic view. Secondly, it clearly does not help for the dynamic perspective of intellectual capital development. Here, you have to go into the details and to provide a model of the dynamics, i.e. about acquisition, conversion, transfer and development of the IC assets. Again, domains, elements and variables of different branches have to be brought into relation here. That means to enable personal development and knowledge adaptation of individual employees in order to improve business processes or customer relationships – just to name an example. See also (Mertins et al. 2010) for an argument along this line. In this paper, a networked model of intellectual capital and its development is proposed. It extends a taxonomic model like the Intellectus model by links between the different branches of the model, which indicate their inter‐dependencies and correlations. Links are applied with recognition of the level of the taxonomic reference model. Figure 2 illustrates how links are applied to the Intellectus model. That means, that a link always interconnects two entities on the same layer. For example, a link on the domain layer may interconnect two different domains and a link on the variable level connects two variables. Links are applied at each level of the taxonomic model. Note, that because of the taxonomic nature of the reference model, links on lower layers can only exist, if their aggregating entities on the next‐higher layer are interconnected by a link on this layer. Or, seeing it the other way around, a missing link between two entities on a higher layer excludes the possibility of having links between derived lower‐level entities. These links can be identified by an investigation into the structure and semantics of the Intellectus model.

Eckhard Ammann

Figure 2: The intellectus model with links (symbolized)

3.2 Links on the domain layer On the highest layer of the Intellectus model, eight out of possible ten links can be applied. Figure 3 shows these eight links. Clearly the human capital domain as a whole is interconnected with each of the other domains, because every asset depends on the involvement of the employees. Organisational capital helps to develop technological and business capital, the latter one also interlinked with the technological capital domain. Social capital does not interconnect with business and technological capital.

Figure 3: Links on the domain layer

3.3 Links on the element layer On the element layer, there are 22 elements in the Intellectus model, which possibly could be interconnected by links. According to the taxonomic nature of the Intellectus model, two types of links could be applied. The first one interconnects two elements out of the same domain; for example, the aptitudes element in the human capital domain can be interconnected with the capacities element. The second link type connects two elements out of different domains, e.g. organisational learning in the organisational capital domain again with the capacities element. Note however, that for the second link type only those links are possible, which could be derived from links on the next higher layer of the model. That means, that the latter link is only possible, because human capital and organisational capital are interconnected on the domain layer. Figure 4 symbolically shows, how links on the element layer may look like.

Eckhard Ammann

Figure 4: Links on the element layer (partial)

3.4 Links on the variable layer Because there exist 72 variables in the Intellectus model, a lot of links could in principle exist on this layer. In fact the number could be 2556. But this number of candidates is significantly reduced by the restrictions imposed by missing links on the element layer, which itself have been restricted by missing links on the domain layer. Of course, not every possible remaining link is meaningful in addition. Again, two types of links on this layer can be observed. The first one interconnects two variables of the same element, the second one two variables of different elements. For the second type, refinement of the example in sub‐section 3.3 (linking ‘capacities’ and ‘organisational learning’) leads us to the link between learning (a variable in the capacities element) and learning environments (a variable in the ‘organisational learning’ element).

4. Potential for IC development The networked model for intellectual capital as described before makes inter‐dependencies and correlations of different entities on each layer explicit. For a dynamic view of intellectual capital development, a basis for development activities on the different levels is provided by the links on the appropriate layer in the following way. A link interconnects a pair of entities on the same layer of the intellectual capital model. An intellectual capital development activity on this layer can now be modeled by taking a set of links on this layer and formulating an appropriate intellectual capital transformation on this layer. A transformation in general would be defined by taking a set of entities as source entities and a set of entities as destination entities. The transformation would then transform the source entities into the destination entities. Destination entities could be changed or new entities, also the sets of source and destination entities must not be disjoint. That means for example, that a source entity may be transformed into a expanded/improved form by the transformation. Due to the (still) hierarchical nature of our networked model of intellectual capital, development activities can now be modeled in a hierarchical way: on a strategic level, transformations on the domain layer can be identified. These may then be broken down to more planning and then operational levels by deriving element transformations from this domain transformations and then variable transformations from the element transformations. A final refinement would then be an individualization step, which defines individual entities as source or destination entities instead of aggregated variable entities (e.g. the experience of a single employee instead of the variable ‘experience’). The following section 5 will detail this potential by refining transformations from the domain layer over the element and variable layer until the final individualization step.

Eckhard Ammann

5. A hierarchical modeling approach to intellectual capital development A hierarchical modeling approach is described here, which allows for refinements of IC domain transformations to element and variable transformations and to individualized knowledge conversions. The important point will be, that all source and destinations assets (be it domains, elements or variables) can now be gained from the corresponding links in the network model. The general procedure is normally, to start at the domain layer and refine to the lower layers then. First you have to choose an entity (or several entities) on the domain layer of the model, which is to be developed further in the company. This entity (these entities) would be on the destination side of the domain transformation to be built. If you follow all links from this entity (these entities), then you will be lead to other entities on this layer, which together establish the potential pool for the source side of the domain transformation. A certain (relevant) subset or all of them has to be chosen, which then would establish the source side of the transformation. Then you refine to lower layers, of course by only recognizing entities interconnected by links on this layer. Having completed the variable transformation on the variable layer, a final refinement step will follow, where the variable entities of the identified variable transformation are individualized as indicated in section 4. In the following sub‐sections we proceed on this way and start with an overall goal of improving the business capital, which is modeled as domain transformation. Afterwards this is refined into goals of planning and operative nature; an element and a variable transformation is gained through this refinement. Finally individualization is applied. This path of explanation is followed with the help of an example, which aims at improved customer relationships (i.e. part of the business capital domain) in the form of a new customer enquiry scheme.

5.1 Domain transformations Transformations between intellectual capital domains are the drivers for high‐level intellectual capital development. In order to improve the business capital, this domain is placed at the destination side of a domain transformation, as shown in Figure 5. Then, according to the general procedure explained before, the links connected to the business domain are inspected and a subset (or all of them) is placed at the source side of the transformation. Here, the business domain is connected to the three domains (but not to the social capital domain) as shown in Figure 3 in section 3. The result is a 3‐to‐1 domain transformation as shown in Figure 5.

Figure 5: Transformation on the domain layer

5.2 Element transformations Going down one layer of abstraction, i.e. from a strategic to a planning level), the domain transformation of sub‐section 5.1 is refined. Here interconnected links on the element layer are inspected, which are covered by the 4 domains. This leads to the ‘relationships with customers’ element on the destination side of a new element transformation. On the source side of this transformation, relevant elements for our overall goal are

Eckhard Ammann ‘aptitudes’ and ‘capacities’ in the human capital domain, ‘organisational learning’ and ‘Processes’ in the organisational domain and finally the ‘technological infrastructure’ element in the technological capital domain. Other elements, like ‘culture’ in the organisational domain are not taken, they are not connected to the ‘relationships with customers’ element in the business capital domain, as one can observe from Figure 4 (in principle, Figure 4 does not shown all links on the element layer). An IC element transformation is gained as a model for development activities on a planning level as shown in Figure 6. This transformation is a 5‐to‐1 element transformation.

Figure 6: Transformation on the element layer

5.3 Variable transformations The refinement procedure from domain to element layer as described in the previous sub‐section 5.2 can now be applied accordingly, when the further refinement from element to variable layer has to be performed in order to formulate a transformation on the operative layer. Again the links between variables are taken into account, which are derived from interconnected elements of the element transformation. Figure 7 displays the resulting IC variable transformation, which is an 8‐to‐3 transformation. Especially, on the destination side, three variables derived from the ‘relationships with customers’ element are to be recognized and improved by this transformation.

Figure 7: Transformation on the variable layer

Eckhard Ammann

5.4 Individualization and knowledge dynamics The final step in modeling of the IC development activity for an improvement of business capital is individualization. On the variable layers, there still are aggregated entities like ‘experience’ and ‘specialised training’. In order to really proceed with development activities, those entities must be individualized, i.e. experience and specialized training of individual employees are taken into account or a specific customer relationship process must be improved or even a new additional one be implemented. Also, the customer relationships processes variable on the destination side is individualized to a customer requiry scheme, which augments the existing process for communication with customers. The result is an individualized transformation, which in fact is a general and complex knowledge conversion. A corresponding conception of knowledge and of knowledge dynamics has been introduced in (Ammann 2009). It extends the well‐known SECI‐model by Nonaka and Takeuchi (Nonaka, Takeuchi 1995) by extending the type dimension of knowledge on the one side and by introducing general n‐to‐m knowledge conversions based on five basic conversions.

5.5 Layers of modeling The overall perspective of this hierarchical approach to intellectual capital modeling is shown in Figure 9. Based on the links on the appropriate layer of the Intellectus model, intellectual capital transformations are recognized as drivers for intellectual capital development. Starting from the highest layer, overall development activities are planning on a strategic level. These can be broken down to lower levels by refining these activities to entities on the lower levels. Detailed development activities can be finally modeled by individualizing the IC transformations on the variable layer. E.g. personal requirements to employees in order to perform the development activity (like experience or self‐motivation) are instantiated and personalized to single employees.

Figure 9: Layers of modeling

6. Summary and conclusion A method to augment taxonomic models for the intellectual capital is presented, where additional links are introduced between the domains, elements and variables of the model. These links represent inter‐ dependencies and correlations between the different branches of the taxonomic model. The result is a networked model for IC with augmented information, which otherwise is not recognized in strongly hierarchical models. Having identified these links, they can be used a base for IC development activities. These activities can be established at different levels of granularity, from a strategic domain level to a detailed variable level and up to the specific knowledge conversion and transfer level. The activities can be modelling with an approach including hierarchical IC transformations and knowledge conversions.

Eckhard Ammann In this way, the introduced links in the IC model can be seen as a kind of interface between the static taxonomic IC model and the hierarchical approach to IC dynamics, i.e. the development of IC in a company. In other words, IC transformations could be instantiated (i.e. populated with source and destination assets) with appropriate IC assets, which are interconnected with links in the networked IC model. From an overall perspective, this modeling approach shows as well characteristics of the ostensive research approach to IC as those of the performative approach. An ostensive approach is related to knowledge and value objects and objectives in a more or less fixed model. In the performative approach, IC is seen as part of actively‐undertaken knowledge management and is utilized to develop organizational conditions. See (Mouritsen 2006) for a detailed discussion on these two approaches. In few words, our modeling approach follows the ostensive way by using the original taxonomic IC model and motivates the performative way, when development activities are to be done using IC and knowledge assets interconnected by links in the networked IC model.

References Alwert, K., Bornemann, M., and Will, M. (2008) Wissensbilanz – Made in Germany. Leitfaden 2.0 zur Erstellung einer Wissensbilanz, Guideline Published by the Federal Ministry for Economics and Technology, Berlin. Ammann, E. (2009) “The Knowledge Cube and Knowledge Conversions”, in: Proc. of the World Congress of Engineering, International Conference on Data Mining and Knowledge Engineering (ICDMKE), London, UK, pp. 319‐324. Ammann, E. (2010) “A Hierarchical Modelling Approach to Intellectual Capital Development”, Electronic Journal of Knowledge Management (EJKM), Volume 8, Issue 2, July 2010, pp. 181‐192. rd Ammann, E. (2011) “A Modeling Approach to Intellectual Capital Advancement”, in: Proceedings of the 3 European Conference on Intellectual Capital 2011 (ECIC 2011), Nicosia, Zypern, pp. 32‐40. Andriessen, D. (2004) Making Sense of Intellectual Capital, Elsevier. Bueno, E., Salmador, M., Rodriguez, O., and De Castro, G.M. (2006) “Internal Logic of Intellectual Capital: A Biological Approach”, Journal of Intellectual Capital, Vol.7, No.3, pp. 394‐405. European Commission (2008) InCaS – Intellectual Capital Statement – Made in Europe, European ICS Guideline, [online] www.incas‐europe.org. IADE‐CIC (2003) “Model for the measurement and management of intellectual capital: Intellectus Model”, Documentos Intellectus 5, Universidad Autónoma de Madrid, Madrid. Mertins, K., Will, M., and Meyer, C. (2010) “Analysing and Enhancing IC in Business Networks: Results from a Recent nd Study”, Proc. of the 2 European Conference on Intellectual Capital (ECIC 2010), Lisbon Portugal, pp. 450‐456. Mouritsen, J. (2006) “Problematising intellectual capital research: ostensive versus performative IC”, Accouning, Auditíng & Accountability Journal, Vol.19, No.6, pp. 820‐841. Nonaka, I., and Takeuchi, H. (1995) The Knowledge‐Creating Company – How Japanese Companies Foster Creativity and Innovation for Competitive Advantage , Oxford University Press, London. Roos, G., Pike, St., and Fernström, L. (2005) Managing Intellectual Capital in Practice, Elsevier. Sánchez‐Canizares, S.M., Ayuso Munoz, M.Á., and López‐Guzmán, T. (2007) “Organizational culture and intellectual capital: a new model”, Journal of Intellectual Capital, Vol.8, No.3, pp. 409‐430. Sveiby, K.‐E. (2001) “A Knowledge‐Based Theory of the Firm to guide Strategy Formulation”, Journal of Intellectual Capital, Vol.2, No.4, pp. 344‐358.

Structural Capital, Innovation Capability, and Company Performance in Technology‐Based Colombian Firms Nekane Aramburu1, Josune Sáenz1 and Carlos Blanco2 1 Deusto Business School, University of Deusto, San Sebastián, Spain 2 KM‐INOVA, Bogotá, Colombia nekane.aramburu@deusto.es josune.saenz@deusto.es carlo_co@yahoo.com Abstract: In today’s economy, innovation is considered to be one of the main driving forces behind business competitiveness, if not the most relevant one (Drucker 1988; Shapiro and Varian 1998; Sveiby 1997). The study of innovation has been addressed from different perspectives. Recently, the literature on knowledge management and intellectual capital has provided new insights (Subramaniam and Youndt, 2005). Along these lines, the aim of this paper is to analyze the impact of different organizational conditions – i.e. “structural capital” – on innovation capability and innovation performance, from an “intellectual capital” (IC) perspective. As regards innovation capability, two dimensions are considered: new idea generation and innovation project management. The population subject to study is made up of technology‐based Colombian firms. In order to gather information about the relevant variables involved in the research, a questionnaire was designed and addressed to the CEOs of the companies making up the target population. The sample analysed is made up of 69 companies and is large enough to carry out a statistical study based on structural equation modelling (partial least squares approach) by means of PLS‐Graph software (Chin and Frye, 2003). The results obtained show that structural capital explains to a great extent both the effectiveness of the new idea generation process and of innovation project management. However, the influence of each specific organizational component making up structural capital (organizational design; organizational culture; hiring and professional development policies; innovation strategy; technological capital; and external structure) varies. Moreover, successful innovation project management is the only innovation capability dimension that exerts a significant impact on company performance. From a practical perspective, the findings of this research could guide managerial decisions towards a better management of intangibles in order to increase the innovation capability of firms. Keywords: intellectual capital; structural capital; new idea generation; innovation project management; company performance; Colombian firms

1. Introduction and research purpose Today’s economy is driven by what we could call the “innovation imperative”. As Bessant & Tidd (2007) point out, the logic is very simple: if companies do not change what they offer to the world (products and services) and how they create and deliver them, they risk being overtaken by others who do. Therefore, understanding the sources of successful innovation has become one of the main challenges for academic researchers in the business world. Since the seminal works by Nonaka in 1991, and Nonaka and Takeuchi in 1995, a close link has been established between innovation and knowledge creation. In other words, it is assumed that innovation involves the generation of new knowledge. As Subramaniam and Youndt point out, “It is widely accepted that an organization’s capability to innovate is closely tied to its intellectual capital, or its ability to utilize its knowledge resources” (Subramaniam and Youndt, 2005:450). Moreover, innovation lies at the core of what is known as “dynamic capabilities”. According to Teece (2007, 2009), the “dynamic capability” concept encompasses three first‐level (i.e. more simple) capacities. The first one is the capacity to sense and shape opportunities and threats. This is the ideation dimension of the innovation capability (i.e. the generation of new ideas). The second one is the capacity to seize opportunities. This refers to the selection of the new ideas to be addressed and to their subsequent development and fulfilment (i.e. innovation project management). The last one refers to the company’s capacity to reinvent/transform itself and not die because of unfavourable path dependencies generated by past success (i.e. change capacity).

Nekane Aramburu, Josune Sáenz, and Carlos Blanco With this in mind, the aim of this paper is to analyze the impact of different organizational enablers – i.e. “structural capital” – on the first two innovation capacities mentioned above (new idea generation and innovation project management), from an “intellectual capital” (IC) perspective.

2. Conceptual framework Given that (as previously explained) innovation consists of an ongoing pursuit of harnessing new and unique knowledge (Subramaniam and Youndt, 2005), the study of innovation‐permitting conditions involves the study of those conditions that foster the creation of new knowledge. According to Nonaka, von Krogh, and Voelpel (2006), knowledge creation involves a continuous process through which one overcomes the individual boundaries and constraints imposed by information and past learning by acquiring a new context, a new view of the world, and new knowledge. By interacting and sharing tacit and explicit knowledge with others, the individual enhances the capacity to define a situation or problem, and apply his or her knowledge so as to act and specifically solve the problem. In the case of organizational knowledge creation, this means making available and amplifying the knowledge created by individuals as well as crystallizing and connecting it with the organization’s knowledge system (Nonaka and Takeuchi 1995; Nonaka, von Krogh, and Voelpel 2006). Therefore, an organizational context which favours knowledge creation and subsequent innovation is a context which favours the exchange of ideas and experiences among people, and which encourages experimentation and continuous questioning of established patterns. In the case of this paper, this organizational context is going to be analyzed through the lens of “intellectual capital”. This concept of IC has been the object of multiple definitions, but most of them could be grouped into two categories. The first one equates the concept of IC with that of “knowledge capital”. Along these lines, IC is considered to be the sum of all knowledge firms utilize for competitive advantage. This is the point of view of authors such as Nahapiet and Ghoshal (1998), Stewart (1997), and Youndt, Subramaniam, and Snell (2004). Other authors, however, view things from a broader perspective and consider IC to encompass other intangible resources and activities as well. As an example, the European Commission (2006) states that “Intellectual capital is the combination of the human, organizational, and relational resources and activities of an organization. It includes the knowledge, skills, experiences, and abilities of the employees; the R&D activities, the organizational routines, procedures, systems, databases, and intellectual property rights of the company; and all resources linked to the external relationships of the firm with customers, suppliers, R&D partners, etc.”. Authors such as Bontis (1999), and Marr (2006) are closer to this second perspective. Whichever the perspective adopted (limited to knowledge or holistic), IC tends to be split up into different categories. Although the specific labels employed may vary, an initial distinction is generally made between human and structural capital (i.e. between thinking and non‐thinking resources; Andriessen, 2004). A second distinction is then drawn within the latter between organizational and social capital – in the case of the knowledge perspective – and between internal and external structure – in the case of the holistic one (see Figure 1). Differences between the knowledge and holistic perspective arise when it comes to conceptualizing structural capital and its two sub‐components. In the case of the “knowledge perspective”, the type of knowledge considered lies at the basis of the distinction made between organizational and social capital. The former refers to the institutionalized knowledge and codified experience (i.e. “explicit knowledge”) residing within and utilized via databases, patents, manuals, structures, systems, and processes (Youndt, Subramaniam, and Snell, 2004), whereas social capital is the knowledge embedded within, available through, and utilized by interactions among individuals and their networks of interrelationships (Nahapiet and Ghoshal, 1998). Of course, this second definition refers to “tacit knowledge” and it is important to note that the networks and interrelationships mentioned could be both internal and external to the firm. In the case of the “holistic” perspective of IC, the “location” of the intangible resources and activities lies at the basis of the distinction made between internal and external structure. Along these lines, internal structure refers to the knowledge and other intangible resources that stay within the company when the employees have left and that derive from the organization’s action processes (CIC, 2003). In other words, it encompasses the organization’s essential operating processes, the way it is structured, its information flows and databases,

Nekane Aramburu, Josune Sáenz, and Carlos Blanco its leadership and management style, its culture and incentive schemes, as well as intellectual property rights (Marr, 2006). External structure refers to all resources linked to the external relationships of the firm with customers, suppliers, or R&D partners (Meritum Project, 2002). Those resources could be related to knowledge, but they could also refer to other intangible assets, such as brand image, customer satisfaction, customer loyalty, negotiating power, etc.

Figure 1: Intellectual capital categories For the purposes of this research, the holistic perspective of IC will be adopted, as it is assumed that organizational conditions that foster innovation capability go beyond previously accumulated knowledge in different forms (i.e. databases, manuals, procedures, etc.) and encompass other intangible factors too. Hence, structural capital will be conceptualized as referring to what is left in the company when the employees have gone home (Edvinsson and Richtner, 1999; European Commission, 2006), excluding tangible resources, of course. On the other hand, starting off from Sveiby’s classic distinction between internal and external structure (Sveiby, 1997), an additional division will be proposed for internal structure (CIC, 2003) between organizational capital and technological capital. Organizational capital (notice the difference in meaning with the knowledge perspective) refers to the set of intangibles of both an explicit and implicit, formal and informal nature, which enable company activity to be structured and developed effectively and efficiently. It encompasses elements such as organizational design, organizational culture, organizational policies and guidelines, and strategy. In contrast, technological capital (i.e. technological endowment) refers to the set of intangibles directly linked to the development of activities and functions which make up the company’s technical system of operations. Within the context of this research, Information and Communication Technologies (ICT) infrastructure will be the specific element considered in this domain. There are few studies (all of them very recent) that adopt an IC perspective when analyzing the different factors that may foster or, on the contrary, hinder the innovation capability of firms. The most comprehensive of them is the one published by Subramaniam and Youndt in 2005. In this study, Subramaniam and Youndt adopt a knowledge IC perspective and analyze the impact of human, organizational, and social capital on the incremental and radical innovation capabilities of firms. In fact, this knowledge‐focused perspective seems to be the prevalent one in empirical research. This is the case of the studies published by Brookes et alter and Menor et alter in 2007 which are aimed at explaining product innovation performance by means of social capital and knowledge‐based resources, respectively. However, Wu et alter (2007) represent an exception to this trend, as they adopt a holistic IC perspective in order to explain technological innovation performance.

Nekane Aramburu, Josune Sáenz, and Carlos Blanco

Figure 2: Structural capital architecture (holistic perspective) This paper is aimed at analyzing the organizational conditions that foster innovation capability. For this to be done, the structural capital architecture previously explained offers a comprehensive framework of analysis.

3. Research hypotheses According to the conceptual framework previously outlined, research hypotheses have been developed as follows:

3.1 Organizational capital‐related hypotheses The first element making up organizational capital is organizational design. This refers to the type of organizational structure in place within the company, to the specific organizational units and work teams that make up this structure, to the communication channels (both vertical and horizontal) that link the aforementioned units and teams, and to the physical design of the workplace. As regards organizational structure, Nonaka and Takeuchi (1995), and Nonaka, Toyama, and Byosière (2003) advocate the fact that certain types of structure facilitate knowledge sharing and knowledge‐creation processes more than others (i.e. they are more “learning‐supportive”). In particular, they defend the hypertext type of organization (a combination of hierarchy and adhocracy) as the most suitable one in order to foster knowledge sharing and creation. On the other hand, these processes could be further supported via the existence of a specific organizational unit or group of qualified people specifically devoted to facilitating the generation and implementation of new ideas. The existence of such a unit gives formal impulse to the generation of a specific “ba” for innovation (that is, a physical or virtual space where knowledge sharing and knowledge creation takes place – Nonaka, Reinmoeller, and Senoo, 1998). Likewise, communication channels could play a substantial role in fostering knowledge sharing and subsequent knowledge creation. As Kalla (2005) points out, knowledge sharing is a function of integrated internal communications. Although in the past knowledge flows used to be mainly vertical, from supervisor to supervisee, organizations today also need to foster the flow of knowledge horizontally (Dalkir, 2005). Hence, it is assumed that vertical and horizontal communication channels act as catalysts for knowledge sharing. Finally, physical design of the workplace is the last element making up organizational design that could promote or, on the contrary, hinder knowledge‐sharing processes. According to Nonaka, Schamer, and Toyama (2001:233), “The single most important factor shaping the quality of knowledge is the quality of place”. This idea is related once more to the concept of “ba”. Therefore, buildings and the space they embrace play a vital role in the intangible area of knowledge management (Nenonen, 2004).

Nekane Aramburu, Josune Sáenz, and Carlos Blanco In accordance with the prominent role that, from a theoretical point of view, organizational design could play in the generation of new knowledge and, hence, on innovation capability, the following hypotheses have been formulated: H1a: Having an organizational design which favours knowledge sharing positively affects the generation of new ideas. H1b: Having an organizational design which favours knowledge sharing positively affects innovation project management. Organizational culture is the second element making up organizational capital. As said earlier, an innovation‐ supportive culture should encourage knowledge sharing, as well as experimentation and continuous questioning of established patterns. Authors such as Allee (2003), Friedman, Lipshitz, and Overmeer (2003), and Wiig (2004) describe the values that shape such a culture: trust, transparency, open mentality, mistakes considered as learning opportunities, support for experimentation and exploration of new territories, and cooperation and mutual help. All this gives rise to the following hypotheses: H2a: Having an organizational culture which promotes experimentation and knowledge sharing positively affects the new idea generation process. H2b: Having an organizational culture which promotes experimentation and knowledge sharing positively affects innovation project management. Organizational policies and guidelines, and more precisely, hiring and professional development policies, could play a significant role in fostering innovation capability. Innovation is a human activity and, therefore, purposefully enhancing people competences related to this domain (such as teamwork, creativity, entrepreneurship, leadership, learning, and networking capabilities) could be crucial in order to facilitate successful innovation. Thus, the following hypotheses have been formulated: H3a: Hiring and professional development policies which try to foster knowledge sharing and innovation‐ related competences positively affect the generation of new ideas. H3b: Hiring and professional development policies which try to foster knowledge sharing and innovation‐ related competences positively affect innovation project management. Finally, strategy, and more specifically, innovation strategy, is the last component that has been considered within organizational capital for the purposes of this research. This refers to the guideline principles that indicate to an organization’s members in which area knowledge creation or innovation should be pursued (Ichijo, 2007). Having a clearly established and shared innovation strategy should increase the effectiveness of the new idea generation process and the innovation project management. This gives rise to the following hypotheses: H4a: Having an explicit and organization‐wide shared innovation strategy positively affects the new idea generation process. H4b: Having an explicit and organization‐wide shared innovation strategy positively affects innovation project management.

3.2 Technological capital related hypotheses Information and communication technologies can also contribute to a great extent to knowledge sharing and innovation. In particular, the existence of specific technological tools that foster the capture and storing of knowledge, as well as the connection between individuals and groups may be very helpful (Dalkir 2005). Therefore, the following hypotheses have been formulated: H5a: Having ICT systems which facilitate knowledge sharing and permanent connection with different agents positively affects the new idea generation process. H5b: Having ICT systems which facilitate knowledge sharing and permanent connection with different agents positively affects innovation project management.

Nekane Aramburu, Josune Sáenz, and Carlos Blanco

3.3 External structure‐related hypotheses As is supported by Nonaka and Takeuchi (1995), the mobilization of external knowledge held by outside stakeholders is an essential aspect in order to promote knowledge creation. In other words, the exchange of knowledge with external agents is a key element for creating new knowledge. This idea is also supported by other authors who state that “the scope and breadth of knowledge available from outside sources is generally much greater than that available from inside sources” (Maznevski and Athanassiou, 2007:69). In accordance with this, the following hypotheses have been formulated: H6a: The extent to which the company has an external innovation network positively affects the generation of new ideas. H6b: The extent to which the company has an external innovation network positively affects innovation project management.

3.4 Company performance related hypotheses Finally, the last set of hypotheses is related to the relationship between innovation capability dimensions and company performance. Although previous studies have already demonstrated the relevance of innovation as a source of superior growth and performance, they have usually done so by calculating the statistical correlation between R&D investment and some specific measure of business growth or profitability. The aim here is to estimate the specific contribution of each innovation capability dimension to firm performance. This will provide companies with a useful insight in order to assess what to focus on in order to improve their innovation results. Thus, the following hypotheses have been formulated. H7a: The effectiveness of the new idea generation process positively affects company perfornance. H7b: Effective innovation project management positively affects company performance. Research hypotheses are summarized in Figure 3.

Figure 3: Research hypotheses

Nekane Aramburu, Josune Sáenz, and Carlos Blanco

4. Research method The population subject to study is made up of technology‐based Colombian firms. A questionnaire (see the Appendix) was administered to the CEOs of the companies making up the target population of the research. The sample obtained encompasses 69 companies and is large enough to carry out a statistical study based on structural equation modelling (partial least squares approach). The software used for the statistical analysis is PLS‐Graph (Chin and Frye, 2003). A PLS model is analyzed and interpreted in two stages: firstly, an assessment of the reliability and validity of the measurement model is made, and secondly, an assessment of the structural model is carried out. This sequence ensures that the measures making up the constructs are valid and reliable before attempting to draw conclusions regarding relationships among constructs (Barclay et alter 1995).

5. Research findings Following the sequence of analysis previously described, the main findings of the multivariate analysis carried out are as follows: As far as the measurement model evaluation is concerned, this differs depending on the nature of the construct being analysed (reflective or formative). In the case of constructs made up of reflective indicators, individual item reliability, construct reliability, convergent validity, and discriminant validity should be checked. In the case of formative constructs, however, multicolinearity problems should be explored. In both cases, all the tests carried out have shown satisfactory results. Once the quality of the measurement model has been guaranteed, the quality of the structural model should then be assessed. This refers to the strength of the research hypotheses and to the amount of variance 2 explained (R ). In order to assess the research hypotheses, path coefficient levels should be examined, as well as their degree of significance, by means of bootstrapping techniques. Table 1 and Table 2I summarize the results obtained. In these tables, we can also see the contribution of each exogenous construct to the amount of variance explained. Table 1: Structural model evaluation – Influence of structural capital on innovation capability

Path Correlation Contr. to R2 Path Correlation 2 Contr. to R

New idea generation Innovation project management

Organi‐ zational design 0.169 0.599 10.12% 0.103 0.559 5.76%

Organi‐ zational culture 0.122 0.587 7.16% ‐0.207* 0.381 ‐7.89%

HPDP 0.207* 0.607 12.56% 0.230* 0.543 12.49%

Inno‐ vation strategy 0.337*** 0.706 23.79% 0.476*** 0.698 33.22%

Techno‐ logical capital 0.061 0.453 2.76% 0.222** 0.548 12.17%

External structure 0.187* 0.513 9.59% 0.184* 0.477 8.78%

Total R2 66.00% 64.53%

Notes HPDP: Hiring and professional development policies ***p<0.001, **p<0.01, *p<0.05 (based on t499, one‐tailed test) Table 2: Structural model evaluation – Influence of innovation capability dimensions on company performance

Company performance

Path Correlation Contr. to R2

New idea generation 0.257 0.566 14.55%

Innovation project management 0.483*** 0.647 31.25%

Total R2 45.80%

Notes***p<0.001, **p<0.01, *p<0.05 (based on t499, one‐tailed test) The results obtained show that, in the companies studied, structural capital explains to a great extent both the effectiveness of the new idea generation process (amount of variance explained: 66.00%) and of innovation

Nekane Aramburu, Josune Sáenz, and Carlos Blanco project management (64.53%). In particular, having an explicit and organization‐wide shared innovation strategy proves to be the most relevant factor contributing to each innovation capability dimension (23.79% and 33.22%, respectively), followed by hiring and professional development policies, and external structure. Therefore, hypotheses H3a, H3b, H4a, H4b, H6a, and H6b are clearly supported. As far as technological capital is concerned, this only exerts a significant impact on innovation project management. Thus, hypothesis H5b is supported, but hypothesis H5a is not. However, organizational design does not exert a relevant influence in any capability dimension. Hence, hypothesis H1a and H1b are not supported. On the other hand, the role of organizational culture is especially noteworthy: whereas in the case of the new idea generation process having a culture that promotes experimentation and knowledge sharing does not exert a significant influence (i.e. hypothesis H2a is not supported), in the case of innovation project management it does exert a relevant but negative one. Why can this be so? Why such a culture exerts a counterproductive effect?. Maybe this is due to the fact that, in the case of Colombian firms, such an organizational culture grows at the expense of efficiency to a point which is really detrimental and, as a result, innovation project management suffers. Finally, it should be noticed that successful innovation project management is the only innovation capability dimension that exerts a significant impact on company performance. Perhaps this is due to the greater difficulties that Colombian firms may encounter in the implementation of their innovative ideas.

6. Conclusions and practical implications To bring this paper to a close, it can be concluded that intellectual capital is a key issue in order to promote innovation capability. In particular, structural capital proves to be extremely relevant when it comes to reinforcing the innovation capability of technology‐based Colombian firms. More precisely, innovation strategy, hiring and professional development policies, and external structure are the most relevant enablers both of ideation and of innovation project management. Conversely, ICT infrastructure is only relevant for the management of innovation projects. Thus, it seems that technology‐based Colombian firms do not take advantage of the possibilites offered by ICT systems as a source of new ideas that could enhance their innovation capability. Perhaps, investments in this domain are more focused on facilitating internal project management than on facilitating access to external sources of knowledge, and cooperation and interaction with external agents. More surprisingly, having an organizational culture that fosters experimentation and knowledge sharing has a negative effect on innovation project management, and is not relevant enough for the promotion of new ideas. These results contradict theoretical assumptions about this issue. Perhaps, specific features of Colombian culture could explain this paradox. Hofstede (1983) analysed different cultural dimensions of 50 countries around the world. In the case of Colombia, it is noteworthy that uncertainty avoidance scored very high. This means that propensity towards risk assumption is very low. The feeling of uncertainty generates anxiety (a negative emotion) and, thus, people try to avoid attitudes and behaviour that could increase this anxiety. This specific feature of Colombian culture could explain why a very open organizational culture that fosters experimentation may not promote creativity and effective project management. The creation of a context that enables experimentation implies an important degree of uncertainty which, in the case of Colombian firms, could contribute to increase people’s anxiety. In such a state, people may not be able to develop new ideas or to perform innovation projects effectively. Another Colombian cultural feature highlighted in Hofstede’s study is the low degree of individualism. In Hofstede’s view, individualism shows the relative importance awarded to job aspects such as personal time, freedom, and challenge and the relative unimportance awarded to training, use of skills, physical conditions, and benefits. This feature stresses goals in which the individual is an active agent versus those in which he or she is dependent on the organization (being trained, skills being used, working conditions, and benefits being provided). A low individualistic culture means that people are not active agents within the organization, but

Nekane Aramburu, Josune Sáenz, and Carlos Blanco that they are very dependent on it. Perhaps, for this reason, a culture that fosters experimentation (and, therefore, the creation of a free organizational context where people have the opportunity to develop individual initiative) is counterproductive. Once more, this freedom can generate anxiety and hinder the development of new ideas as well as good project performance. When people are too dependent on the organization, they need very well established rules about what to do and how to do it. As far as the relationship between first‐level innovation capacities and company performance is concerned, only the second first‐level innovation capacity (i.e. innovation project management) has a significant and positive influence on firm performance. Hence, it can be concluded that although generating new ideas is a necessary pre‐condition to innovate, effective execution of innovation projects is the key for transforming innovation into profits. In other words, the fact of having good ideas is not enough to obtain economic outcomes. If ideas are not implemented succesfully it is not possible to improve company performance. From a practical perspective, this research highlights the key aspects that managers of technology‐based Colombian firms should emphasize to increase the innovation capability of their firms and to transform this capability into performance: setting out an explicit and organization‐wide shared innovation strategy; promoting hiring and professional development policies that try to enhance knowledge sharing and innovation‐related competences (in particular, recruiting professionals with innovation expertise and specific training in research – i.e people that hold PhD degrees, something that is not very frequent in Colombia); and developing external innovation networks (i.e. cooperating with external agents). To conclude, it has to be recognized that the research carried out has some limitations that should be borne in mind. The main limitation is that the study is a cross‐sectional one. Innovation processes are complex and the outcomes of innovation activities are not always immediate. There is often a gap between the specific point in time when an innovation project is finished and the moment when the results of this project impact on business performance. For this reason, and in order to carry out a more accurate analysis of the influence of innovation capability on business performance, a longitudinal study would be necessary, allowing the analysis of innovation processes along a period of time and their effects on company’s performance.

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A Structural Model for Organizational Justice in Universities Based on Intellectual Capital Azizi Balvand, Fattah Nazem, Alireza Chenar and OmalbaninSadeghi Department of Education, Roudehen Branch, Islamic Azad University, Roudehen, Iran z.azizi2011@gmail.com nazem@riau.ac.ir A.chenari@yahoo.com osadeghi82@yahoo.com Abstract: The purpose of the present study was to provide a structural model for organizational justice in universities based on intellectual capital. The population of the research included all employees of Islamic Azad University (administrative region 11) in Iran. 203 employees were selected using stratified and cluster random sampling. The research instruments were two questionnaires which were administered in 13 branches and education centers: Beugre’s (1998) organizational justice questionnaire which consisted of 20 items with three underlying constructs of procedural justice, interactional justice, and distributive justice and Cronbach Alpha of 0.93 and Bontis’s (1997) intellectual capital questionnaire which consisted of 52 items with three underlying constructs of human capital, customer capital, and structural capital and Cronbach Alpha of 0.89. The results of path analysis using LISREL software indicated that dimensions of intellectual capital had a direct effect on organizational justice with the indices of 0.73. The model also showed that the factor of customer capital in intellectual capital had the highest direct effect on the factor of procedural justice in organizational justice. It was also concluded that the proposed model showed full fit. Keywords: organizational justice, intellectual capital, structural model, universities

1. Introduction and purpose of the study Organizational justice helps define the rights and obligations of people related to each other and to the social institutions in a society of which they are a part (Stevens & Wood, 1995). Social justice is one of the most fundamental topics within the ambit of justice and is generally concerned with the belief that society should be based on giving individuals and groups' fair treatment and a just share of the benefits of the society without discrimination by class, gender, ethnicity or culture (Fua, 2007). Organizational justice theory was proposed and put forward from applied research in organizational settings, and focuses on how individuals socially construct incidents of justice and injustice. Justice in organizational justice research is examined through the perceptions of employees who make judgments about the actions of organizational leaders in organizations (Cropanzano & Greenberg, 1997; Folger & Cropanzano, 2001; Folger & Martin, 1986; Folger, Rosenfield, & Robinson, 1983; Greenberg, 1990). A leadership act is just in the eyes of employees when they perceive it as just and respond accordingly. Organizational justice, in this sense is subjective which is to say that what might be perceived as just by one person may be perceived as unjust by another. However, justice is also socially constructed; therefore coherent, long‐standing groups (such as employee groups) often develop shared conceptions of what constitutes justice (Bies, 1987; Lind & Tyler, 1988; Tyler & Lind, 1992; Tyler & Smith, 1999). Organizational justice assumes crucial importance due to the linkage to variables such as organizational commitment, organizational citizenship, job satisfaction, and performance (Colquitt, 2001; Colquitt et al., 2002; Tatum et al., 2002). Organizational justice concept was first introduced by Greenberg (1990), to describe the role of fairness as it directly relates to the workplace. Specifically, organizational justice is concerned with the ways in which employees determine if they have been fairly treated in their jobs (Moorman, 1991). According to Greenberg (1990), a perception of organizational justice was operationalized as a three‐ dimensional construct:

Distributive justice: According to Greenberg (1990), distributive justice involves employee assessments of fairness of rewards and inducements received in exchange for contributions at work.

Procedural justice: Procedural justice involves employee assessments of the extent to which decisions are based on fair methods and guidelines. In other words, employees evaluate the extent to which they feel processes used to make decisions that influence them are just (Ang & et al., 2003: 563).

Interactional justice: Interactional justice suggests that perceptions of procedural justice can originate from an organization’s procedures and how those procedures are implemented (Wat & Shaffer, 2005: 409).

Azizi Balvand et al. Yılmaz and Tasdan(2009) published an article entitled organizational citizenship and organizational justice in Turkish primary schools. They concluded that the teachers had positive perceptions regarding organizational citizenship and organizational justice. Their organizational citizenship perceptions did not vary according to gender, field of study and seniority, whereas their organizational justice perceptions varied according to seniority. A similar study was carried out in terms of the relationship between the leadership behaviors, organizational justice and organizational trust, by Yilmaz and Altinkurta(2012), their findings demonstrated that teachers have positive perceptions about organizational trust, organizational justice and school administrators’ leadership behaviors. Moreover, there is a high correlation between school administrators’ supportive leadership behaviors and teachers’ perceptions of organizational justice and perceptions of trust to administrator. What’s more, there is a moderate positive correlation between school administrators’ supportive leadership behaviors and teachers’ perceptions of organizational justice and trust in colleagues and stakeholders. Another study entitled the effect of organizational justice and organizational environment on turn‐over intention of health workers in Ekiti State of Nigeria by Owolabi (2012) reported that organizational justice has a significant effect on turnover intention while organizational environment has no significant effect on turnover intention. Ahmadi and Habibitabar (2011) researched the associativity degree between organizational justice and the organizational health in public organizations of Iran and concluded that there is meaningful relationship between organizational justice and its dimensions with organizational health. Moreover, organizational justice affects the development of organizational health. In another study by Nazim et al.( 2010) the impact of organizational justice on organizational citizenship behavior of bankers of NWFP in Pakistan was researched, this study provides statistical support for significant positive relationship between distributive justice and organizational citizenship behavior and procedural justice and organizational citizenship behavior. Moreover, organizational justice was also found to have a significant positive correlation with overall organizational citizenship behavior. A research on the role of justice and organizational culture in employees' search for work‐life equilibrium by Zagorski(2004) found that work‐life policies are only effective in a compatible context, which includes supportive attitudes and behaviors in organizational, supervisory, and peer levels. The three facets of supportive culture were also significantly linked to the perceptions of justice, with peer attitudes correlated with both distributive, procedural fairness, and support at the company and supervisory level associated with interactional justice. . Howard (1993), conducted a survey to assess the effects of organizational culture on distributive justice, his findings indicated that when organization professed reward allocation strategies matched employees perceptions of reward strategies actually used employees perceived higher levels of fairness, which suggests the importance of formally communicating criteria for differentiating rewards to employees and cultural direction or value orientation that influence employee attitudes more than either cultural strength or person‐ culture fit. Dustin (2010) in his study on the perceptions of organizational justice, job satisfaction, and organizational commitment in intercollegiate athletics stated that sport type interacts in the relation between organizational justice and both overall job satisfaction and organizational commitment. According to Mccardle (2007) , in his study on the role of organizational structure, powerlessness, and information salience, organizational justice, perceived powerlessness, and centralization exert direct effects on workplace deviance. Kaneshiro (2008) analyzed the organizational justice, trust, and commitment relationship in a public organization, his findings supported the significant relationship between organizational justice and trust. On another study by Robinson (2004), support was found for the relationship between employee perceptions of organization justice and organizational outcome variables. In Weng (1998), article statistical support was found for the relationships among organizational trust, organizational politics, and organizational justice, and their effects on merit pay outcomes in the Malaysian public sector introducing are job security, fringe benefits, and pension as the main factors that attract employees to join the public sector. Shelton (2010) reported positive relationships between openness, creativity, organizational justice and autonomy, while those found between organizational justice and creativity, organizational misbehavior and autonomy were negative. Results also indicated that autonomy and organizational justice moderated the relationships between creativity and organizational misbehavior. Casas (2007), researched human resources, professionals’ justice perceptions and organizational justice, his study supported the association between participant level of involvement in implementation of justice‐related policies and procedures and procedural justice perceptions. Additional associations were also found to exist between participant level of involvement in the devising of justice related policies and procedures and procedural, distributive and informational justice perceptions. Ford (2012) ,in his research on the role of self efficacy, distributive justice, and procedural justice on large scale organizational change initiatives found that there exists positive correlations between self‐ efficacy, distributive justice, and procedural justice with organizational change practices. Furthermore, the results indicated individual perceptions may have a strong effect on the implementation of organizational

Azizi Balvand et al. change initiatives. The study contributes to positive social change by guiding organizations to more agilely implement organizational change and operate successfully in a global environment were change is the norm. Today’s business model in a globally competitive environment is increasingly dependent on the use of intangible resources that offer value for organizations and is becoming more important than the value of tangible assets. The intangible resources that represent the concept of intellectual capital are the source of competitive capabilities for the organization (Cohen & Kaimenakis, 2007). Over the past years, important developments in intellectual capital have been achieved that establishes intellectual capital as a recognized area of research and practice. Yet the research on intellectual capital is still in its early stages, and further efforts are needed to improve the understanding of this field’s contribution to the management and strategy of the knowledge economy (Stahle &Bounfour, 2008). Knowledge is a part of intellectual capital because it is closely related to human capital, which brings skills, talent, and knowledge. Together with information capital, which provides support to knowledge management, organization capital, knowledge sharing and usage, knowledge capital becomes part of the resources that generate value and revenue for the organization (Curado, 2008). Intellectual capital is the sum of all knowledge that is possessed by all individuals in an organization and provides the organization with a competitive advantage when used correctly (Arenas & Lavanderos, 2008). To measure intellectual capital usage, the Value Added Intellectual Coefficient (VAIC) method was used. The VAIC method could identify both the size and efficiency of intellectual capital by analyzing data from the organization’s financial reports (Pulic, 1997). The theoretical perspective utilized in this study interlaces theories of technology and organization with theories and models for strategic management of intellectual capital and with the theory of academic capitalism (Slaughter & Rhoades, 2004). Zhou and Fink (2003) maintained that intellectual capital management and knowledge management serve different purposes. Intellectual capital management is more related to top and strategic levels of management while knowledge management concentrates on tactical and operational implementations of knowledge. Edvinsson and Sullivan (1996) defined intellectual capital as knowledge that can be converted into value. Generally, researchers in the field of intellectual capital have divided the concept of intellectual capital into three main constructs (Bontis, Chua, & Richardson, 2000; Bontis ,1996; 1998; 1999; Roos et al. ,1998; Stewart ,1991; 1997; Sveiby ,1997. Edvinsson and Malone (1997), Saint‐Onge (1996) as well as Edvinsson and Sullivan (1996) among others, have defined intellectual capital as comprised of human capital, structural capital, and relational capital. The concepts of intellectual capital seem to have been classified into three different groups (Edvinsson and Sullivan, 1996; Ross and Ross, 1997; and Steward, 1995):

Human capital: Human capital simply comprises the competence, skills, experience, and intellectual abilities of the individual employees (Bounfour, 2003; Brooking, 1996; Edvinnson and Malone, 1997; and Ross et al., 1997).

Structural capital: Structural capital includes processes, systems, structures, brands, intellectual property, and other intangibles that are owned by the firm but do not appear on its balance sheet (Bounfour, 2003; Brooking, 1996; Edvinsson and Malone, 1997; and Ross et al, 1997).

Customer (social) capital: Customer capital is an intermediary form of intellectual capital consisting of knowledge in groups and networks of knowledge resources embedded within and derived from a network of relationships (Edvinsson and Malone, 1997; and Ross et al., 1997).

Intellectual capital has been described as intellectual material that has been formalized, captured and leveraged to produce a higher valued asset (Klein & Prusak, 1994). Intellectual capital is about how to let the knowledge of an organization work for it and have it create value (Roberts, 1999) and includes all intangible resources as well as their interconnections (Bontis et al., 1999). An interesting conceptualization sees intellectual capital as the combination of intangible resources and activities that allow an organization to transform a bundle of material, financial and human resources in a system capable of creating stakeholder value (European Commission, 2006) and organizational innovation (Lerro et al., 2009). In particular, intellectual capital can be thought as the economic value of two categories of intangible assets of a company, i.e. organizational capital and human capital (OECD, 1999). Intellectual capital includes thus a set of intangible elements (resources, capabilities and competences) that drive the organizational performance and value creation (Bontis et al., 2000; Roos & Roos, 1997). This suggests causal relationships between intellectual capital and organizational value creation (Marr & Roos, 2005). The research purpose is to construct a structural model of organizational justice in Islamic Azad University based on the intellectual capital.

Azizi Balvand et al.

2. Research questions

What is the structural model of organizational justice based on the intellectual capital in Islamic Azad University?

Which variable does have the highest effectiveness in organizational justice?

What is the predictive value of intellectual capital regarding promoting organizational justice?

How much is the goodness of fit in this study?

3. Method of the study The research methods which were used in this study are: library research to access the theoretical framework and the related literature; and the survey method to collect, classify, describe, and analyze the data. The population under investigation in this study consists of all the official staff members working in 13 branches of Islamic Azad University (11th administrative region) in Iran, who were selected using simple random sampling.

z 2σ In order to estimate the least volume of the sample, the following formula was used n = d 2

. The

research instruments were two questionnaires which were administered in 13 branches and educational centers. Beugre’s (1998) organizational justice questionnaire which consisted of 20 items measuring three underlying constructs of procedural justice, interactional justice, and distributive justice with Cronbach Alpha of 0.93, and Bontis’s (1997) 50‐item intellectual capital questionnaire, measuring three underlying constructs of human capital, customer capital, and structural capital with Cronbach Alpha of 0.89. The results of the study were calculated through path analysis using LISREL software.

4. Findings of the study The data included the different indexes of central tendency, variability and the distribution of staff’s groups, the staff members’ scores were obtained through the administration of intellectual capital and organizational justice questionnaires and their related components. The distribution of the staff members’ scores in the given variables showed tendency toward normality. Research questions:

What is the structural model of organizational justice based on the intellectual capital in Islamic Azad University?

Which variable does have the highest effectiveness in organizational justice?

What is the predictive value of intellectual capital regarding promoting organizational justice?

How much is the goodness of fit in this study?

Figure 1: Path analysis model for components of intellectual capital and organizational justice

Azizi Balvand et al. As shown in Figure 1, the Lambda rate of external latent variable of intellectual capital components was 0.80 for human capital, 0.84 for structural capital, and 0.90 for customer capital, it’s worth mentioning that their accumulation form the intellectual capital variable with the effectiveness rated 0.73, that is to say that, 73% of the variation in the dependant variable of organizational justice is explained by a collection of these indices. The variable of customer capital indicates the highest amount of internal consistency in the external latent variable. The Lambda rate of internal latent variable of organizational justice components was 0.91 for procedural justice, 0.72 for interactional justice, and 0.58 for distributive justice. Their accumulation form the organizational justice variable. The validity of variable indicates the highest amount of internal consistency in the internal latent variable. Since the model’s goodness of fit index is 0.95, it can be stated that it has an acceptable fit. The calculated index indicates the direct effect of intellectual capital components on the organizational justice. Moreover, the model shows that the highest direct effect is related to customer capital. The following table presents the indices related to the model’s fit: Table 1: Model’s fit indices Index

Rate

Interpretation

Lewis‐Tucker (Non‐normed fit index)

0.93

High fit (more than 0.90)

Bentler‐Bonett’s (Normed fit index)

0.91

High fit (more than 0.90)

Hoelter

0.73

High fit (more than 0.70)

Root Mean Square Error (RMSE)

0.031

High fit (equal to or less than 0.05)

GFI

0.95

High fit (more than 0.90)

The five goodness of fit indices presented model’s fit and empirical data. Therefore, desirability adaptation is provided for the designed model and empirical data and can approve it as an appropriate model for the organizational justice.

5. Discussion and conclusions The results of path analysis method revealed that dimensions of intellectual capital have positive impact on organizational justice. This finding is in line with the research findings carried out by Owolabi(2012), Yılmaz and Altinkurta(2012), Ford(2012), Ahmadi and Habibitabar(2011), Nazim et al., (2010), Shelton(2010), Dustin(2010),Kaneshiro(2008), Mccardle(2007), Casas (2007) , Robinson(2004), Zagorsk(2004) ,Weng (1998), and Howard(1993) ,findings also support previous literature highlighting the influential role of intellectual capital on organizational justice. The results of this study are in harmony with the research by Cohen and Prusak (2001) who have carried out a research into the social capital, and in their study, support was found for the value of human connections based on confidence and on personal networks with a community vocation. This notion is supported by the many previous studies that have also found that without social capital innovation, the sharing of knowledge and productivity can be dramatically reduced. A research on social capital by Koening explores that (1998) social capital facilitates the behavior rules of the organization, reducing transaction costs and promoting cooperation. These reasons justify the introduction of social capital into intellectual capital. The concept of justice is one of the important variables in organizational behaviour (Greenberg, 1999). Organizational justice is defined as the processes and procedures being measured, regular and that the staffs find their leaders impartial, sincere and that they find the deeds logical (Dessler, 1999). Nam (2008), in the survey s/he conducted, concluded that organizational justice is a determining factor in making up confidence. Moreover, There is a relationship between job satisfaction and organizational commitment (DeConinck & Stilwell, 2004).The results of the present research with regard to the effect of the intellectual capital upon the organizational justice, is meant to suggest that for the growth of organizational justice, long‐range plans are bound to be devised and developed in the universities regarding the efficient management strategies, in order to minimize the expenditure and the pursuit of cost reduction, growth of the average per capita income, promotion of innovative and novel ideas, facilitation of constant innovation, elimination of unnecessary and excessive bureaucracy, and creation of a favourable organizational climate are definitely needed. Customer capital is defined as the value of the organization perceived by those with whom an organization conducts business (Edvinsson & Malone, 1997; Saint‐Onge, 1996). This perceived value is formed by the relationships between the organization and its customers (Rudez, n.d.). This is shared

Azizi Balvand et al. knowledge among the individuals. Customer capital is elevated to the primary tier, Edvinsson & Malone (1997). With regard to significant and strong effect of the dimensions of customer capital on the organizational justice it can be proposed that:

universities foster long‐term relationship with their students and customers;

provide their customers with the most adequest and comprehensive level of educational service and satisfaction;

receive appropriate and relevent feedback;

reduce the time of dealing with and eliminating the problems;

shed light onto market conditions and applicants’ tendency toward new fields of study.

In conclusion, the newly‐proposed results in this research can be effectively employed to enhance the organizational justice in similar organizations.

Acknowledgements The authors want to extend a heart‐felt thank you to the members of Islamic Azad University (administrative region 11) for their commitment and efficient research assistance. They are truly appreciated as their partnership was vital to carrying out this research.

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What Makes an Enterprise Sustainable? Or: is “Green” Really “Green”? Dina Barbian University of Erlangen-Nuremberg, Information Systems I, Nuremberg, Germany dina.barbian@wiso.uni-erlangen.de

Abstract: The main goal of enterprises is to maximize their profits and to minimize their costs. This fact – coming from utilitarianism and the neo-classics – is not new. But by doing this, firms don’t recognize that they cause a huge amount of negative externalities with an extreme impact on the ecosystem. The consequences are a decreasing quality of the environment and natural resources, resulting in social costs (diseases, epidemics, life quality reduction) and ecological costs (degradation of air, water and soil, and ozone depletion). It is evident that every production process has an impact on the environment when it extracts resources from the environmental system and inserts waste into it. The ecosystem has its limits and its own complex rules, based on biochemical, geochemical, physical, oceanographic etc. processes. The environmental system has the unique capacity to assimilate waste and to regenerate. How can enterprises drive their processes in a socially and environmentally friendly manner? How could economic interests balance with social and environmental incentives? The concept of sustainable development can give some answers. Our way of life, which has become “greener” in recent years is a step towards sustainability: we produce “green products”. We use “green IT”. We live in “green homes”. But is that really a sustainable path? Technological progress can help to implement strategies and apply principles for sustainable competitive positioning and long-term success for sustainable enterprises. This paper gives an insight into the complexity of our world system, and the interdependences between enterprises and the natural environment. By applying a simple world model the author tries to develop a concept which is the basis to explain what does sustainability really means. The World Commission on Environment and Development (WCED) contributed the most frequently quoted definition. At the end of this article some “green” examples are shown and strategies for successful sustainable enterprises are developed. Keywords: sustainable development; environmental system (ecosystem, ecological system); laws of thermodynamics; world model; strategic management

1. Our world – a complex system The human species inhabits a unique planet – planet earth. The earth has existed for about 4.6 billion years. Humans have inhabited this planet for perhaps only 3 million years. With our technology we are able to destroy the environment, but we also have the capability to improve the living conditions for present and future generations. For about 200 years now, man has been aware that he is overexploiting nature and its resources. Our world is the only planet in the solar system, as far as we know, that supports life and has the right living conditions for several different species and living organisms, humans included. What makes planet earth so unique for humans? We have the right distance of approximately 150 million kilometres from the sun and thus the right temperature, not too cold and not too hot. According to NASA the global average temperature on earth is about 15°C. Temperatures on earth are suitable for life unlike anywhere else in our solar system. Venus, closer to the sun, reaches temperatures of more than 400°C, while Mars – further away from sun – drops to temperatures of -63°C. Not only the temperature, but also the existence of water, the atmosphere with just the right gases surrounding the earth, the fertile soil and the ozone layer make human life comfortable. We are absolutely dependent on planet earth and on the environment with its complex and dynamic processes. We call the whole system “ecosystem” or “environmental system” or “ecological system”. Planet earth is a closed system. A closed system exchanges energy only within its own environment (Georgescu-Roegen 1999). This means that neither matter nor energy can be created or destroyed (First Law of Thermodynamics). The First Law of Thermodynamics is one of the absolute physical laws of the universe. No exceptions or contradictions to this law have ever been observed. Oceans and planets and solar systems all operate under the control of the First Law of Thermodynamics. While the quantity of matter/energy remains the same (First Law), the quality of matter/energy deteriorates gradually over time (Second Law of Thermodynamics). Usable energy is inevitably used for productivity,

Dina Barbian growth and repair. In the process, usable energy is converted into unusable energy. Usable energy is irretrievably lost in the form of unusable energy. Applying the thermodynamic theory to the economic system there is a transformation from usable matter/energy (resources) to unusable matter/energy (waste). Enterprises produce goods for satisfying human needs. The economic system is thoroughly dependent on the usable matter/energy from the ecological system. Without these conditions the economic system couldn’t function, e.g. produce goods and services. In the context of achieving sustainable development it is essential that the economic system produces goods and services without consuming too much of the environment’s usable matter/energy. Planet earth is a very complex system with its own rules. Earth's main cycles, the water, carbon, nitrogen, phosphorus and glacial-interglacial cycles and earth’s biophysical, geochemical, oceanographic etc. processes are difficult to understand. In the next chapter the author will endeavour to develop a simple world model in order to simplify the complex system called earth. This simple world model is the basis used to find strategies and to apply principles for sustainable enterprises.

2. A simple world model To produce in a sustainable manner and to be a sustainable enterprise means to understand the relationship between human needs and the natural environment. Currently the world population is over 7 billion people. All these people need food, shelter, mobility and energy. How can enterprises satisfy the needs of a growing world population without damaging the natural environment in which they live? First of all there is the need to understand the system called earth on which the human race is dependent. A first approach as shown in Figure 1 is to reduce our complex ecosystem to five natural elements necessary to sustain life: water, soil, air, the ozone layer and the sun (Barbian 2001).

Figure 1: A simple world model Human beings are dependent on these five natural elements, because all life is controlled by one or more of these. Soil: Our world has a limited surface area. Arable land for agriculture and cultivation is limited. Our earth can produce a limited amount of food. Only 29 % of the earth’s surface is soil as 71 % is water. According to the 2 Food and Agriculture Organization of the United Nations (FAO), 14 million km (11 %) of land are used for agriculture, 26 % is pasture land and 32 % forests. The remaining 31 % is grazing land, desert, cities etc. The soil used for agriculture is limited because not all soils are arable. With our technology we are able to improve and increase productivity. Water: 99.5 % of our biosphere and 71 % of the earth’s surface consist of water. Humans need water for various usages. Only 3 ‰ of the water on earth is potable. We have enough water for everyone; the problem is the availability of drinkable water.

Dina Barbian Air: There is enough air for all living beings. Pollution is the main reason for diminishing air quality. What are the consequences? Regions with high air pollution cause harm and discomfort to humans and other living organisms. Air has the capacity to regenerate. Ozone layer: The ozone layer is a layer in the earth’s atmosphere which contains relatively high concentrations of ozone. This layer absorbs 97-99 % of the sun’s high frequency ultraviolet light, which is damaging to life on earth. So the ozone layer makes life possible. It can be depleted by various greenhouse gases (such as, carbon dioxide, methane, water vapour). Sun: The sun is the star at the centre of the solar system. The energy of sunlight supports almost all life on earth and drives earth’s climate and weather. The sun is the only natural element that cannot be influenced by man. What are the main influences on the natural elements? The way enterprises drive their businesses in order to satisfy the consumers’ needs have an impact to these elements. In the following chapter the interdependences between enterprise and the ecosystem are illustrated.

3. Enterprises and the ecosystem The main goal of enterprises is to maximize their profits and to minimize their costs. They have to do this for their competitive positioning, but by doing so they often cause negative externalities (social and environmental costs). Some enterprises seem to forget that they are dependent on the ecosystem. For the production of goods and services (for consumer’s satisfaction of needs) they use environmental resources, such as water, and produce waste (see Figure 2). Gaseous waste is dissipated into the air; solid and liquid waste into the ground.

Figure 2: Interdependences between enterprises and the ecosystem Resources come from sources and waste is put into sinks; both sources and sinks are located in the ecosystem. How much waste can planet earth absorb? The ecosystem has the unique ability to regenerate. Natural resources can be renewed, but over what period of time? Every production process impacts on the environment by extracting resources from and inserting waste into the ecosystem. A minimum impact on the environment is inevitable when driving business processes, because almost all production lines need resources and produce waste. The economic system is completely dependent on the ecosystem; therefore it is considered a subsystem of the environmental system. The environmental system has its limits. The question is: Can a subsystem grow when the main system has its limits? Many services are provided by the ecosystem and most of them at no cost. Common and Stagl (2005) mention the following interdependences between the ecosystem and the economic system (see Figure 3): 

The environment is the source of inputs of natural resources to production;

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the receptacle for the wastes arising in production and consumption;

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a source of amenity services to consumption;

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the source of life support services to humans.

Figure 3: Interdependences between the ecosystem and the economic system (Common and Stagl 2005) For a sustainable competitive positioning of an enterprise it is necessary to minimize the impact to the environment as much as possible. What does it mean to be sustainable? This question will be discussed in the next chapter.

4. What is sustainability? Today everybody is talking about sustainability, but do companies really know what it means? Sustainable development thinking was progressively developed through the World Conservation Strategy (1980) by the International Union for Conservation of Nature and Natural Resources (IUCN), which was founded in 1948. IUCN is the world’s oldest and largest global environmental network. The term “sustainable development” was first mentioned in the IUCN's “World Conservation Strategy – Living Resource Conservation for Sustainable Development” (IUCN 1980). The most frequently quoted definition of sustainable development is that from the World Commission on Environment and Development (WCED 1987): “Sustainable development is development that meets the needs of the current generation without compromising the needs of future generations.” Analyzing this definition, it is evident that sustainable development expresses a concept that combines the following five descriptors: anthropocentrism (humans at the centre of interest), long-term durability, nature conservation, equality and justice (intra- and intergenerational) and comprehensiveness. The starting point is the ethical idea based on obligations towards future generations. The definition contains two key concepts: the concept of needs, particularly the essential needs of the world population; and the idea of limitations, expressed by the ability of the environment to meet present and future needs (WCED 1987). The UN Conference on Environment and Development (UNCED), held in Rio de Janeiro (Brazil) in 1992, called for sustainable development “to ensure socially responsible economic development while protecting the resource base and the environment for the benefit of future generations”. The concept is built on three pillars: Economic, Social and Environmental (IUCN 2004; Vom Brocke, Seidel and Recker 2012). Nowadays the economic pillar has still the most importance. Change in the other two pillars (social and environmental) is necessary to allow them to grow and have the same degree of importance as the economic pillar. This model is also known as the triple bottom line (TBL), designed to recognize not only the importance of economic values in an enterprise but also to target social and environmental performance. In 1981 Freer Spreckley stated: “To maintain a separate existence, enterprises should not be in a position to use this freedom at the expense of social and environmental costs.” Spreckley pointed out the importance of all three pillars for enterprises. Later, in 1994, John Elkington mentioned the term “triple bottom line” for the first time.

Dina Barbian He discusses some ways in which enterprises can develop new "win-win-win" strategies, which simultaneously benefit the company, its customers and the environment through sustainable development. In 1997 he continued to develop the concept in his book “Cannibals with Forks” (Elkington 1997). The TBL is a model which evaluates success using three criteria: Economic (Profit), Social (People) and Environmental (Planet). Balance of these three domains theoretically results in sustainable development. In this article the author follows the TBL model and tries to find some descriptors in order to develop a concept (see Figure 4) that could contribute to clarity in defining principles for enterprises. The descriptor “Anthropocentrism”: Principle 1 of the Rio declaration states: “Human beings are at the centre of concerns for sustainable development. They are entitled to a healthy and productive life in harmony with nature.” (UNCED 1992) Sustainability emphasises an anthropocentric point of view. Anthropocentrism means that humans are at the centre of the debate. Sustainability focuses on humans. Humans are absolutely dependent on natural resources and an intact ecological system. The descriptor “Nature Conservation”: Humans care first for their own life and then for Nature. But man is only “complete” if he protects Nature adequately. So it is essential that the ecological system plays an important role when founding principles for sustainability. The descriptor “Justice and Equality”: Different developments in world countries give the people different chances to secure their survival and the survival of their descendants. Currently one quarter of the world population is using three quarters of the world’s resources. Justice can only exist within the coordinates of equality. This basic view can be elaborated in many different ways, according to what goods are to be distributed – wealth, respect, opportunity – and who they are to be distributed equally between – individuals, families, nations, races, species (Jonas 1985; Rawls 2005). Pearce (1988) differentiates between three sorts of justice: Intragenerational Justice (justice within present generation), Intergenerational Justice (justice between present and future generation) and Justice to Nature. Sustainability focuses on humans of all generations, present and future. All generations should have the same access to natural resources and the same chances to satisfy their needs. Problem: How can we allocate resources between the present and future generations? Future generations are not represented in the decision-making processes of today. Do we know the preferences of future generations? And: How can we expect impoverished countries to care about future generations, if they cannot even care for their own people today? Edith Brown Weiss (1989) developed a theory of intergenerational equity: “At any given time, each generation is both a custodian and trustee of the planet for future generations and a beneficiary of its fruits. This imposes obligations upon us to care for the planet and gives us certain rights to use it.” According to this statement there should be fairness among all generations. Each generation is a user and has moral obligations to future generations. It receives a natural and cultural legacy in trust from previous generations and holds it in trust for future generations. The descriptor “Comprehensiveness”: Sustainability affects all world countries and all human beings. In most cases environmental problems (e.g. enhanced greenhouse effect, holes in the ozone layer) are world problems. An interdisciplinary approach should be taken, because economic theory alone cannot solve existing problems. The descriptor „Long-term durability”: How long is long-term? Costanza and Patten (1995) have discussed this question and came to the conclusion that it does not mean “maintenance forever”, because “nothing lasts forever, not even the universe”. According to them a “sustainable system … is thus one that attains its full expected life span within the nested hierarchy of systems within which it is embedded”. This means that a shortening of the normal natural lifecycle of systems has to be avoided.

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Figure 4: Concept of sustainable development (Barbian 2001)

5. Threats to sustainability Every production process has an impact on the environment when businesses extract resources from the environment and insert waste into it. The ecosystem has its limits and its own complex rules, based on biochemical, geochemical, physical and oceanographic processes. The environmental system has the unique capacity to assimilate waste and to regenerate. We donâ&#x20AC;&#x2122;t know what exactly the regenerative and assimilative capacities of the environmental system really are, so there is need for research in these areas. What are the major negative influences on the environment? According to the simple world model presented in chapter 2, man is dependent on five natural elements: sun, water, soil, air and the ozone layer. There is increasing soil degradation on earth caused by desertification, deforestation, erosion and wastage. According to the World Bank there is a decrease of arable land of about 20 million hectares per year (World Bank 2008). An increasing world population and the increasing demand for food and energy make it necessary to intensify the use of arable soil. By applying sustainable competitive positioning principles to enterprises it is of major importance to implement strategies for protecting the soil. Increasing water pollution in oceans, rivers and lakes contributes to the threat to potable water. In the sense of sustainability, water should be saved and protected. Implementation of adequate water and waste water management in enterprises is needed. Air pollution (caused by industry, private households and traffic) accounts for several problems especially in urban areas. Increasing air pollution is harmful for peopleâ&#x20AC;&#x2122;s health, because it leads to chronic heart and lung diseases. But not only mankind suffers; air pollution can be harmful for plants and trees and also for buildings. Who meets these social and environmental costs? Medical treatment and restoration of buildings can be very expensive. Increasing deterioration gives the environment less time to maintain the natural elements for life. Enterprises have the option of choosing a sustainable way with a less destructive influence on the ecosystem. This means that the impact of interference with nature is allowed within the regenerative capacity to produce natural resources and the assimilative capacity to absorb waste. Sustainable enterprises run their businesses by protecting air, water and soil, and by reducing their greenhouse gases. Their use of natural resources is efficient. Sustainable enterprises protect the environment by reducing emissions and preventing biodiversity loss. By doing this, enterprises implement adequate waste management and effective recycling efforts. In the following paragraphs the author shows two examples of how enterprises run their businesses badly using needless transport and causing unnecessary traffic (BUND 2006): ď&#x201A;§

Where are German North Sea shrimps from? Sent from the German shrimp fishery for shelling by Moroccan workers, they have travelled thousands of kilometres across Europe by the time they reach the market. From an economic point of view this is one way to reduce economic costs, because Moroccan

Dina Barbian labour is cheaper than German labour and the transport costs are cheap enough. But what about the environmental costs (exhaust fumes, noise, and carbon dioxide emission)? 

The long journey of German strawberry yoghurt: By the time the yoghurt reaches the shop it has caused 9,000 kilometres of truck transport in Europe: the strawberries are from Poland and transported to Aachen (Germany), the tags are printed in Bavaria (Germany) on Dutch paper and the glue comes from Belgium.

Modern economies do not give suitable answers to reducing negative externalities, because their main focus lies on the economic issue. Sustainable enterprises reduce not only their economic costs but also their environmental impact and call for social incentives. Some enterprises are already on a sustainable path. They produce “green” products, use “green IT” or even are situated in “green” buildings. Whether this is really “green” is of interest for the following chapter.

6. Is “green” really “green”? As mentioned, enterprises have to “use” the environment’s resources and services to produce goods for the satisfaction of needs. With our knowledge and our technology we are able to find sustainable ways to produce environmentally friendly consumer goods and services. In the first instance, sustainability calls for product longevity and efficient use of natural resources, especially energy and water. This means avoiding energy and water loss. Therefore efficient water, energy and waste management must be implemented. This implementation requires extra personnel and some financial investment. As a result, enterprises can profit from advantages such as effective use of resources, energy efficiency and reduction in waste. The remaining question is how to design products so that they can be called “green products”? Some “green” examples: 

The Tûranor PlanetSolar Catamaran: In May 2012 it became the first ever solar electric vehicle to circumnavigate the globe. It is the largest solar-powered boat in the world. (Cost: € 12.5 million; Speed: 14 knots (26 km/h; 16 mph) (max), 7.5 knots (13.9 km/h; 8.6 mph) (cruising))

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The One Litre Car created by Volkswagen: The Company announced the production for 2013. The car is a two-seater. It was designed to travel 100 kilometres on 1 litre of diesel fuel (from L/100 km: equivalent to 235 miles per U.S. gallon or 282 miles per Imperial gallon), while being both roadworthy and practical. To achieve such economy, it is produced with lightweight materials, a streamlined body and an engine and transmission designed and tuned for economy.

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The Bullitt Center - The Greenest Commercial Building in the World (Seattle, Washington): Solar panels on the roof to generate energy; special toilets (composting toilets) the contents of which will be composted and decontaminated; a geothermal system to warm the building up in the winter and cool it down in the summer; temperature regulation by programmed windows which open and shut automatically.

These are three examples from the real world that show how life could become more sustainable. Since man has been on earth he has been satisfying not only his needs but also his wishes and wants (to become faster, bigger, richer, better etc.). The remaining question is: Are the examples presented really “green”? The socalled “green movement” has pushed the importance of a sustainable development into the awareness of businesses, but there is still the need to design suitable business processes (Vom Brocke, Seidel and Recker 2012). This means that there is the need to find holistic solutions for businesses. The solution can’t only be product-oriented, but should at the same time be process-oriented (“cradle-to-grave”). It is also important not to forget the consumer. Consumer have the choice between buying (maybe more expensive) sustainable products and using conventional (cheaper) and less environmentally friendly products (Cooney 2009). The following chapter shows how an enterprise could become a sustainable enterprise and be successful in sustainable competitive positioning.

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7. A sustainable enterprise Sustainable enterprises have the same goals as all enterprises - to maximize their profit and to minimize their costs, but they achieve it in an environmentally and socially friendly manner. They ensure that all processes, products, and manufacturing activities adequately address economic, environmental and societal concerns. What is meant by being a “sustainable business” is described by Tueth (2010) as the implementation of “effective sustainable business practices which increase profits and improve the relationships an organization has with the natural world and human society”. These businesses will achieve a competitive economic positioning not only by investing in its employees but also by complying with ecological issues such as to respect the natural elements necessary to sustain human life. Another explanation is given by Cooney (2009). He calls a “green business” to be “an enterprise that has no negative impact on the global or local environment, community, society, or economy”. Sustainable enterprises – as mentioned here – must care for the environment by protecting the natural elements necessary to sustain life. This means that sustainable enterprises make efforts to protect the air, the soil and the water. They implement strategies to reduce their carbon dioxide and other greenhouse gas emissions in order to protect the ozone layer. Where possible, they try to reduce their amount of nonrenewable resources, and search for renewable substitutes. They use – where possible – recycled materials to drive their manufacturing processes. They respect nature’s ability to absorb waste and to regenerate. (This implies knowledge of the assimilation and regeneration function of the environment, so research and education is important.) Sustainable enterprises implement suitable waste and waste water management systems. This means that they avoid producing waste where possible and when it is unavoidable, they reuse or recycle it. Agreeing to the “Concept of Sustainable Development” (see figure 4) enterprises should follow the five descriptors: “Anthropocentrism”, meaning that humans are at the centre of interest; in the case of firms, the employees and customers play an important role. They are considered to be an essential part of long-term success. “Nature Conservation”, meaning that the impact of interference with nature is kept to a minimum. Efforts must be made to reduce emissions and investment made in projects where emissions can be absorbed, e.g. reforestation. “Equality and justice”, meaning that non-discrimination, tolerance, respect for diversity, equality of opportunity, security and the participation of all people are important. “Comprehensiveness”, meaning a holistic point of view, which sees the enterprise as a whole, as in a cybernetic regulating cycle (Vester 2007). Every part of the enterprise is important. “Long-term durability”, meaning that enterprises aim for competitive positioning and long-term success. They produce long-lasting and durable products. Furthermore they continuously improve their environmental performance and their processes. When all companies aspire to these measures and implement them in their strategic management, then we are on the road to sustainability.

8. Conclusion and discussion Our world is a complex and dynamic system; some natural processes are difficult to understand. Therefore the author uses a simple world model in order to reduce it to five natural elements which are necessary to sustain life on earth: air, water, soil, ozone layer and sun. Every production process on earth, even the production of so-called “green products”, has an impact on the ecosystem, but “green” companies try to reduce their negative externalities by improving their efficiency. Technological progress can help.

Dina Barbian The concept of sustainable development (see figure 4) can contribute to find strategies, principles and policies for sustainable enterprises. Economic incentives are as important as ecological and social incentives. Humans (such as employees, customer, supplier) play an essential role. In chapter 6 some examples has been shown how the world can become “greener”. In some cases we are already on a sustainable path, but there is need for a holistic integrated “cradle-to-grave” view. This means that enterprises have to integrate their “green” thinking in the whole manufacturing process. Sustainable enterprises implement strategies and tools for their plant and product lifecycle management and integrate them in their supply chain management, customer relationship management and enterprise resource planning. They strive for long-term solutions and long-lasting products.

References Barbian, D. (2001) Ökonomie und Sustainable Development, Shaker, Aachen. Brown Weiss, E. (1989) In Fairness to Future Generations: International Law, Common Patrimony, and Intergenerational Equity, United Nations University, Tokyo. BUND (2006) Wahnsinn Güterverkehr, [online], Berlin, http://www.bund.net/fileadmin/bundnet/publikationen/verkehr/20060800_verkehr_gueterverkehr_flyer.pdf. Common, M. and Stagl, S. (2005) Ecological Economics – An Introduction, Cambridge University Press, New York. Cooney, S. (2009) Build a Green Small Business – Profitable Ways to Become an Ecopreneur, McGraw-Hill, New York. Costanza, R. and Patten, B.C. (1995) “Defining and Predicting Sustainability”, Ecological Economics, Vol. 15, No. 3, pp 193196. st Elkington, J. (1997) Cannibals with Forks: the Triple Bottom Line of 21 Century Business, SustainAbility Ltd., London. Georgescu-Roegen, N. (1999) The Entropy Law and the Economic Process, Harvard University Press, Cambridge. IUCN (1980) World Conservation Strategy – Living Resource Conservation for Sustainable Development, IUCN – UNEP – WWF Report, [online], Gland, http://data.iucn.org/dbtw-wpd/edocs/WCS-004.pdf. IUCN (2004) The IUCN Programme 2005-2008 – Many Voices, One Earth, The World Conservation Congress, [online], Bangkok, http://cmsdata.iucn.org/downloads/programme_english.pdf. Jonas, H. (1985) The Imperative of Responsibility: In Search of an Ethics for the Technological Age: In Search of an Ethic for the Technological Age, University of Chicago Press, Chicago. Pearce, D. (1988) “Economics, equity and sustainable development”, Futures, Vol. 20, No. 6, pp 598-605. Rawls, J. (2005) A Theory of Justice, Harvard University Press, Cambridge. Rennie, E. (2008). "Growing Green, Boosting the bottom line with sustainable business practices", APICS Magazine, Vol. 18, no. 2. Spreckley, F. (1981) Social Audit - A Management Tool for Co-operative Working, Beechwood College, [online], Leeds, http://www.locallivelihoods.com/cmsms/uploads/PDFs/Social%20Audit%20-%20A%20Management%20Tool.pdf. Tueth, M. (2010) Fundamentals of Sustainable Business – a Guide for the next 100 Years, World Scientific, London. UNCED (1992) Report of the United Nations Conference on Environment and Development, Rio Declaration, 3-14 June 1992, [online], Rio de Janeiro, http://www.un.org/documents/ga/conf151/aconf15126-1annex1.htm. Vester, F. (2007) The Art of interconnected thinking: Tools and concepts for a new approach to tackling complexity, MCB Verlag, Munich. Vom Brocke, J., Seidel, S. and Recker, J. (Eds.) (2012) Green Business Process Management – Towards the Sustainable Enterprise, Springer, Heidelberg. WCED (1987) Our Common Future, Oxford University Press, New York. World Bank (2007) World Development Report 2008: Agriculture for Development, [online], Washington, http://siteresources.worldbank.org/INTWDR2008/Resources/WDR_00_book.pdf.

Creating Virtual Mentoring Programs for Developing Intellectual Capital Bob Barrett American Public University, Charles Town, USA docjob00@msn.com Abstract: As more technologies evolve, economies change, and companies desire to expand globally, the intellectual capital in any organization also needs to be considered in terms of their current value and what they could offer the organization even more with the proper attention and development. Stewart (1997) noted that “human capital includes the dynamics of an intelligent (learning) organization in a changing competitive environment, its creativity, and innovativeness” (Stewart, 1997, p. 13). However, the dynamics of an organization can be affected by differences in locations, time zones, cultural and societal differences, as well as many more factors. The key to addressing these items is constant communication, as well as applying a continuous improvement program. While many companies may seek to expand, they may only focus on the profits as an immediate reward. However, they also need to consider the value of their employees and what they can bring to the table, as well as how the employer can help develop their employees to their fullest extent. Thus, many organizations are starting to realize the importance of having a learning organization that constantly monitors and tries to improve upon its current practices and strategies, as well as reflecting on lessons learned. According to Senge (1990), “learning organizations [are] organizations where people continually expand their capacity to create the results they truly desire, where new and expansive patterns of thinking are nurtured, where collective aspiration is set free, and where people are continually learning to see the whole together.” (p. 3) However, in many organizations, employees may be tied to specific performance requirements and set methods of modus operandi, which may limit their own personal development. If the employees are not considered in terms of their value and potential, then the organizations starts to fail in their capacity as a learning organization. In particular, some learning organizations may only be seen as valuable in terms of helping their employees in or near the main location. Expatriates may feel distanced from the main organization and lacking in proper attention and/or training for their jobs. On the other hand, many of these organizations who are now expanding globally are realizing the need to keep a close connection to the expatriate and the main headquarters. One way that many learning organizations are connecting employees and showing a personal interest in their employees is through the creation of mentoring program. While many people think of mentoring relationships as being on an one‐on‐one basis and in the same physical location, this is changing with modern technology. Thus, this paper will focus on the importance and need to create mentoring program as another lay of commitment in which management and employees can connect and help develop the organization’s intellectual capital both locally and globally. Keywords: virtual mentoring, human capital; organizational dynamics; learning organization

1. Human capital’s role in today’s organizations The key focus of this paper will be to look at how one university assessed the current intellectual capital needs in order to create virtual mentoring programs. While values may change, economics can fluctuate, and standards can be reorganized and aligned as needed, one key element is constant – the need for human capital to help make organization run. How they operate, whether smoothly or not, may depend on the types of people recruited and hired, as well as how they are treated. While Morgan (1986) focused his work on various metaphors used to describe the work of organizations, he did also focus on organizations and how they learning. By examining the works of Weiner in the field of cybernetics, he looked at this area and how it leads to a theory of communication and learning in the context of four principles. According to Morgan (1986): “First, that systems must have the capacity to sense, monitor, and scan significant aspects of their environment. Second, that they must be able to relate this information to the operating norms that guide system behavior. Third, that they must be able to detect significant deviations from these norms. And fourth, that they must be able to initiate corrective action when discrepancies are detected.” (pp. 86‐87). If one considers these factors, a variety of patterns can be seen that organization have many different facets that are interconnected. Further, there is a need for learning and sharing information in organizations, but the key is whether or not the organization deems learning and sharing as two key items of their modus operandi. While they may think they make it known to their employees that these two factors are important to the organization as a whole, they need to “practice” what they espouse in theory and put it into daily use (National Education Institute, 2012). Morgan (1986) noted that “the whole process of learning to learn hinges on an ability to remain open to changes occurring in the environment, and on an ability to challenge operating assumptions in a most

Bob Barrett fundamental way.” (p. 91). While some organizations think they are open and accepting of change, it may be for show only and no processes may be in place. Therefore, how do these organizations actually learn what is happening or not happening in their organization? Along the same lines, how do they know if learning and sharing of information is really happening among their employees or is it decentralized and only is shared and contained in certain departments instead of the entire organization? If this does happen, why does it continue to be self‐contained or operating in a vacuum? Further, Morgan (1986) stated that “the learning abilities thus defined are limited in that the system can maintain only the course of action determined by the operating norms or standards guiding it.” (p. 87). Thus, we might be able to surmise that it may be a result of organizational culture or department/division limitations that might put parameters on the process of learning in an organization. Consequently, the limitations may be on the current human capital in organization or perhaps those with a certain mindset that control or limit progress or change in the organization. As a result many companies and organizations are trying to “break these patterns” and institute some changes in their recruitment and selection process. Barbara Smith (2000), chief learning officer for Burson‐Marsteller stated that “If we don’t have the best people creating the best product, we can’t compete. What I’m after is creating the best people in the industry. E‐Learning is an option that provides us with real competitive edge – it helps us maximize our intellectual capital” (Koprowski, 2000, pg. 1). Thus, technology is breaking down current knowledge barriers and practices. One of the key barriers has been the lack of acknowledging the effect of errors and changes in an organization in terms of learning and creating more knowledge for the organization. Morgan (1986) addressed why it is important to accept errors and changes in order to learn from them. He noted, that ““Rather than create conditions which lead employees to hid or deny error and to avoid asking problematic questions, as often happens under bureaucratic systems of accountability, it is necessary to encourage them to understand and accept the problematic nature of the situation with which they are dealing. A philosophy that ‘it is admissible to write off legitimate error against experience,’ and that ‘negative events and discoveries can serve as a source of knowledge and wisdom of great practice value,’ is an example of the kind of stance required” (p. 91). If we keep this in mind as a foundation in this paper, we can then move forward to look at the current value of intellectual capital, namely human capital, in today’s organization and how learning and the use of mentoring can help organizations to progress, rather than live and operate in their own world (SCORE, 2012). The next section will focus on the rethinking and re‐valuing of intellectual capital in today’s world of work. In particular, it will concentrate on what is changing and needs to change in order to help progress both human capital and its value in organizations today.

2. Rethinking and re‐valuing intellectual capital Senge (1990) reinforced Argyris’s line of thinking in terms of why corporate managers may find “collective inquiry inherently threatening” (p. 25). He noted that “School trains us never to admit that we do not know the answer, and most corporations reinforce that lesson by rewarding the people who excel in advocating their views, not inquiring into complex issues” (Senge, 1990, p. 25). For many decades, employees did what they were “instruct” and never question issues and pursued inquiry of questionable factors, issues, or decision – unless they were told to proceed in such a manner. However, this has been changing over the past two decades in terms of employees starting to be given more empowerment and leverage to proceed with inquiry and perhaps profits from good findings in terms of merit bonuses and promotions. Clark and Mirabile (2004) summed; up a few of the realities that started to appear in the reshaping of key workplaces. They noted:

Work itself is being redefined. More emphasis is placed on lifelong learning, higher‐order thinking, and an ever‐increase demand for innovation.

The war for talent is an explicit condition of strategic business operations.

Companies specializing in some form of online training or knowledge management that did not exist five years ago have burst into the technology and service marketplace (p. 113).

Thus, as we see changes happening in the workplace as a result in changes in work processes and technology, we are also seeing changes in social capital. Daniel, Schwier and McCalla (2003) wrote that “Social capital is an imprecise social construct that has emerged from a rather murky swamp of terminology, but it is still useful for exploring culture, society and social networks. The notion of social capital originated from studies of conventional or temporal communities. Social capital highlights the central importance of networks of strong personal relationships that develop over a period of time.” (p. 2) Thus, as companies and organizations continue to grow and invest in technology, sometimes employees feel a disconnection between them and

Bob Barrett their own network of family, friends, and colleagues. Further, as they start to interface more with technology, they tend to question their worth and value in their respective places of employment. In terms of this paper, we will now turn the key focus to human capital and its importance in today’s world of work and its contributions to the field of business and intellectual capital.

3. Human capital and knowledge management We can view human capital in two capacities. One as a caring, nurture group of people involved has an interest not only in being part of the organization, but also interested in helping the organization reach its goals. In another view, the human capital, as a whole, can be seen as a group that not only expects to be valued, but also values employers and managers who see them as an important commodity in the organization’s overall progress and schema. However, what happens when human capital is not fully value and their potential realized? As more organizations have started to change their perspective towards human capital, they have also realized the need to learn from their mistakes and take another look at lessons learned. However, they have also started to latch onto a new way of managing and leading with a useful tool called knowledge management. They have learned to start “capturing” what they have been doing and analysing whether what they have done has been good, bad, or indifferent with their intended objectives and goals. Stewart (1997) wrote that “human capital includes the dynamics of an intelligent (learning) organization in a changing competitive environment, its creativity, and innovativeness” (Stewart, 1997, p. 13). One has to wonder if the dynamics of an organization can be affected by differences in locations, time zones, cultural and societal differences. One way to view these items is by looking at the way organizations communication. We need to consider that while many companies may seek to expand, they may only focus on the profits as an immediate reward and not consider the factors that attribute to their success. Also, they also need to think about the value of their employees and what they can bring to the table. Therefore, many companies and organizations are starting to realize the importance of creating a learning organization that constantly monitors and tries to improve upon its current practices and strategies, as well as reflecting on lessons learned. If we go one step further here, we need to consider the relevance of an organization’s consideration of the lack of or limited use of knowledge management. Mintzberg, Ahlstrand and Lampel (1998) stated: “Ask someone to define strategy and you will likely be told that strategy is a plan, or something equivalent – a direction, a guide or course of action into the future, a path to get from here to there. Then ask that person to describe the strategy that his or her own organization or that of a competitor actually pursued over the past five years – not what they intended to do but what they really did. You will find that most people are perfectly happy to answer that question, oblivious to the fact doing so differs from their own definition of the term (p. 9). If we keep the above line of reasoning in mind, we can see a need to have clearer lines of communication, a potential for creating a learning organization to benefit the human capital and organization, as well as creating a form of knowledge management for the better of all parties in the organization. As organizations have started to go through a small evolution, or metamorphosis, change either works for them or against them. Also, change effects the organization and its components, namely, the human capital. However, the key is how do organizations and its human capital learn? Or is their learning done in a vacuum? This leads to the next major question as to whether learning organizations can learn and develop an organization and its human capital.

4. Organizational change and learning organizations Senge (1990) noted that “learning organizations [are] organizations where people continually expand their capacity to create the results they truly desire, where new and expansive patterns of thinking are nurtured, where collective aspiration is set free, and where people are continually learning to see the whole together.” (p. 3) The key point to keep in mind from this point forward in this paper is whether employees are free learn and whether this learning is nurtured by the company. Do companies and organizations truly show an interest in their human capital and reiterate this interest in terms of their various forms of communications and actions? Further, we need to question whether or not the human capital of an organization is encouraged or permitted to expand their capacity – or perhaps is it limited by the organization as a whole and its leadership. Further, we can see that in many organizations, the human capital may be tied to specific performance requirements and set methods of modus operandi, which may limit their own personal career development

Bob Barrett and mobility. If the employees are not considered in terms of their value and potential, is it possible that organizations may start to fail in their capacity as a learning organization? Specifically, is it possible that some learning organizations may only be seen as valuable in terms of helping their employees in or near the main location and no further? In particular, expatriates may feel some form of “distance” between them and the main organization and the organization’s lacking in proper attention and/or training for their jobs. Consequently, many of these organizations who are now expanding globally are realizing the need to keep a close connection to the expatriate and the main headquarters. As technology has expanded the possibilities of interaction, socialization, and managing people from multiple global locations, the need to “harness” these possibilities and to understand them is growing even greater each year. Sokolowska (2006) viewed learning organizations in the context of two trends, which she stated that “such organizations are understood in dynamic organizational categories that are oriented at their development, searches for new chances on the market and continually increases their effectiveness, efficiency and flexibility. The second group of learning organizations consists of such entities that choose growth through development of their employees.” (p. 158) Therefore, one has to wonder if this emphasis on dynamic categories, new market approaches, and employee development might affect other stakeholders in the framework of creating and maintaining trust in the workplace. One way to build trust and to “recapture” the interest of all workers is to show that the organization does value its human capital and will “invest” in them. As a result, we are seeing a trend in organizations where they are encourage interaction among the employees and sharing of information. Lave and Wenger (1998, 2006) defined communities of practice as being formation of “people who engage in a process of collective learning in a shared domain of human behavior . . .” (pg. 3). These communities can create, share, maintain, develop, and evaluate all types of knowledge through various activities, such as problem solving, sharing developments, documenting and working on projects, and mapping out knowledge and seeking improvements, as well as striving for better achieve of organizational objectives and goals. Further, there is a growing trend in which learning organizations are connecting workers and showing a personal interest in them via the creation of mentoring programs. While people may think of a mentoring relationship as being on an one‐on‐one basis and in the same physical location, this is changing with modern technology. Thus, this paper will focus on the importance and need to create mentoring programs as another layer of commitment in which organizations and employees can connect and help develop the organization’s intellectual capital both locally and globally.

5. Mentoring and global learning organizations The world is getting smaller, and learning organizations are continuously training. Why are they changing? They are changing due to the demands of learning, sharing, and economics factors that affect organization. According to Professor Mitleton‐Kelly (2003) wrote “A learning organization is one that is able to change its behaviors and mind‐sets as a result of experience. This may sound like an obvious statement, yet many organizations refuse to acknowledge certain truths or facts and repeat dysfunctional behaviors over and again” (para. 1). While some organizations may want to follow the trends of others, one has to wonder if they willing to make the time and investment in learning and using the best practices of others? In some cases, there may be some organizations that may want to take the “quick fixe” approach, as noted by Senge (1995). It should be noted that while true learning organizations soon realize the benefits of trying “different approaches”, others organizations may called themselves a learning organization without realizing that they are not “tapping into” their Intellectual Capital at its fullest potential. In particular, they may not realize the value and potential contributions of their human capital – and may not fully utilize all of their skills and abilities to the benefit of their organization and the employees overall. While some organizations may not fully realize the value of their human capital, this does not mean that learning in the organization is not possible. In fact, it may be happening in various forms or context and later realized by some key people. Mitleton‐Kelly (2003) noted that “Learning organizations encourage self‐ organization, so that groups can come together to explore new ideas without being directed to do so by a manager outside that group. This is the process that occurs naturally around the coffee machine or the water cooler, but learning organizations actively encourage self‐organization and do not see it as a waste of time. This is an essential part of the innovative process which is also an integral part of creating an environment that facilitates co‐evolutionary sustainability” (page 2). While many companies may view this “informal learning” as not being beneficial and not consider it as a learning event or situation at the present time or even at a later

Bob Barrett date. In any event, we have to consider – can all of the potential with our Intellectual Capital occur in a cubicle or office? Must it always occur in a physical location – or can it occur globally through the use of virtual meetings and interactions as well? Thus, how do organizations manage its Human Capital? Management needs to measure various factors to determine whether employees are benefitting the organization and to what degree. How can they be measure? Here are some items that management can evaluate in this area.

Training programs

Credentials

Experience

Competence

Recruitment

Mentoring

Learning programs

Individual potential

Personality (cited in http://www.cpavision.org/vision/wpaper05b.cfm)

While many companies may promote the importance of training and education, many entities are rethinking and re‐evaluating the use of their funds to help support their employees. Thus, we need to understand what is mentoring and how does it play a role or function in today’s learning organization? This leads us to the next section to focus on whether or not virtual mentoring programs can help organizations in terms of their domestic, as well as international, employees? Basically, can human capital benefit from such a virtual program or should it always be a live event?

6. Creating and developing virtual mentoring programs We have seen many world economies grow or shrink in terms of budgets and employee numbers, many organizations are starting to go global even more today than ever before. As more and more organizations rush to open global locations, they are still faced with the realities of dealing with overseas locations in terms of different time zones, cultures, business practices, and customs. While many businesses are relying more on virtual technology, and there appears to be a growing need for more training education. As a result, many companies are looking towards virtual learning to help bridge the gap between traditional training in order to meet different work hours, business commitments, as well as other external factors which may hinder the traditional approach to training. Specifically, as more companies rely on the training and transfer of expatriates to overseas locations, there is a growing need for better training applications to help in the creation and implementation of such training opportunities. Many Human Resources (HR) personal are realizing that quick training on the domestic side may not be enough for the success of their expatriates. We need to consider the relevance and importance of virtual management. Virtual Management consists of the daily operation of both the virtual team and workflow. While virtual teams are created, there is no guarantee that all employees will be productive, trustworthy, and/or technologically savvy. Thus, this leads to the key question of this paper in terms of how organization can show an interest in their human capital, help them realize their potential, and continue to show their interest. In particular, one way that learning organizations can help show their interest with their global employees (expatriates) is offering additional training or mentoring to help keep them “connected” rather than “disconnected” from the organization. How does an organization create a virtual mentoring process? Just like any type of training program, it should be done in phases (National Services Knowledge Network, 2012). First, there needs to be a needs assessment. We need to find out if there is a need for mentoring program? If so, could there be a possible use for developing a virtual mentoring program to help not only virtual employees, but also fulfil the developmental needs of expatriates? While some companies may think that a mentoring program may be a solution, it may not be a need, but rather than a want.

Bob Barrett Basically, this university realized the need for mentors to assist with new online instructor. Rather than relay on a simple mentor‐mentee informal relationship, the academic staff realized the need for the development of a more detailed and formal mentoring relationship. Thus, the design and development of any training or mentoring program should be carefully planned and organized. The key is to consider the learning objectives and limitations of the program in terms of technology, people, and training. While a mentoring program may be an idea solution in one sense, the mentors may need some training prior to the implementation of the program. For example, while a company may have many good employees to serve as mentors, they may not be using certain types of technology. As a result, the university developed a three‐week training program for all mentors to learn about the university’s expectations, policies and procedures, and how to complete all necessary paper on the mentoring processing. This was not a just simple workshop, but rather all members of the mentoring workshop had to be active and contributing participants in order to be considered for future mentoring assignments (National Mentoring Partnership, 2012). According to the Expatcanada.org (2012), “Canadian Expats and former Canadian expats selected for this Program learn mentoring approaches and skills, and gain experience mentoring other Canadian expats, either those preparing to leave Canada, those recently relocated from Canada, or those preparing to return to Canada or who have recently returned. Mentors lend the benefit of their expat and repatriation experiences to their mentees and provide them with encouragement and social support. Those completing this Program will learn new listening techniques, and new motivation and persuasion practices. They will learn to adopt the best approach matched to the mentee’s circumstances and personality and so more quickly build an effective relationship with the mentee” (para. 1). Considering the Canadian approach here, they value the work experiences of their expatriates to help prepare and guide their current and future expatriates. Rather than hiring external training resources or consultants, they realize the value of this segment of their human capital. Turtenwald (2012) further adds that mentoring for expatriates is not a one‐time deal. Rather, they may need on‐going mentoring during their overseas assignment and before and after they return back to their home location.

7. Conclusion While this university has focused on the needs analysis prior to developing a mentoring program, it has seen a need to be sure to focus on what is needed in a mentoring program – rather than just offering a generic mentoring program. Mentoring has been an art and skill used in many different types of organizations. However, the approach used by various organizations can range from a simple and quick form of mentorship or to a well‐organized and developed program/process that requires the input of more than just the mentor and mentee. The key point to consider here is whether or not an organization values its human capital and its relevance and importance to the overall organization and its goals. Nonaka (1986) wrote “Self‐renewal of an organization can be seen as a process of dissolving an existing organizational order and creating a new one. Order in an organization refers to the structural and cognitive order which affects the pattern of the members of the organizational activities, namely, the pattern of deployment, organizational structure, systems, processes, and cultures. There can be no self‐renewal without dissolution and creation of order” (p. 3). While technology has been ever changing, companies and organizations have realized that they need to offer more than just company training for all employees. In fact, it may be necessary to offer more training, but on an informal basis in the form of mentoring. As a result of the impact of online learning, changing technology, and the use of overseas and virtual locations, more companies and organizations are realizing the relevance and importance of virtual mentoring (National Education Institute, 2012).

References Argyris, C. (1990). Overcoming organizational defense. Prentice‐Hall, New York. In P. Senge (1990). The fifth discipline: The art & practice of the learning organization. Doubleday: New York. Clark, S. & Mirabile, R. (2004). “Knowledge mapping: An application model for organizations.” In M. Goldsmith, H. Morgan & A.J., Ogg (2004). Leading organizational learning: Harnessing the power of knowledge. Jossey‐ Bass: San Francisco. Daniel, B., Schwier, R., & McCalla, G. (2003). Social capital in virtual learning communities and distributed communities of practice. Canadian Journal of Learning and Technology, 29(3), 113‐139. Expatcanada.org (2012). Retrieved November 1, 2012, http://www.expatcanada.org/index. php?option=com_content&view=article&id=48&lang=en. Intellectual Capital: Tomorrow’s Asset and Today’s Challenge. http://www.cpavision.org/vision/ wpaper05b.cfm. Retrieved November 5, 2009. As cited in T.A. Stewart, (1997). Intellectual Capital. New York: Doubleday Currency. pp. 62‐63.

Bob Barrett Koprowski, G. (2000). Online Learning: The competitive edge. IW‐Information Week. Retrieved 7/10/12 http://www.informationweek.com/801/prlearn.htm. Lave, J., & Wenger, E. (1998). Communities of Practice: Learning, Meaning, and Identity: Cambridge University Press Mintzberg, H., Ahlstrand, B. & Lampel, J. (1998). Strategy safari: A guided tour through the wilds of strategic management. Free Press, NY. Mitleton‐Kelly, E. (2003). What are the characteristics of a learning organization? Retrieved on October 3, 2011 from http://www.gemi.org/metricsnavigator/eag/What%20are%20the%20 Characteristics%20of%20a%20Learning%20Organization.pdf. National Education Institute (2012). Peer mentoring and coaching online. Retrieved December 12, 2012 from http://www.teachereducation.com/courses/online/peer‐mentoring.html. National Mentoring Partnership (2012). How to build a successful mentoring program: Using the elements of effective practice. Retrieved Dec. 12, 2012 from http://www.mentoring.org/ downloads/mentoring_418.pdf. National Service Knowledge Network (2012). Creating a successful online mentoring program. Retrieved Dec. 12, 2012 from http://www.nationalserviceresources.org/practices/17573. Morgan, G. (1986). Images Organization. Sage: Newbury Park. Nonaka, I. (1986). “Destruction and creation or organizational orders: A suggested paradigm self‐organization.” Organizational Science (20)(1). Retrieved Aug. 2, 2012 from http://www.ai.wu.ac.at/ ~kaiser/birgit/Nonaka‐ Papers/Creating‐Organizational‐order‐out‐of‐chaos.Self‐renewal‐2001.pdf. Senge, P. et. al. (1994) The Fifth Discipline Fieldbook: Strategies and Tools for Building a Learning Organization. SCORE (2012). Mentoring. Retrieved Dec. 12, 2012 from http://www.score.org/mentors. Sokolewska, O. (2006). ELearning w szkoleniu kadr – raport z badań. Zarządzanie zasobami ludzkimi, 3‐4 (48‐49) 100‐108. Turtenwald, K. (2012). Effective steps for mentoring expatriates. Retrieved Oct. 20, 2012 from http://www.ehow.com/info_8040217_effective‐steps‐mentoring‐expatriates.html.

The Impact of Intangibles on Value Creation: Comparative Analysis of the Gu&Lev Methodology for the United States Software and Hardware Sector Leonardo Basso, Herbert Kimura, Juliana Saliba¹ and Erica Braune¹ Mackenzie Persbyterian University, São Paulo, Brazil leonardobasso@mackenzie.br herbert.kimura@gmail.com julianasaliba@hotmail.com ericabraune@hotmail.com Abstract: This article compared the proposal for measuring intangibles of Gu&Lev for the sectors of software (classified in services) and equipment and technology for computing (classified in industry) in the United States. The idea of comparing the sectors arose from the discovery in two previous articles of a discrepancy in the results mainly for the indices proposed by Gu&Lev to measure intangibility and their impact on value creation. The database used was Thomson‐Reuters collected in Datastream, with information covering the period from 2001 to 2010. Gu&Lev (2003, 2011) present a proposal that aims to calculate a variable, comprehensive value, which encompasses the tangible and intangible assets of the company and are therefore a proxy for their market value. If this variable explains the market value, it is a solution to a problem that afflicts accountants, which is how to account for intangibles in the balance sheet. They also propose two other variables, one that is a proxy for the flow of intangibles (Intangibles‐Driven‐Earnings‐ IDE), and another that is a proxy for the stock of intangibles (Intangible Capital ‐IC). They present a set of hypotheses that relate traditional variables linked to intangibility (research and development expenditures ‐ RD, selling, general and administrative expenses ‐ SGA, and investment in fixed capital ‐ CAPEX) with the flow (IDE), stock of intangibles (IC) and intangibility indicators that explain the shareholder return. We observed differences between the sectors, reinforcing the conviction that the sectors are important to explain differences in the intangibility indices. The results indicated for the software sector that: (i) the explanatory variables expenditures on research and development (RD) and selling, general and administrative (SGA) were statistically significant in explaining the flow of intangibles (IDE) and the stock of intangibles (IC), but the strong correlation between the explanatory variables may have changed the RD signal, that (ii) the flow of intangibles (IDE) and earnings (EARN) represented by EBITDA showed no statistical significance in its relation to the return to shareholder (TSR), that (iii) the Comprehensive value presented positive and significant relationship with the market value of the company and (iv) the rate of intangibility ICBV and RI had significant explanatory power on the variable and shareholder return. The comparison with the hardware sector enabled us to verify that there are no repeated results. Since the results for the hardware sector indicate that: (i) the variables RD, SGA and CAPEX were significant in explaining the IDE, however, the variables RD and CAPEX showed negative signs which contradicts what was expected, since the variables RD, SGA and CAPEX were not statistically significant in explaining the IC, that (ii) IDE and EARN were not statistically significant compared to the TSR and that (iii) the Comprehensive value had a significant relationship with market value company and (iv) the rate of MtCV, ICM and RI had explanatory power in relation to shareholder return. Keywords: intangible assets, value creation, total shareholder return, intangibility indices

1. Introduction Intangibles are being studied by various areas of knowledge. Bontis (2002) observed that concern about the topic is present in economics, sociology, psychology, administration (information technology, human resource administration, management research). Andriessen (2004), supported by the work of Bontis (2002) and Bontis et. al. (1999), selected five important schools of thought for the study of intangibles. The intellectual capital community is interested in the definition and measurement of intellectual capital, one of the forms of intangibles. Andriessen (2004) brought up 12 methodologies that seek to provide a response to the problems of definition and measurement (Pike and Roos, 2000; Brooking, 1996; Roos et. al, 1997; M'Pherson and Pike, 2001; Sveiby, 1997; Viedma, 2001; Bounfour, 2002; Mouritsen et al, 2001; Sveiby et. al, 1989; Edvinsson and Malone, 1997; Sulivan, 1998a,b,c; Pulic2000,a,b). The accounting community is interested in the accounting of intangibles in the financial statements, on the basis that traditional financial accounting does not present a satisfactory response for the market value of companies that is very different from the value expressed in traditional financial statements (Stewart, 1997; Hall et al, 2001; Gu and Lev, 2003; Standfield, 2001; Stewart, 1997; Lev, 2001). Andriessen (2004) brought up seven methodologies that develop studies along this line of research. The performance measurement community incorporated the concept of intangibles to lend greater credibility to the focal points of performance measurement and according to Andriessen there are 2 methodologies that work with this concern (Kaplan and Norton, 1992; 1996a,b; 2001; Stewart III, 1994). The

Leonardo Basso et al. valuation community, arising from financial studies, seeks to improve measurements (from the perspectives of the discounted cash flow and real options) of the highly uncertain values that originate from intangibles. Andriessen (2004) verified three methodological focuses that work along this line of research (Dixit and Pindyck, 1998; Khoury, 1998; Reilly and Schweihs, 1999). The human resources community, with a representative in the survey conducted by Andriessen (2004), seeks to reactivate human resources accounting techniques that developed in the 1960s and 1970s (Sackmann et. al, 1989). Gu&Lev are representatives of the accounting area, as they are interested in approximating the book values of a company to the market value. From this point of view they are close to the line of thought of normative accounting, which is concerned about establishing rules for the accounting of intangibles (Córcoles, 2010; Sedlacek and Konecny; 2010; Epstein and Jermakowicz, 2009).

2. Theoretical background Gu and Lev (2003; 2011) base their proposal on an enlarged production function. In the simple production function, the factors that are included to achieve levels of production are capital and labor. The enlarged production function considers intangibles as a production factor, as expressed in the equation: Economic Performance = α * (Physical Assets) + β * (Financial Assets) + γ * (Intangible Assets) This equation expresses the fact that value creation (measured by the value added, i.e., the sum of profits and salaries in a simple economy) can be explained by the contributions of labor and capital. The empirical observation that these two factors alone did not explain the production of an economy led economists to include a third factor in the equation. This factor encompasses what it is not the contribution of capital and of labor and is generally designated intangibles. Where α, β and γ represent respectively the contributions of physical assets, of financial assets and of intangible assets. The algebraic manipulation of the equation shows that the value of intangibles can be obtained by subtracting the economic performance from the normal returns of the physical and financial assets. The result is the contribution of intangible assets, designated “Intangibles‐Driven‐Earnings”, IDEs. Five steps are necessary to calculate the contribution of intangibles. a) Calculation of the normalized economic performance. The normalized earnings (we will use EBITDA, earnings before interest, taxes, depreciation and amortization as a Proxy for economic performance) as it represents the company’s gross value creation (in other words, before any deduction, and before any distribution to the stakeholders). To compose this variable the author recommends the use of past and prospective earnings. The reason is simple: intangibles increasingly act on the generation of future earnings. It is recommended to use the same number of years for the past and the future (3 to 5), whereas the higher weights should be reserved for the future. b) The second step consists of calculating the physical and financial assets. Physical assets are defined as property, plant and equipment, (GU and LEV, 2003). Financial assets are defined as cash on hand, shares, and financial instruments, (GU and LEV, 2003). To calculate the contributions of physical and financial asset the author uses data already available in the economic literature. The rate of return of 7% for physical assets was based on the studies by Nadiri and Kim (1996) and Poterba (1997). For the financial assets, the rate of 4.5% was based on the 10‐year average return of the US treasury, (JUERGEN 2001). The values of the physical and financial assets should be restated using appropriate discount rates for restated values.

Leonardo Basso et al. c) The third step consists of the estimation of IDEs. To obtain this amount the contribution of financial (β) and physical (α) assets, multiplied by the respective values of the physical and financial assets, are then subtracted from the company’s Estimated Economic Performance. The result of this subtraction is the contribution of the intangible assets, which is defined by the authors as IDE (Intangibles‐ Driven‐Earnings). d) The fourth step consists of the calculation of prospective IDEs for three future periods (Gu and Lev, 2003). The first period is composed of years 1 to 5, calculated in the previous stages. The second period covers years 6 to 10 and the projection of the IDEs is based on applying a linear growth (or decay) rate to the IDE obtained in year five, until the growth rate reaches 3%. The third period extends infinitely from year 11, and it is assumed that the IDE will grow annually at a rate of 3%, which is the growth rate expected from the economy. e) The fifth step consists of determining the stock of intangible capital obtained by the deduction of the prospective IDEs using a rate that reflects the degree of risk of the IDEs; as they are a product of intangibles, the rate needs to be above average. Gu and Lev (2003) are not very precise in their explanation of the determination of this rate. We used a rate of 7.5% in this paper. Gu and Lev (2003) also defined the comprehensive value of companies, which encompasses the tangible and intangible part, aiming to correct the differences observed in the book value of these companies. Comprehensive value is defined as the sum of the book value and of the intangible capital explained previously. As a result, Gu and Lev (2003‐05), formulated a series of new company performance appraisal indices, based on public information: Intangible Capital Margin (ICM): (Intangible Capital / Sales); Intangible Capital Operating Margin (ICOM): (IDE / Operating Income ‐EBIT); Comprehensive Value (CV): (Intangible Capital + Book Value); Return on Investment of R&D: (RI) ‐ (Intangible Capital / Investments in R&D); Market‐to‐Comprehensive Value (MtCV): (Market Value / Intangible Capital). Values close to 1.00 indicate the importance of the intangibles and the closeness of this indicator to the company’s market value; Intangible Capital to Book Value (ICBV): (Intangible Capital / Book Value). This will indicate to what extent the company or sector analyzed is based on intangible assets. These indicators were transformed into hypotheses by means of an association with shareholder value creation, represented by the total shareholder return.

3. Methodology We used the econometric methodology denominated panel data, which analyzes all the companies (cross section) in various periods of time (time period) to conduct the tests. This study presents two novelties in relation to the articles of Gu&Lev (2003; 2011). The first is that we expanded the hypotheses in relation to the articles that we used as a reference. A first block of hypotheses is similar to the set of hypotheses tested by Gu&Lev (2003; 2011). The traditional hypotheses are: Hypothesis I and II: The higher the investment in research and development (RD), capital expenditures (CAPEX) and selling, general and administrative expenses (SGA), the higher the degree of intangibility (IDE) of the company and the higher the intangible capital (IC) of the company. Hypothesis III: The higher the degree of intangibility (IDE), the variation in the degree of intangibility, the operational performance (EARN) and the variation in the operational performance (EARN), the higher the total shareholder return (TSR). Hypothesis IV: The higher the Comprehensive Value (CV) the higher the company’s market value (MV). We consider the fourth hypothesis the most relevant of the studies by Gu&Lev, since if it is corroborated we can obtain an approximation for the market value of companies, particularly for unquoted ones. For the estimates to be more accurate, we need to expand the studies for all the sectors, as the values of the angular coefficients may be different for sectors with a high degree of intangibility in relation to those with a low degree.

Leonardo Basso et al. The second block of hypotheses (that represented the innovation of this study) consisted of testing the model of Gu and Lev (2003) for the intangibility indices proposed by the authors with value creation represented by total shareholder return. Gu and Lev created a series of indicators to measure intangibility; since intangible resources increasingly represent the largest portion responsible for value creation, a general hypothesis can be constructed: The higher the intangibility (measured by an appropriate indicator) the higher the value creation. Hypothesis V: The higher the Intangible Capital Margin the higher the shareholder return. Hypothesis VI: The higher the Intangibles Driven Earnings Margin the higher the shareholder return. Hypothesis VII: The higher the Intangible Capital Operating Margin the higher the shareholder return. Hypothesis VIII: The higher the intangible capital to book value the higher the shareholder return. Hypothesis IX: The higher the market to comprehensive value, the higher the shareholder return. Hypothesis X: The higher the return on the investment in research and development, the higher the shareholder return.

4. Results and discussion We decided to compare two sectors relevant to the US economy: that of software and that of hardware. The first presented 856 companies in the base and the second 551. After eliminating companies that did not present data for all the years, we arrived at 792 observations for the software sector and 591 for the hardware sector. The number of observations drops steeply when we test the alternative hypotheses (incorporating the intangibility indicators) because many companies do not present all the necessary data. The variables were collected directly in Datastream.

4.1 Correlation matrix Interesting results are observed. In the software sector for the traditional hypotheses (models 1, 2 and 3 already tested by Gu&Lev) we observed high correlation between the independent variables: (i) model 1, RD with CAPEX correlation of 0.81, (ii) model 2, RD with CAPEX correlation of 0.81, (iii) model 3, IDE with EARN of 0.99 which entails the phenomenon of multicollinearity between the independent variables, which may give rise to a change of signal of the explanatory variable. Model four, which if corroborated presents an important contribution by Gu&Lev (2003;2011) to the study of intangibles, as it proposes a Proxy for the market value of companies, relates the comprehensive value to the total shareholder return (presenting a positive correlation of 0.94). The correlations of models 6 to 10 are related to the innovation of this study (testing of the intangibility indices proposed by Lev (1999) and Gu and Lev (2003;2011)). The dependent variable, total shareholder return (TSR), in general, presents low linear correlation with the independent variables: ICM (0.016), IDEM (0.038), ICOM (‐ 0.13), ICBV (0.024), MtCV (0.34) and RI (‐0.04). Due to the low correlations it would not be surprising if the new hypotheses of the intangibility indicators were not corroborated. In the hardware sector for the traditional hypotheses (models 1, 2 and 3 already tested by Gu&Lev) we observe correlations that diverge from the correlations found for the software sector: (i) model 1, RD with CAPEX correlation of 0.45 and a high correlation of RD with SGA (0.91); there is also a high correlation between IDE and SGA (0.86) indicating a high explanatory power for this variable, (ii) model 2, the correlation of RD with CAPEX (0.45) is much lower than that found in the software sector, (iii) model 3, IDE with EARN of 0.957, which entails the phenomenon of multicollinearity between and among the independent variables which could bring about a change of signal of the explanatory variable. Model 4 presented high correlation (0.714) between MV and CV (comprehensive value), increasing our expectations regarding the explanatory power of CV as a Proxy for intangibility. The correlations of models 6 to 10 are related to the innovation of this study (testing of the intangibility indices). The dependent variable, total shareholder return (TSR), in general, presents low linear correlation

Leonardo Basso et al. with the independent variables: ICM (0.15), IDEM (‐0.023), ICOM (0.052), ICBV (0.093), MtCV (0.02) and RI (0.22). Judging by the low correlations it would not be surprising if the new hypotheses of the intangibility indicators were not corroborated.

4.2 Analysis of the hypotheses Static panel data analyses were carried out for all the models. The results of the regressions associated with the ten hypotheses of this study (regressions for the two sectors) are presented in tables 1 and 2. It is possible to observe in the tables the results of the angular regression coefficients, as well as the values of R² within, R² between, R² overall, the F‐ and/or Chi‐squared test statistic for general validity of the model, the value of the F, Breush‐Pagan and Hausman tests (which allows us to decide between the pooled data grouping models, fixed or random effects models), the value of the Breusch‐Pagan/ Cook‐Wesberg test statistic that allows us to observe the existence of heterocedasticity and the Wooldridge test statistic to verify the existence of autocorrelation (Hsiao, 2003). To analyze the fixed effects models (with a significant result for the presence of heterocedasticity in the Breusch‐Pagan/ Cook‐Wesberg test), we used the model with robust variance according to the Newey‐West estimator that corrects the effects of the presence of heterocedasticity. First we analyzed the software sector (Table 1 and 2); model 1 presented as proxies for intangibility both RD and SGA. However the strong correlation between the explanatory variables may have changed the RD signal. The same thing happens with model 2, where the two explanatory variables presented statistical significance to explain the stock of intangibles, but there may have been a change of signal of the RD variable. Model 3 did not present significance for any of the explanatory variables. Model 4, which we consider the most important, as it proposes a methodology to approximate the book value to the market value, presented statistical significance at 1%. We consider the result promising if it is corroborated for other sectors. Table 1: Results for software sector: Model 1 to model 5 Variable Dependent Constant RD CAPEX SGA IDE ΔIDE EARN ΔEARN CV ICM N.B. F-test Breush-Pagan Hausman R² within R² between R² overall F model Heterocedasticity Autocorrelation

Model 1 (*) IDE -9453,26 -1,54 0,32 1,19

* *

Model 2 IC -315499 -25,58 -6,13 17,69

Model 3 TSR 0,26

Model 4 (*) MV * 4370366 *

Model 5 TSR 0,218

***

* * 11100000 -25900000 -11600000 826000000 0,053

792 16,36 344,7 810,53 0,66 0,85 0,84 28,86 3500,78 43,28

* * *

792 340,21 1607,54 141,45 0,09 0,24 0,24 20,83 4,41 2676,89

* * *

* ** *

63 0,51 1,39 0,31 0,01 0,1 0,02 1,4 3,89 17,68

*** *** ***

** *

1025 87,46 1961,74 14,44 0,01 0,3 0,29 0,86 16,77 16,24

* * *

* *

0,0018 63 0,83 0,99 2,99 0,056 0,0033 0,0003 0,02 4,48 14,23

*** *** ***

** *

(*) Robust Fixed Effect – According to Newey W est estimator N.B.: (*) statistically significant at the level of 1%, (**)statistically significant at the level of 5%, (***)statistically significant at the level of 10%.

As concerns the intangibility indices the only indicators that presented explanatory power (at 10%, however) were ICBV and RI, a disappointing result. It was to be expected that ICBV would present explanatory power, since the stock of intangible capital (as well as the flow ‐ model 4) had explanatory power over the market value (it is expected that the higher the market value, the higher the total shareholder return). The same applies to the variable RI, obtained from the division of the stock of intangibles by the research and development expenditures. The stock of intangibles explains the market value (model 4) and the RD

Leonardo Basso et al. expenditures exhibit high correlation with IDE. We did not manage to find a justification for the lack of statistical significance of the other intangibility indices (ICM, IDEM, ICOM; MtCV). The hardware sector (Table 3 and 4) did not present results similar to those of the software sector. As concerns the traditional hypotheses (models 1 to 4 of those proposed by Gu&Lev) model 1 presented as proxies for RD, CAPEX and SGA. However, two of the variables (RD and CAPEX) presented negative signals, which is unexpected. The correlation between RD and CAPEX is not high; the strong correlation between SGA and RD may have changed the RD signal. Models 2 and 3 did not present significance for any of the explanatory variables. As mentioned previously, the result of model 4 is the most relevant, and the result is similar to that of the software sector; the comprehensive value explains the market value. Table 2: Results for software sector: Model 6 to model 10 Variable Dependent Constant IDEM ICOM ICBV MtCV RI N.B. F-test Breush-Pagan Hausman R² within R² between R² overall F model Heterocedasticity Autocorrelation

Model 6 TSR -9453,26 -1,54 0,32 1,19

63 0,73 0,94 1,7 0,035 0,0002 0,0015 0,09 3,2 22,01

Model 7 TSR -315499 -25,58 -6,13 17,69

* *

*** *** ***

*** *

63 0,68 0,66 1,19 0,04 0,08 0,01 1,11 0,16 18,89

Model 8 TSR 0,26

Modelo9 (*) TSR * 4370366 *

Model 10 TSR 0,218

63 0,76 2,15 4,3 0,15 0,34 0,12 1,71 37,87 25,78

58 0,85 1,14 3,15 0,06 0,03 0,0026 0,85 0,69 15,68

***

* * 11100000 -25900000 58 0,91 0,69 3,37 0,066 0,006 0,0006 3,12 2,28 12,43

*** *** ***

*** *

*** *** **

*** *** *

*** *** **

* *

*** *** ***

*** *

(*) Robust Fixed Effect – According to Newey West estimator N.B.: (*) statistically significant at the level of 1%, (**)statistically significant at the level of 5%, (***)statistically significant at the level of 10%.

Table 3: Results for the hardware sector: Model 1 to model 5 Variable Dependent Constant RD CAPEX SGA IDE ΔIDE EARN ΔEARN CV ICM N.B. F-test Breush-Pagan Hausman R² within R² between R² overall F model Heterocedasticity Autocorrelation

Model 1 (*) IDE 403015 -1,92 -0,13 1,03

* * * *

Model 2 IC 28400000000 4482,08 -180,48 -874,33

Model 3 TSR 0,418

Model 4 (*) MV -3566083

0,656

Model 5 TSR -0,11

15100000 -0,0000003 -0,00000017 0,00000012

615 10,73 337,89 270,61 0,17 0,36 0,35 10,73 2190,49 23,24

* * *

* *

615 338,93 1259,39 0,22 0,002 0,0006 0,0006 0,34 35,74 177000000

* * ***

* *

141 2,53 2,9 0,46 0,0186 0,0125 0,0118 2,25 5,53 18,01

* *** ***

* *

1218 38,56 1866,22 190,93 0,379 0,533 0,511 6,37 12946,54 13,59

* * *

0,054 132 0,83 1,44 3,73 0,05 0,01 0,02 5,99 0,19 7,45

(*) Robust Fixed Effect – According to Newey West estimator N.B.: (*) statistically significant at the level of 1%, (**)statistically significant at the level of 5%, (***)statistically significant at the level of 10%.

** *** *** **

** *** *

Leonardo Basso et al. Table 4: Results for the hardware sector: Model 6 to model 10 Variable Dependent Constant IDEM ICOM ICBV MtCV RI N.B. F-test Breush-Pagan Hausman R² within R² between R² overall F model Heterocedasticity Autocorrelation

Model 6 TSR 0,228 -0,061

Model 7 TSR 0,196

Model 8 TSR 0,172

Model 9 (*) TSR -0,43

Model 10 TSR ** 0,107

0,138 0,0059 1,032 132 0,68 1,52 0,17 0,0007 0,014 0,0005 0,07 16,8 12,42

*** *** ***

* *

132 0,67 1,74 2,18 0,0015 0,234 0,0028 0,36 0,65 13,96

*** *** ***

*** *

132 0,75 1,62 0,16 0,007 0,016 0,008 1,13 1,32 13,27

*** *** ***

*** *

132 1,33 1,23 17,7 0,12 0,11 0,0004 15,04 0,46 50,41

*** *** *

* *** *

0,0016 132 0,57 2,71 1,04 0,029 0,319 0,052 7,24 1,88 14,37

* *** *** ***

* *** *

(*) Robust Fixed Effect – According to Newey West estimator N.B.: (*) statistically significant at the level of 1%, (**)statistically significant at the level of 5%, (***)statistically significant at the level of 10%.

As concerns the intangibility indices the results diverge from those found for the software sector. The only indicators that presented explanatory power at 1% were MtCV, ICM and RI, a disappointing result. We did not manage to find a justification for the lack of statistical significance of the other intangibility indices (IDEM, ICOM; ICBV). The comparison with the software sector indicated that the intangibility indicators are not repeated when we carry out a sectoral analysis. We believe that the result of model 4 is the most relevant, as if it is corroborated for more sectors and countries it will lend considerable credibility to the methodological proposal of Gu&Lev (2011) who claim to have a solution for approximating the book values (found in financial statements) to the market value of a company. This calls for a broader study, involving all the sectors to assess the impact of the angular coefficients at the sectorial level, as well as at the level of size.

5. Conclusions This article compared the proposal for measuring intangibles of Gu&Lev for the sectors of software (classified in services) and equipment and technology for computing (classified in industry) in the United States. We observed differences between the sectors, reinforcing the conviction that the sectors are important to explain differences in the intangibility indices. For the software sector the model presented research and development expenditures (RD) and selling, general and administrative expenses (SGA) as proxies for intangibility. However the strong correlation between and among the explanatory variables may have changed the RD signal. The same thing happens with the model where the two explanatory variables presented statistical significance to explain the stock of intangibles, but there may have been a change of signal of the RD variable. The model that sought to explain the contribution of the flow and of the stock of intangibles in the determination of the total shareholder return did not present significance for any of the explanatory variables. We believe that the result of the model that explains the market value by the sum of stocks of tangibles and intangibles is more relevant, as if it is corroborated for more sectors and countries it will lend considerable credibility to the methodological proposal of Gu&Lev (2011), who claim to have a solution for approximating the book values (found in financial statements) to the market value of a company. This calls for a broader study, involving all the sectors to assess the impact of the angular coefficients at the sectoral level, as well as at the level of size. The comparison with the hardware sector enabled us to verify that there are no repeated results. The hypothesis test for the hardware sector showed that as far as traditional hypotheses are concerned (models 1 to 4 of those proposed by Gu&Lev) the model presented research and development expenditures (RD), capital expenditures (CAPEX) and selling, general and administrative expenses (SGA) as proxies for intangibility. However, two of the variables (RD and CAPEX) presented negative signals, which was unexpected. The correlation between RD and CAPEX is not high; the strong correlation between SGA and RD may have changed

Leonardo Basso et al. the RD signal. The other two models did not present significance for any of the explanatory variables. As mentioned previously, the result of the model that relates the sum of intangibles and tangibles to the company’s market value is the most relevant and the result, similar to that of the software sector, is promising, as we achieved statistical significance. As concerns the intangibility indices, the results diverge from those found for the software sector. Like every study that seeks to corroborate hypotheses using econometric models, this study presents some limitations. The first concerns the sample, as we selected companies available in the two sectors analyzed (nonrandom sample). The second concerns the variable chosen to represent value creation, which is total shareholder return; in future studies we need to consider other variables such as profitability, Tobin’s Q and the price to book (Carton & Hofer, 2006). Another limitation is due to the use of static panels that do not capture the effect of lagged variables. Two other limitations are due to the arbitrary choice of weightings and the arbitrary choice for the contribution of the physical and financial assets. For the calculation of economic performance, we arbitrarily assign weights to the annual EBITDAs. The contributions of the physical and financial assets for calculation of the Intangibles‐Driven‐Earning (IDE) were sought in the existing literature and may not reflect changes in the structural conditions of the economy. The discount rate used to calculate the Intangible Capital (IC) was also arbitrary. To verify whether the results remained the same, we varied the discount rate of the idiosyncratic risk in the range of 7.5% to 15% and verified that the results were the same. Thus the methodology appears promising for the theoretical line of thought that seeks models to record the value of intangibles, particularly for unquoted companies.

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The Existence and Disclosure of Intangibles versus Corporate Financial Performance in France Leonardo Fernando Cruz Basso1, Evelyn Seligmann‐Feitosa2, Diógenes Bido1 and Herbert Kimura1 1 Universidade Presbiteriana Mackenzie, São Paulo, Brazil 2 Universidade Federal do Piauí, Teresina, Brazil leonardobasso@mackenzie.br evsf@globo.com dbido@mackenzie.com herbert.kimura@gmail.com Abstract: Aiming for superior performance, companies need to have and skillfully use rare, valuable, irreplaceable and inimitable resources, with special emphasis on intangibles, usually constructed in lengthy and risky processes or strategically accumulated via Mergers and Acquisitions (M&A), an important strategic alternative for obtaining and accelerating acquisition of these resources. To analyze how financial performance, after 36 months of M&A is related to the previous existence / disclosure of intangibles, we investigated one hundred and seventy‐seven companies were in fifty‐ nine cases of M&A occurred in France among 1997 and 2007. We built textual‐based indicators of disclosure, by applying the content analysis technique to financial and accounting reports provided by the companies prior to the events. We also use financial measures, as proxies for the existence of intangibles, to compare their explanatory power for growth and corporate profitability, the analytical dimensions for financial performance. Using Structural Equations Modeling, via Partial Least Squares (SEM‐PLS), we find positive relationships among these indicators. Best results were achieved by models developed with variables of semantic origin, in comparison to those with financial indicators only. The results obtained lead to the conclusion that the strategic choice for business growth via M&A transactions favors the acquisition of intangible assets by companies in search of improved performance, validating the strategic option for the M&A. Keywords: intangibles; disclosure of intangibles; indicators of disclosure of intangibles on a textual basis; financial indicators of existence of intangibles; mergers and acquisitions; financial performance

1. Introduction The allocation of resources has been a constant target of research. Corporate competitiveness has been the subject of several studies to understand the sources of a sustainable competitive advantage, to identify explanatory factors of certain firms’ superior performance, with the development of analysis structures. The scenario’s profound and constant transformations, potentiated by the advancement of technology, expansion in the services sector, growth and sophistication of markets and creation of wealth from information (Nonaka,1991), increasingly shifted the analytic focus towards intangible assets (Bounfour, 2003). Two approaches stand out in the reflection on the evolution of corporate strategy (Lavie, 2006): the structural view of the industry, in which above‐average returns arise from the firm’s participation in a sector with favorable structural characteristics; and the Resource‐Based View – RBV (for core concepts, see Barney (1986, 1991, 2001) and Peteraf (1993), among others), which suggests that the superior performance of a firm is essentially derived from its heterogeneity and from the specificity of its resources, where the company’s ability to accumulate rare, valuable, irreplaceable resources and capacities that are hard to imitate leads this firm to a competitive advantage over its rivals. It is adopted here, given its influence for the study of intangibles. In the RBV, the "strategic factor market" (Barney, 1986, p.1231) acquires fundamental importance to obtain high returns, directly proportional to its future revenue generation capacity, including in the context of acquisition of other firms. Therefore, M&A are seen as competitive movements for retention or acquisition of strategic resources, especially intangibles, and as a way of shortening the time lag necessary for its accumulation, as an alternative to internal growth. This factor has been attractive for finance theoreticians, especially with regard to the results for the shareholders. “The OECD economies have reached an inflection point: from now on, growth dynamics and value creation rest, especially, on immaterial elements” (Levy and Jouyet, 2006, p. 10). The role performed by intangible resources for the establishment of a sustainable competitive advantage is stressed in the discussion of several authors. The growing interest shown by experts, the variety of opinions issued and the extent of discussions

Leonardo Fernando Cruz Basso et al. indicate that the intangibles represent a vast field of research. As general objective, we analyze how firm performance (in its ramifications of profitability and of growth), resulting from M&A transactions after a minimum period of 36 months from the event, is related to the existence, the disclosure and to the nature of intangible assets at the organizations involved.

2. Theoretical background 2.1 Intangible assets Authors are unanimous about the lack of consensus with regards to the very concept of intangibles and of their constituent constructs, given the variety of constructs and also to the most appropriate measurement or valuation method. Frederick (2009); Kristandl (2006); Levy and Jouyet (2006); Yardimcioglu (2008), e.g. geared towards the elucidation of the aspects and variables that contribute most to the generation of wealth have highlighted the intangible aspects of organizations, which have been considered a source of sustainable and lasting competitive advantage for the party that holds them (brand or business reputation) and puts them to work in its favor (intellectual capital). There is a tendency to use the expressions 'intangibles', 'intangible assets' and 'intellectual capital', and others, as synonyms. OECD (2007, p. 7) acknowledges that “they refer to the same reality: a non‐physical asset with a potential flow of future benefits”. As a “product of the active debate” (Holland, 2001, p. 7), there were several attempts to categorize and define components, various taxonomies, with the frequent adoption of a tripartite categorization. Andriessen (2005) highlight that classifications overlap and the importance of the synergy among the types of intangible resource. At the same time, there is growing interest in the enhancement of the disclosure and transparency of information of firms. A lot of studies evidence the important role of financial reports in providing information to the stakeholders on the value of a company, but, “the evidence suggests there is a significant lacuna of reports related to intangible resources” (Zambon et al., 2003, p.2). The attempts to measure and to assess the value of intangibles in surveys (on their existence and/or their disclosure) are incalculable and diversified, focusing on different dependent and independent variables, and on different levels of aggregation. Respecting the European Commission’s taxonomy (OECD, 2006, p.10), seeking inspiration in Andriessen (2004) and in Yardimcioglu (2008), we classify ‘intangible assets' into three categories: Human Capital, Relational Capital, and Structural Capital. 2.1.1 Existence of intangibles – financial proxies Andriessen (2004) lists and discusses 25 techniques for the valuation of intangibles and Sveiby (2001‐updated 2009) lists 34 different methodologies, aiming to establish one that will achieve widespread recognition. OECD (2006; 2007) and others agencies have made an effort to establish a form of standardization that is acceptable to the majority. To assess the value of intangibles, researchers e.g. Chung and Pruitt (1994) and Lock Lee, Guthrie and Gallery (2009), use financial metrics as a general indicator of global intangibility or presence of intangibles in the organization. Tobin’s q is renowned for being one of the most widely used indices in studies of the kind, despite its limitations, pointed by Andriessen (2004). The use of a global indicator, however, does not allow us to envisage the true source of value creation, requiring an evaluation of the nature of intangibles present at the company (Wyatt, 2002). Among others, Low (2000) modeled a value creation index, highlighted different weights for the factors, according to the type of economic activity, and in Europe, it was been developed a methodology called “Baromètre du Immatériel”, that organizes the measurement, comparison and evolution of 10 fundamental assets, 71 analysis criteria and 175 measurement indicators for the studied firms (L’Observatoire of l'Immatériel, 2009). Based on the academic literature (Table 1), we used numerical proxies derived from financial accounting reports of the companies that made up the sample group, which were tested by type.

Leonardo Fernando Cruz Basso et al. 2.1.2 Disclosure of intangibles ‐ textual indicators Researchers need to be capable of justifying the specific research methods that they use to collect empirical data, which is examined in order to provide support and to test opinions in relation to different management approaches and the intellectual capital report (Guthrie et al., 2004). Accordingly, Bounfour (2003), Holland (2001), and Zambon et al. (2003), examined the disclosure of intangibles by firms from several European countries. Wyatt (2002) shows the insights resulting from the Australian experience in structuring of financial reports on intangibles. Among the methods available for researchers to be able to examine and understand intellectual capital, the content analysis of annual reports of companies is the most widely used tool (Guthrie et al., 2004). A list of words, with adaptations, was validated and used in this study. Intangible asset disclosure indicators were built for the various natures of these assets, through the application of this technique (Bardin, 2007) to the financial accounting reports of the firms studied, prior to the M&A. The word was the chosen unit of analysis; there was the automated detection and the attribution of relative importance to words in the categories of intangible assets, used as context units. The methodological precautions were applied in the various phases of the study. The indicators built are in Table 1.

2.2 Corporate financial performance Lock Lee, Guthrie and Gallery (2009) linked corporate capital and its components to firm performance, at 155 companies in the information technology sector. They revealed that human capital is the best predictor of corporate performance. Others theorists consider that corporate performance is more likely to be typified as a multidimensional construct and proposed models (Brito and Vasconcelos, 2005). Carton and Holfer (2006) studied the conceptualization and measurement of corporate financial and economic performance and maintain that this is a multidimensional construct; it should be analyzed and evaluated from more than one perspective and at different moments in time. The company can be typified as "a temporal economic chain", that convert corporate tangible and intangible assets into tangible results, Kronmeyer and Kliemann (2005, p. 7) demonstrated that investments in human, technological and organizational capital will result through the chain in satisfied shareholders after 3 years. Here we analyzed financial performance of firms resulting from M&A operations, with at least three years after the event. The study covered more than one perspective, as recommended: Profitability and Growth. The analytical dimensions of the corporate financial performance construct were measured through financial indices (Table 1).

3. Methodology The cornerstone was the adequacy and validity of the use of publicly available information. Underlying the investigation, as predominant assumption, was the existence of a positive relation between the retention, use and disclosure of intangible assets by the firms involved in M&A and the superior corporate performance, translated into favorable financial and accounting indices. We detail the central hypothesis (H0), the sub‐ hypotheses relating to the Existing Intangible Assets and Financial performance constructs (H1a and H1b) and referring to the Disclosure of Intangible Assets and Financial performance constructs(H2a and H2b): H0: The Profitability and the Growth of the company resulting from an M&A, after the minimum interval of 36 months subsequent to the event, are related to the level of intangibility of the companies involved and to the disclosure by these companies of their intangible assets. H1a: The Existence of intangible assets is positively related to Profitability. H1b: The Existence of intangible assets is positively related to Growth. H2a: The Disclosure of the intangible assets is positively related to Profitability. H2b: The Disclosure of the intangible assets is positively related to Growth. The sample consisted of M&A processes between 1997 and 2007, listed on the website of Autorité des Marchés Financiers‐AMF, the monetary entity in the French market. An investigation was conducted with 118 companies, in 59 cases of M&A occurred in France in the period, in a multi‐method, pluralistic, qualitative and quantitative research.

Leonardo Fernando Cruz Basso et al. The time interval was chosen based on convenience and judgment, as it is a recent period of greater economic stability with availability of reliable data, and encompasses the possible results of the events in the desired timeframe (Kronmeyer and Kliemann, 2005), with an impact on financial performance (Carton and Holfer, 2006). Table 1 summarizes the main variables used. To carry out the survey, we employed correlation, factor and multiple regression analyses, besides structural equation modeling, with the use of partial least squares‐SEM‐PLS. The main findings are summarized in Results.

4. Results and discussion The separate treatment of the chosen dimensions of the construct representing Financial Performance (Profitability and Growth), was tested and confirmed. The variable “Profitability” was subdivided, by factor analysis, into: Profitability for Shareholders and Corporate Profitability. Growth, in turn, remained a single construct, observed through the indicators Growth of Assets and Growth of Sales of the companies resulting from the M&A examined. Profitability for Shareholders was evaluated by the Rate of Return on Invested Capital (ROEi); and Corporate Profitability, measured jointly by the indicators Rate of Return on Assets (ROAi), Rate of Operating Return on Assets (R_Op_Ai) and Operating Margin (M_Opi). Table 1: Survey variables

INDEPENDENT (numerical proxies for existence of intangibles)

Return on assets‐ROAi – Return on equity‐ROEi Operating return on assets – R_Op_Ai –Operating Margin – MOpi

Profitabilit yi

Variation in gross sales‐Cresc_Vendasi – Variation in total assets‐Cresc_Ativosi

Growthi

[ Carton and Holfer (2006), Kronmeyer and Kliemann (2005) ] Number of employees [ Edvinsson and Malone (1997), Liebowitz and Suen (2000), Gandia (2003), Herman and Kauranen (2005), Huang and Wang (2008), Wang (2008), Liu, Tseng and Yen (2009) ] Sales per employee [ Liebowitz and Suen (2000), Stewart (2001), Koka and Prescott (2002), Tsan (2002), Wu (2003), Chen (2004), Huang and Wang (2008) ] Net income per employee [ Brennan and Connell (2000), Dzinkowski (2000), Tsan (2002), Wang (2008), Huang and Wang (2008), Liu, Tseng and Yen (2009)]

Hum an Capital (CH_A_ prox and CH_c_ prox)

Operating income per employees [ Lacroix and Zambon (2002), Huang and Wang (2008) ] Personnel Expenses Intensity [ Lacroix and Zambon (2002) ] Growth rate of sales [ ASTD (1999), Buren (1999), Brennan and Connell (2000), Dzinkowski (2000), Tsan (2002), Marr and Adams (2004), Huang and Wang (2008), Wang (2008), Liu, Tseng and Yen (2009)] Firm longevity [Florin, Lubatkin and Schulze (2003), Herman and Kauranen (2005), Huang and Wang (2008) ]

Structural Capital (CE_A_prox and CE_c_ prox)

Financial performan ce (post M&A firm) DES_F&Ai

DEPENDENT

Variables observed and [ Theoretical basis ]

Indicators of the existence of intangible assets (acquiring and acquired companies) (quantitativ e basis – accounting proxies) At_Int_A_ prox and

Intangible assets involved in the M and A operation AT_ INT_ F&A

Constructs (Latent Variables) 3rd st nd 1 order 2 order ord er

Leonardo Fernando Cruz Basso et al.

Variables observed and [ Theoretical basis ]

Constructs (Latent Variables) 3rd st 2nd order 1 order ord er

Dilution of shareholding structure [ Cerbioni and Parbonetti (2006), Schadewitz and Blevins (1998) ] Growth Rate of Operating Income ‐ Marketing exp. per share [ Huang and Wang (2008)] Net income per share [Huang and Wang (2008), Wang (2008) ] Net income/sales [ Koka and Prescott (2002) ] Selling and administr. expenses / employee [ Edvinsson and Malone (1997), Roos et al. (1997), ASTD (1999), van Buren (1999), Stewart (2001), Tsan (2002), Wang (2008)]

Degree of stability of the company in the period studied [ Huang and Wang (2008), Herman and Kauranen (2005) ] Relational Capital (CR_ proxy) R and D expenses/net income [ Huang and Wang (2008), Gandia (2003), Lacroix and Zambon (2002) ]

R and D expenses / share ‐ Assets/share ‐ Selling and administr. expenses/sales [Wang (2008) ]

Human Capital (CH_text)

Intellectual Property – R & D and Innovation ‐ Process management ‐ Information systems and networking

Structural Capital (CE_text)

Brands, Reputation, Image and Corp. social responsibility ‐ Customers ‐ Financial relationsand Governance‐ Partnerships in the productive chain networking

Relational Capital (CR_text)

Indicators of disclosure of intangible assets (acquiring and acquired firms) (textual basis, Content analysis) (At_Int)

Intangible assets of the M&A AT_ INT_ F&A

INDEPENDENT (textual proxies for disclosure)

Human factor ‐ Corporate culture ‐ Competence

[OECD (2006), Guthrie et al. (2004), Yardimcioglu (2008) ] Note: the suffix i is an allusion to the M&A case and j relates to each company. Source: based on Wang (2008) and on Barros Jr. et al., (2010).

Leonardo Fernando Cruz Basso et al. Regression analyses carried out resulted in five models, all with 5% of statistical significance. They indicate that:

Growth of the companies resulting from the M&A is related to variables relating to the Relational Capital: Growth Rate of Sales and Firm; longevity, Marketing Expenses per share and Dilution of the Shareholding Structure (acquiring companies) and Previous profitability (acquired company).

Corporate Profitability of the resulting firms are influenced by Previous experience in M&A (acquired firms).

Besides the models established by multiple regression analysis, diagrams of relationships among the examined constructs were built using SEM‐PLS. SEM, particularly through the use of PLS, is a powerful statistical technique for obtaining better and more reliable results, even with samples of reduced size, allowing a more accurate analysis of the relations between the variables studied (Grace and Bollen, 2005). We developed seven structural models, in three different types:

Type 1 – composed only of variables of textual nature and referring to the influence of the constructs in allusion to the Disclosure of Intangible Assets (by the merged companies) on Corporate Profitability and post‐M&A Growth;

Type 2 – prepared only with variables of a financial nature, numerical proxies, in two versions, they relate the constructs referring to the Existing Intangible Assets at the acquiring and acquired companies, prior to the M&A, in relation to the same dependent variables; and

Type 3 – hybrid models, formed by textual and numerical variables, in four versions, gathering the impacts of both indicia of intangibility (disclosed and existing at the firms), to verify their relationship with the dependent variables.

The main results of the structural models built using SEM‐PLS are in Table 2. Table 2: Synopsis of the significant results of the Structural Models built Structu ral model 1 2

Dependent VL Corporate Profitability Growth Corporate Profitability Corporate Profitability Growth

Corporate Profitability Growth

Corporate Profitability

Structural relation between Independent VL regressor Acronym (Constructs) Order AT_INT_F&A_text (Disclosure of Intangible Assets)

3rd

At_Int_A_proxy (Existing Intangible Assets) At_Int_A_proxy (Existing Intangible Assets) At_Int_c_proxy‐direto (Existing Intangible Assets) (1) AT_INT_F&A (Existing Intangible Assets and Disclosure of Intangible Assets) Ind_At_Intg_F&A (1) (Existing Intangible Assets and Disclosure of Intangible Assets) Indictors_ AT_INT_F&A (1) (Existing Intangible Assets and Disclosure of Intangible Assets) Indictors_At_Int_Exist_F&A (Existing Intangible Assets)

2nd

Nature Semantic Financial

2nd

Explanator 2 y power R

Significance of the structural coefficients

7.2%

Sig. at 1%

3.8%

Sig. at 5%

4.4%

Sig. at 5%

4.6%

Sig. at 5%

5.2%

Sig. at 5%

6.8%

Sig. at 1%

3.5%

Sig. at 10%

7.9%

Sig. at 1%

7.1%

Sig. at 5%

4.5%

Sig. at 10%

Financial 1st 4th

1st 1st 1st

Semantic and Financial Semantic and Financial Semantic and Financial Financial

Notes: 1. Constructs created from the relaxation of the limits of factor loadings, AVE and CC, permitted in exploratory surveys (Hulland, 1999, p.198). 2. Models developed in the SmartPLS 2.0.M3 software (Ringle et al., 2005). 3. All the measurement models appear with validity and reliability according to parameters recommended by the good SEM‐PLS technique and with significant coefficients. 4. The significance of the coefficients, in all the models, was estimated, in the software, by bootstrap with 1000 repetitions. Source: Data from the research. In examining Table 2, this aspects merit special emphasis:

Leonardo Fernando Cruz Basso et al.

The dependent VL Corporate Profitability had its variance explained, with statistical significance, in the seven structural models developed, three of them at 1%;

In all the models, the dependent variables, Corporate Profitability and Growth, were designed as 1st order VLs;

The dependent VL Corporate Profitability had its variance explained by five different constructs of the independent variables, in the seven models;

In only three of the seven structural models, the variance of the dependent VL Growth was explained with statistical significance, at 5% and at 10%;

Three models concomitantly combined greater explanatory power and better significance level (at 1%), all referring to the VL Corporate Profitability, where Structural Model 1was based on semantic indicators and Structural Models 3 and 3A were of a mixed data nature (textual and financial origin);

The models that used constructs consisting entirely of financial proxies exhibited lower explanatory power and significance level at 5%, with intermediate complexity level.

The structural models are analyzed and discussed separately and the two most significant ones are represented graphically here, via software SmartPLS 2.0.M3 (Ringle et al., 2005): Structural Model 1 ‐ Intangible assets disclosed x Profitability and Growth rd nd st It was the second most complex, with one 3 order VL, two 2 order VLs and six 1 order VLs, adding up to 11 constructs, two of which refer to the dependent variables. The indicators manifested in this configuration, are of semantic origin, “textual”, relating to the disclosure of intangible assets. For better visualization, see the model in Figure 1.

Figure 1: Structural model 1: Intangible assets disclosed x profitability and growth Note: Only the coefficient of the variable M_Op appeared non‐significant; the other coefficients appear highly significant (p < 0.01); the structural coefficient of the VL Profitability appeared highly significant (p < 0.01) and the VL Growth was significant at 5%. Source: Data from the research. Structural Model 2 ‐ Existing Intangible Assets x Profitability and Growth

Leonardo Fernando Cruz Basso et al. It refers to the existence of intangibles at the merged organizations and it is composed of proxies, respecting the types of intangible capital assets and grouping them in 2nd order VLs. In this model it was not possible to form a variable gathering the financial indicators of the acquiring and acquired companies. With statistical significance at 5%, the intangible assets existing at the acquiring companies account for 4.4 % of the variance of Corporate Profitability; the other structural or path coefficients appeared non‐significant, according to a bootstrap estimation with 1000 and with 5000 repetitions, via SmartPLS 2.0.M3 (Ringle et al., 2005). Considering the above result, and aiming to obtain better and more reliable results, we conceived an alternative design, Structural Model 2A: Intangible Assets Existing at the Acquiring and Acquired Companies x Profitability and Growth, composed of the 2nd order VL representing the Intangible Assets Existing at the Acquiring companies and of the 1st order VL Intangible Assets Existing at the acquired companies. In relation to the foregoing model, Structural Model 2A offered greater explanatory power, with statistical significance at 5%, over the VLs Corporate Profitability (4.6%) and Growth (5.2%). Bear in mind that for the acquired firm, the intangible assets were composed directly, without respecting the category of intangible capital, since in including them, the model undergoes reduction of the already very low predictive power, especially over the dimension growth. Structural Model 3 ‐ Intangible Assets in M&As x Profitability and Growth th rd It was prepared with a higher level of complexity and abstraction, involving one 4 order VL, two 3 order VLs, nd st four 2 order VLs and twelve 1 order VLs, adding up to 19 constructs, two of which refer to the dependent variables. This model can be seen in Figure 2. This configuration uses manifest variables originating from data of a semantic and financial nature, in an exploratory manner, that is: using relaxation of the limits of factor loadings, AVE and CC, with a theoretical basis on Hulland (1999, p.198). All the variables present in this configuration were gathered in the 4th order, which succeeding in forming significant relationships with the Corporate Profitability construct (it can be held accountable for 6.8% of the variance of this VL, with statistical significance at 1%) and with the Growth construct (explaining 3.5% of the variance of the VL, with statistical significance at 10%). Aiming to obtain the highest content validity, respecting the categories of capital of the intangible assets and covering them all, both in the “textual” variables relating to the Intangible Assets Disclosed (of the acquiring and acquired firms) and in the “numerical” financial proxies referring to the Existing Intangible Assets (of the acquiring companies only), we developed Structural Model 3A: Indicia of Intangibility prior to the M&A x Profitability and Growth. Thus we obtained the highest explanatory percentage (7.9%) on Corporate Profitability, with a significance level of 1%. Considering the exploratory nature of Structural Model 3A, textual variables with minimum load of 0.4 were allowed (Hulland,1999, p.198) and, although the minimum parameters of AVE and CC were met, this is considered a methodologically weaker model. To minimize this issue, variables with loads below 0.5 were discarded, arriving at Structural Model 3B: Indicia of Intangible Assets in M&A x Profitability and Growth. This presents lower content validity, as it does not encompass all the categories of capital of intangible assets, in the “textual” variables. The results show explanatory power of 7.1% only over the variance of the Corporate Profitability construct, at 5% of statistical significance. Finally, only the indicators with loads above 0.7 were taken into account again in Structural Model 3C: Indicia of Existing Intangible Assets in the M&A, originating from the Acquiring Company x Corporate Profitability. In this model, only the variables of the proxy type observed in allusion to the existence of Intangible Assets at the acquiring company remained, forming the 1st order construct, which accounts for 4.5% of the variance of Corporate Profitability, with path coefficient significant at 10%.

5. Conclusions We find statistically significant positive relations among the main constructs examined in our study. We considered validated: the central hypothesis H0 specified here (tested through Structural Model 3 ‐ Intangible Assets in M&As x Profitability and Growth); the sub‐hypotheses in allusion to the relation between the constructs Existing Intangible Assets and Financial Performance (tested through Structural Model 2A

Leonardo Fernando Cruz Basso et al. (alternative configuration): Intangible Assets Existing at the Acquiring and acquired companies x Profitability and Growth); and the sub‐hypotheses in allusion to the relation between the constructs Disclosure of Intangible Assets and Financial performance (tested through Structural Model 1: Disclosed Intangible Assets x Profitability and Growth).

Path coefficient between Independent VL Dependent VL AT_INT_F&A Corporate Profitability AT_INT_F&A Growth

t‐value (by bootstrap) 3.102 1.803

p‐value

Significance

0.002 0.072

Sig. at 1% Sig. at 10%

Figure 2: Structural model 3 ‐ Intangible assets in m&as x profitability and growth Note: All the coefficients of the manifest variables and factors in the measurement models appear significant (p < 0.01). The path coefficient referring to the Profitability construct was highly significant (p < 0.01). That in allusion to the Growth construct proved significant at 10%. Source: Data from the research.

6. Conclusions ‐ continuation Comparing Structural Model 1 with Structural Model 3, the two that offered the best and most comprehensive results in terms of explanatory power and significance, we perceive the superiority of Structural Model 1, which offers a lesser degree of complexity, with methodological robustness. Using this model, we find evidence that disclosed intangible assets are related to both profitability and growth. The analysis via SEM‐PLS yielded better results in terms of explanatory power of the dependent variables and of the level of statistical significance, when using models built with manifest indicators of semantic origin (created by the content analysis technique, applied on the documents published by the companies involved in the M&A operations studied), in comparison to the results obtained from the models that used only financial indicators. The results suggest that the information of a textual nature (semantic), converted into intangible asset disclosure indicators, have greater relationship, simultaneously, with corporate profitability and with growth,

Leonardo Fernando Cruz Basso et al. the two dependent variables, in comparison to information based on proxy independent variables from a financial and accounting perspective. Due to the use of an intentional sample, results cannot be generalized, representing a strong restriction on this study, notwithstanding the other types of generalization, from the phenomenological perspective, following inductive logic. For further studies, we suggest more extensive research on the intangible assets that imply relational capital of merged companies. The authors also suggest, as natural ramifications: the use models of rd th 3 and 4 order formative constructs, with the actual variables from the database analyzed; the incorporation of intervening or moderating variables into the modeling.

Acknowledgements MACK‐pesquisa and CAPES (financial cooperation). For any suggestions or notes regarding this work: evsf@globo.com.

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The Influence of the Process of Measuring IC on Performance Donley Carrington Department of Management Studies, University of the West Indies, Cave Hill Campus, St. Michael, Barbados donley.carrington@cavehill.uwi.edu Abstract: This paper explores the relevance and importance of measurement of IC and its impact on performance in the hospitality industry in the Caribbean. A survey using a 7‐point Likert scale was administered to hotel managers to test the relationship between measurement of IC and performance controlling for hotel size and rating of the hotel. The data analysis techniques used were exploratory factor analysis (EFA) and hierarchical multiple regression. The results of the EFA revealed unidimensionality for the measurement of IC and performance variables. The analysis of hierarchical multiple regressions allowed us to support the hypothesis that measurement of IC explained a significant additional proportion of variance in performance above and beyond that of number of hotel rooms and hotel ranking. This finding of a significant and positive relationship between measurement of IC and performance has relevance to both IC academics and practitioners, as it validates the use of measurement of IC to enhance performance. This paper adds to the extant literature on IC within the Caribbean and the hospitality industry both under‐research areas in terms of IC. Keywords: Caribbean, intellectual capital, hospitality industry, measurement of IC

1. Introduction Since the 1990s, the concept of intellectual capital (IC) has received attention from academic researchers, practitioners, businesses and governments. More specifically, the benefit of intellectual capital to organizations has received significant attention, though no common method for its valuation has been determined. The transition of economies from industrial base to knowledge base was the catalyst for the search for greater understanding of the intangible drivers for this new economy. Whereas, many corporate leaders understand the physical and financial assets of the organization and how to effectively manage them, they are less knowledgeable about the components of intellectual capital. In today’s knowledge‐based economy it has been argued that intellectual capital (IC) is the major driver of performance in an organization and that IC can be leveraged to create and sustain a competitive advantage. An area that has attracted significant empirical research is measurement of IC with a plethora of literature being published in support of methods for measuring intellectual capital (Sveiby 1997, Dumay 2009, Ramirez 2010, Pucar 2012). Sveiby (2005) identified 34 such measurement techniques and Andreissen (2004) identified 24. Although there have been several published articles outlining the merits and demerits of the various methods for measuring IC, there has been no agreement on an acceptable measurement system. An early assessment of IC measurement models by Pike and Ross (2004) concluded that none of the methods used to measure IC was compliant with measurement theory. They argue that some of the methodologies provide useful guidance for managers, but the failure to agree on terminology and defining attributes impacts on the measurement characteristics of distinctiveness, agreeability and independence. Despite the lack of a consensus on a model for the measurement of IC it has been empirically tested that IC impacts performance. Whereas the extant literature has documented the relationship among the components of IC and performance very little empirical work has examined the impact of the process of measuring IC on performance. Therefore the aim of this paper is to report on an empirical investigation of the relationship between the process of measuring IC and performance in the Caribbean hospitality industry.

2. Literature review Intellectual capital research initially focused on defining IC and its components, however, to date there is still a lack of consensus on an agreed taxonomy of IC. Several authors have argued that intellectual capital represents the resources of an organisation that have been formalized, captured and leveraged to create assets of a higher value (Bontis 1999, Sveiby 1997). A three factor conceptual framework has been created to further our understanding of IC, by deconstructing IC into human capital, relational capital and structural capital. Human capital, a multi‐dimensional construct, is not a physical asset of the organisation measured by the number of employees but it relates to employees’ education, skills, training, experience, attitudes about life

Donley Carrington and business, genetic inheritance and values (Edvinsson and Malone 1997; Litschker et al., 2006). Relational capital is described as either relationships existing between employees and external economic actors (Stewart, 1997), or relationships existing among employees and other departments within the organisation (Tsai and Ghoshal 1998). Structural capital is the supportive infrastructure that enables human capital to function and it includes all the non‐human storehouses of knowledge in organizations. Structural capital encapsulates databases, organisational charts, process manuals, strategies, routines, legal parameters, patents, trademarks, research and development and anything whose value to the company is higher than its tangible value (Bontis, 1999; Roos et al. 1997). Measurement of IC has also attracted a significant amount of the initial research. Sveiby (2005) suggests four categories of intellectual capital measurement techniques; direct intellectual capital methods, scorecard methods, market capitalization methods and return on assets methods. Prior research by Luthy (1998) had categorized them into two; the methods that relate to component‐by‐component evaluation and methods that measure the value of the composite of intellectual assets in financial terms. Sudarsanam et al (2005) similarly divided them into; models that estimate the aggregate value of intellectual capital at a point in time, and models that value the investment in intangibles each at a time. It is apparent from the categorization of the models that some models are geared towards the management accounting function while others the financial accounting function. These intellectual capital measurement techniques evolved as a result of IC becoming the constrained resource in this intangible economy where people have become the critical asset in organisations (Boudreau and Ramstad 1997). These measurement techniques sought to reveal value linkages and provide managers with continuous leading indicators to determine how IC was being improved by organizations activities and how such improvements led to organisational success. However, a number of these measurement schemes have been criticized for not meeting the science based measurement criteria of completeness, independence, distinctness, agreeability and scaling (Pike and Roos 2004, Roos 2005). Boudreau and Ramstad (1997) argued that the failure of measurement systems was due to the tendency of framing these measures too much in terms of financial measurement systems and not enough in terms of their ultimate purpose. Chahabaghi and Cripps (2006) purport that the potential of IC will not be realized if management continues to force thinking about IC into the existing frameworks based on accounting and management control. This thinking is not only advocated for practitioners, but for academic researchers as well, so that a deeper understanding of how IC works and how IC is utilised in organisational change can be developed (Mouritsen, 2005). Measurement is the core of accounting and without an understanding of what is measured and how it is measured proper comprehension of accounting is totally impossible (Ijiri 1967). Salterio (1998) contends that measurement is not only about the measurement assignment process, which he terms, the factual level but also incorporates a purposive level, which deals with the relevance of measures. In taking this two‐tier approach to measurement as advocated by Salterio (1998) in relation to IC, consideration should be given to the behavioural characteristics of the users as well as the process of assigning numerical values (or qualitative descriptors) to intellectual capital attributes. This notion of a behavioural role of measurement supports Flamholtz (1980) thesis. Flamholtz (1980) asserted that the principle purpose of measurement in organisations is to influence the behaviour of people, their perceptions, motivation, decisions and actions. He argued that prior attempts to examine measurement have focused extensively upon the system’s output and have not explicitly examined the nature and functions of the process or act of accounting measurement. This dual role of measurement when applied to IC enables the classification of the measurement techniques into internal focus measures and external focus measures (Carrington and Tayles 2011). Therefore those measures design with an internal focus will address the issues raised about behavioural changes within the organisation, whereas those with an external focus requires that the properties outlined in the scientific approach to measurement must be adhered to. The measurement of IC from an internal focus and the resulting behavioural implication of such have not received much attention in the literature pertaining to the measurement of IC. On the other hand, research pertaining to the scientific approach to measurement which is quite appropriate for those measures that have an external focus, has attracted a fleeting glance in the literature. While the current IC studies have provided a foundation that has successfully furthered our understanding of IC, some theoretical tensions remain concerning the synergistic, dynamic and contextual nature of IC. This gap

Donley Carrington in our knowledge is one of the areas that need to be filled in order for a clearer understanding of IC. So, the aim of this paper is not to answer the question of “What are the organisation's measures of IC?” but rather to answer what is the impact of measurement of IC on the performance of the entity. Therefore to contribute to the literature on IC, two basic research questions were framed for this study:

Do hotel managers recognize the importance of measurement of IC components and its impact on their operational decisions?

Is there a relationship between the measurement of IC and performance?

3. Methodology A quantitative approach, using a 7‐point Likert scale questionnaire as the data collection method, was used to assess the impact of the process of measurement of IC on performance while controlling for size and rating of hotels in the Caribbean. The independent variable, measurement of IC, assessed the extent to which managers engage in collecting, analyzing and reporting data relating to the components of IC. Thirteen items drawing on the work of Moon and Kym (2006), Sveiby (1997), Stewart (1997), Bontis (1998), Kaplan and Norton (1992), Brander Brown and McDonnell (1995), Salterio (1998) and Flamboltz (1980) were used for this construct. The dependent variable was a composite scale of items relating to managers’ perception of changes in performance of financial and non‐financial measures. This variable was assessed by 14 items, guided by the work of Brander Brown and McDonnell (1995), Fitzgerald and Moon (1996) and Bontis (1998) customized for the hospitality industry. The use of perceived measures of organizational performance is supported by Dess and Robinson (1984) and has been used in other IC studies by Bontis et al, (2000), Khong and Nair (2006), Tayles et al (2007) and Carrington and Tayles (2011). The telephone directories of the fifteen Caribbean territories provided a sampling frame of 1,429 accommodation properties. The sampling frame was reduced to 429 by eliminating properties with less than 40 rooms based on information glean from the websites of the Caribbean Tourism Organisation and the Caribbean Hotel Association. Given the demographics and geography of the Caribbean, a local resident was used as the conduit for the distribution and return of the questionnaires. The initial posting resulted in 46 questionnaires being returned. Follow up processes were implemented which resulted in a 184 questionnaires representing a 38 percent return rate. Non response bias was evaluated using Lambert and Harrington (1990) approach. The t‐tests revealed no significant difference among the twenty survey items tested. These results do not rule out non‐response bias, but suggest that non‐response may not be a problem. The data were entered in SPSSv19 and subjected to univariate analysis to obtain the means, standard deviations, kurtosis and skewness of all variables in the data set. The results of the skewness and kurtosis which were used to assess normality, revealed that only one of the items with a skewness of ‐2.472 and kurtosis of 7.199 was above the threshold advocated by Wess et al (1995) and was deleted. Table 1 provides some descriptive statistics on the hotels included in the survey. Table 1: Descriptive statistics of selected variables Number of rooms Number of employees Occupancy level Revenue per available room

Mean 133.36 155.37 71.41 US$ 208.02

St. Deviation 121.513 176.26 12.62 208.18

Range 40 – 856 20 – 843 34% ‐ 95% $26 ‐ $1,098

Exploratory factor analysis (EFA) was then used to ascertain whether the survey questions loaded on the respective dimensions for measurement of IC and performance. Principal components analysis with a varimax rotation was used to factor analyze the fourteen items relating to performance and thirteen items relating to measurement of IC. Correlation matrices among the 27 items revealed a number of correlations in excess of 0.3 thus patterns in responses to variables are therefore anticipated. An analysis of the anti‐image correlation matrix revealed all elements on the diagonal of this matrix were greater than 0.5. The Bartlett’s test of sphericity, a measure of homogeneity of variables, which test the null hypothesis that the original correlation matrix is an identity matrix (Field 2000), showed an approx. Chi square of 1476.545, with 231 df and significance 0.000. Pallant (2005) posits that this test should be statistically significant at p<0.05. The results indicated that the correlation matrix was suitable for factor analysis. The Kaiser‐Meyer‐Olkin measure of overall sampling adequacy which provides a means to assess the extent to which indicators of a construct belong together was 0.883 which according to Kaiser and Rice (1974) is meritorious. The original non‐rotated

Donley Carrington principal component analysis using SPSS reveals that the smallest eigenvalue which is associated with the 27 factors is 0.093, not dangerously close to zero. In addition, none of the squared multiple correlations exceed 0.9, the largest being 0.7245. This finding would indicate that multicollinearity and singularity are not a threat in this data set. The first run of the data on the 27 items yielded five factors with eigenvalues greater than 1 that explained 69.035 per cent of the variance. In evaluating the number of factors and variables to retain, the factor loadings were taken into account. According to Comrey and Lee (1992) a factor loading of 0.63 is very good with overlapping variance of 40 percent. Using Anderson and Gerbing (1988) restrictive analysis approach the data were rerun specifying two factors and a cut off factor loading of 0.63. The results of this iteration revealed an explained variance of 55.531 percent, nine variables loading on to factor 1 with the minimum factor loading of 0.739 and six variables loading on to factor 2 with the minimum factor loading of 0.659. The variables which did not load onto any factor were subsequently deleted. The third run of data set specifying 2 factors with 15 variables yielded an explained variance of 65.441 percent. However, before the research questions could be answered in the affirmative, tests of internal consistent of each factor was conducted. The test of internal consistency using the reliability coefficient Cronbach’s alpha revealed that measurement of IC with 6 items and performance with 9 items had a Cronbach’s alpha of 0.834 and 0.942 respectively. These results are above the lower limit for Cronbach’s alpha of 0.70 recommended by Hair et al (2006), which suggests an acceptable level of internal consistency and reliability for the factors. Further analysis of the scales by examining the respective item‐total statistics showed that all 15 items have corrected item‐total correlation of above 0.5 and the Cronbach’s alpha would be reduced by removing any item. A single scale for each research variable was then created by averaging a respondent’s scores over the items measuring the variable. The mean for measurement of IC was 5.540 out of a maximum of 7 indicates there was an above average measurement of IC in hotels in the Caribbean. The dependent variable performance has a mean of 5.426 and a standard deviation of 1.02721. The relationship among the critical research variables was assessed using Pearson’s correlation. The analysis revealed that measurement of IC is moderately and positively associated with performance. This association was significant at p<.001. The control variable of number of rooms has a weak but positive significant association with performance while hotel ratings have a weak and negative significant association with performance. Table 2 shows the results of the Pearson’s correlation analysis. Table 2: Pearson’s correlation coefficients (N=184) Variable Number of rooms (#Rooms) Hotel ratings (HR) Measurement of IC (ICM) Performance (HP)

#Rooms ‐0.353 0.213 0.189

HR ‐0.295 ‐0.275

ICM 0.489

The correlation statistics are all significant at p<.001. To examine the impact of measurement of IC on performance a hierarchical multiple regression analysis was performed. Variables that explain performance were entered in two steps. In step 1, performance was the dependent variable and (a) hotel size measured in number of rooms, and (b) rating of hotel (the standard used in the hospitality industry measured by number of stars) were the independent variables. In step 2, measurement of IC was entered into the step 1 equation. Before the hierarchical multiple regression analysis was perform, the independent variables were examined for collinearity.

4. Results and discussion The six items which loaded on the independent variable measurement of IC supports the finding of Atkinson and Brander Brown (2001) who in their empirical study of performance measures in UK hotels reported that customer satisfaction, customer loyalty and market share were measured 89%, 78% and 62% respectively. Additionally, it has been empirically tested that metric of customer satisfaction enhances financial performance (Ittner and Larcker 1998; Yoo and Park, 2007) and increases loyalty which results in improved

Donley Carrington profitability (Bowen and Chen 2001). These items which were used to construct the scales for measurement of IC and performance are show in the table3 below. Table 3: The results of the rotated component matrix Rotated Component Matrixa Component 1 2 P1 RevPar [revenue per available room] .803 P2 Occupancy percentage .808 P3 Growth in profits .833 P4 Labour productivity .846 P5 Sales growth .866 P6 Customer satisfaction .750 P7 Market share .776 P8 After‐tax return on investment .739 P9 Overall performance .885 M5 Measure Customer satisfaction .699 M6 Measure Employee satisfaction .713 M7 Measure Customer complaints .799 M8 Measure Customer retention .827 M9 Measure Employee training .814 M10 Measure Market share .748 Extraction Method: Principal Component Analysis. Rotation Method: Varimax with Kaiser Normalization. a. Rotation converged in 3 iterations.

The research question which relates to the impact of measurement of IC on performance was assessed by the use of hierarchical multiple regression. The results of the variance inflation factor (all less than 2.0) and collinearity tolerance (all greater than 0.85), shown in table 4, suggests that the estimated βs are well established in the following regression model. Table 4: Collinearity statistics from hierarchical multiple regression on performance Independent variable

Collinearity Statistics Tolerance .859 .896 .892

Hotel ranking Number of rooms Measurement of IC

VIF 1.164 1.116 1.121

The results of the step 1 indicated that the variance accounted for as measured by (R2) with the first two independent variables equaled 0.051 (adjusted R2 =0.039) which was significantly different from zero (F = 4.050, p<0.05). In step 2, the measurement of IC was entered into the regression equation. The change in variance accounted for (ΔR2) was equal to 0.101, which was statistically different from zero (F = 8.892, p<0.001). Measurement of IC explained a significant additional proportion of the variation in performance above and beyond that of number of rooms and hotel ranking with a ΔR2 equal to 10.1%. Measurement of IC accounts for 13.5% of variation in performance. Measurement of IC is positively and significantly related to performance (β=0.323, p<0.001). This finding suggests that hotels with a higher level of measurement of IC performed better. Table 5: Results from hierarchical multiple regression on performance Independent variable Hotel ranking Number of rooms Measurement of IC

Coefficient ‐0.096

t‐statistic ‐1.855

0.106 1.365 0.323 5.381 Notes: Adjusted R2 =.135 F‐value = 8.892 (p<0.001)

p‐value 0.066 0.175 0.000

The results of this analysis were used to answer in the affirmative the fundamental question of whether measurement of IC has a significant and positive impact on performance. These results supports Widener (2006) findings that firms that establish a performance measurement system that provide top managers with critical information pertaining to its resources and capability will positively affect their performance. This argument supports an earlier call from Kaplan and Norton (1996 page 21) who asserted that “if you can’t

Donley Carrington measure it, you can’t manage it”. This finding of a significant relationship between measurement of IC and performance corroborates a number of other empirical studies that referred to performance measures. Hoque (2005) found a positive and significant association between managers’ use of non‐financial measures and performance. Olson and Slater (2002) found that overall performance relative to competitors was positively associated with the extent to which organisations match the use of their measures to the four balance scorecard categories. Van der Stede et al., (2006) report that firms that use objective and subjective performance measures increased their perceived performance. Hyvonen's (2007) results indicate that the use of 'contemporary' performance measures leads to higher perceived customer‐ related performance in firms. These studies support the premise that entities that measure their intellectual capital appear to be more beneficial in their overall performance as these intellectual capital measures are important drivers of long‐ term economic success.

5. Conclusion This research sought to examine the impact of measurement IC on performance in hotel within the Caribbean. The analysis of the empirical data indicated the process of measurement impacted significantly on performance. This initial contribution of the research, especially for those firms in the accommodation sector, is the recognition of the benefits of measuring IC, as this study supports earlier findings of the impact of measurement of IC on performance. This brings managers’ attention to the long term benefits of measuring IC, and therefore management within the hospitality sector should develop integrated measurement systems that incorporate IC factors with the financial measures. This study showed the linkage between measurement of IC and performance. However, the survey data relied on perceptual measures of organizational performance. Although objective measures are more desirable, perceptual measures are regularly used in research. While the perceptions of managers on performance was defended as a strength of this study, obvious limitations rest with this approach as it is conceivable that managers did not respond to the performance questions in a truthful fashion. Therefore, additional work is needed to test how closely perceptions of performance correlate with actual performance in this sample. In addition, future researchers might consider defining the individual performance factors more specifically than was done in this study to hone more accurate and specific performance information from respondents. In addition, there is a possibility that the explained variance offered by each independent variable is biased and or inflated because of omission of the impact of tangible resources (Galbreath and Galvin 2004). Therefore it would be interesting to investigate models that incorporate both tangible and intangible factors of production. Finally, data limitations aside, this research is a step to gaining a further understanding of the beneficial impacts of measuring IC on firm performance. It is hoped that other researchers will adopt and improve on this research, to provide the much needed empirical support to the foundational theories for IC. Based on the findings of this study managers in the hospitality industry and scholars should continue to pursue approaches to better understand the process of measuring IC and performance.

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The Distinctiveness of Knowledge Sharing Processes Within Multinational Companies Vincenzo Cavaliere1 and Sara Lombardi21 1 Department of Business Administration, School of Business, University of Florence, Florence, Italy 2 Department of Business and Management, LUISS Guido Carli University, Rome, Italy vincenzo.cavaliere@unifi.it slombardi@luiss.it Abstract: Despite the great interest among academicians in studying knowledge sharing (KS) processes within multinational corporations (MNCs), a deep understanding of the antecedents of such processes is still lacking. In particular, the role played by MNCs’ corporate culture in affecting employees’ orientation toward KS behaviors seems to be unclear. Some authors have focused on the impact of countries’ institutional environments on moving practices within MNCs, while others have argued that KS is more likely to occur when the organizations involved are culturally compatible. Nevertheless, the role played by MNCs’ organizational culture has not received sufficient attention in the management literature yet. To fill this gap, this paper builds on the relevance of knowledge assets in providing competitive advantage in dynamic economies. In order to outperform rivals in today’s high-velocity markets, firms need to put in place appropriate knowledge management activities that are essential to speed learning processes, that is, to be able to rapidly adapt to evolution. In this regard, KS is considered a key process through which employees can gain access to the organizational intellectual capital and contribute to the firm’s innovativeness and competitiveness. KS processes are particularly relevant in MNCs. By operating in more than one country, MNCs provide an effective organizational vehicle by which to transfer knowledge as they are seen as networks that create, access, and share knowledge across national boundaries. Building on prior literature, we attempt to study how KS processes occur within multinational enterprises, by focusing on the antecedents of such processes. In particular, we investigate the impact of both MNCs’ corporate culture—distinguished in four different typologies, that is, innovative, competitive, bureaucratic, and community culture—and the employees’ perceptions of top management support toward two dimensions of KS, that is, knowledge donating and knowledge collecting. To test our hypotheses, we analyze survey data drawn from a sample of 396 employees working in six MNCs operating in Italy. Our findings show that the four types of MNCs’ organizational culture may differently affect KS sub‐processes. This evidence is consistent with the argument that the two KS dimensions have different natures and, as such, may be influenced by different kinds of factors. We also found top management support to strongly and positively affect both KS dimensions. Keywords: knowledge sharing, multinational companies, organizational culture, top management support

1. Introduction Over the last decades, there has been an increasing interest among academicians in studying knowledge management in multinational corporations (MNCs) (Gupta and Govindarajan 2000; Szulanski 1996). The underlying reason is that a multinational enterprise (MNE) is “an international network that creates, accesses, integrates, and applies knowledge in multiple locations” (Almeida et al. 2002: 148). This is consistent with an MNE’s need to rapidly adapt to environmental uncertainty as well as to integrate the activities of its subsidiaries located worldwide. In particular, knowledge integration managed all over the world is what enables MNCs to acquire the “incremental value of being multinational” (Kogut 1989: 383). However, MNCs’ knowledge management processes are far from being perfect, as they are usually inhibited by several factors. Knowledge is “sticky” and its transfer difficult to implement, especially when it occurs across countries, as in the case of MNCs. An important factor affecting MNCs’ capacity to transfer knowledge is their organizational culture (Lucas 2006). Nevertheless, the role played by such variable does not seem to be clear. This paper is an attempt to provide empirical evidence on how MNCs’ culture affects their subunits’ orientation to intra‐organizational knowledge sharing. We aim at contributing to this field of research by analyzing survey data drawn from 396 employees operating in six MNCs located in Italy.

Corresponding Author. The Authors thank the reviewers for their thoughtful and helpful comments which allowed us to improve the paper and its content.

Vincenzo Cavaliere and Sara Lombardi

2. Theoretical background 2.1 The relevance of knowledge sharing The resource‐based view of the firm and the knowledge‐based perspective state that exploitation and management of knowledge assets are crucial to the organization’s survival and prosperity, being key resources that create value for firms (Grant 1996). Nevertheless, knowledge is not just “a flow of messages”; it also includes know‐how, experience, and interpretations. Knowledge assets are thus hard to trade, to codify and to imitate, representing a valuable source of competitive advantage (Barney 1991). Nevertheless, in order to be more beneficial, knowledge has to be disseminated across all firms’ levels as its value increases when it is shared. Knowledge sharing (KS) can be conceived of as a social interaction culture in which employees exchange opinions, skills, and know‐how (Lin 2007), by listening and talking to each other and by providing mutual help to solve problems and develop ideas. Organizations should motivate employees to contribute to KS as individuals are often reluctant to give away their information: they may lose their power and distinctiveness (Gupta and Govindarajan 2000). By definition, KS involves both a source and a recipient, that is, both the supply and the demand for knowledge. Accordingly, we look at two dimensions of KS, i.e. knowledge donating and knowledge collecting. While the former describes the employees’ willingness to communicate with others and transfer their intellectual capital, the latter is the process of consulting with others to learn from their knowledge (Lin 2007). Given the complexity of this research topic as well as the related terminology used in the literature, it is important to distinguish between the terms knowledge transfer and knowledge sharing. Knowledge transfer describes the identical or partial replication of knowledge from one place to another (Lucas 2006; Szulanski 1996); knowledge sharing is more than transferring knowledge: it is about creating it through social interaction (Van den Hooff and Huysman 2009). Therefore, for the purpose of this paper we are interested in studying the knowledge sharing processes, which we conceive as including knowledge transfer activities.

2.2 Knowledge sharing in MNCs Knowledge exchange activities are central to the growth of the firms as firms’ development is strongly contingent upon their ability to create and replicate knowledge in order to expand their market. This is particularly relevant in MNCs, whose daily activity requires them to transfer their technology both within their subsidiaries and across national boundaries. An MNC is “an economic organization that evolves from its national origins to spanning across borders” (Kogut and Zander 1993: 625). Thanks to this process of international expansion, an MNC transfers its technology (i.e., its knowledge), improving its growth and extending its power (Kogut and Zander 1993, 1992; Zander and Kogut 1995). It thus competes on the market based on its information and know‐how superiority as well as its ability to develop and move knowledge from one place to another or from one entity to another. Kogut and Zander (1993) stress that MNCs are effective in transferring knowledge since they exist to internalize markets; by defining them as social communities that efficiently create and transform knowledge “into economically rewarded products and services” the authors point out that knowledge transfer capability is what distinguishes MNCs from domestic firms. Among the streams of literature on KS in MNCs, we focus on the analysis of the antecedents of KS behaviors in MNCs’ subunits. So far, several scholars have brought attention to the factors that may affect MNCs’ KS activities. Teece (1977) found that the costs of transferring knowledge are determined by the age of the technology and the firm’s prior experience in pursuing it; Szulanski (1996) investigated how subsidiaries’ managers’ motivation and knowledge characteristics impede the internal KS; Adler (1995) examined cultural distance between foreign and home country. Nevertheless, few studies have shed light on MNCs’ subunits’ behaviors in KS. In this regard, we believe that, prior to any analysis of KS among MNC’s subsidiaries, among subsidiaries and corporates, as well as among subsidiaries and host country’s local firms, it is essential to look at how KS occurs within the firms comprising an MNC’s network. Thus, before examining whether the whole MNC successfully performs KS, we should investigate the extent to which these processes are pursued within the MNC’s “units.” By looking at MNCs’ KS as involving at least three levels of analysis (i.e., country, organizational, individual; Kostova 1999), we chose to focus on the individual one, in order to come up with empirical evidence that may contribute to the analysis at the other two levels. We thus agree with Mowday

Vincenzo Cavaliere and Sara Lombardi and Sutton (1993) that the study of KS in MNCs should provide a multilevel approach, which, even if challenging, is better able to capture the complexity involved in the examination of such a phenomenon.

2.3 Organizational culture in MNCs As they operate internationally, MNCs must face the complexity of the global environment and carefully manage cultural issues. In particular, in MNCs different kinds of cultures come into play: corporate, subsidiary, home‐country, host‐country’s culture. This paper focuses on corporate culture, by investigating how employees perceive their firm’s corporate culture. Being the organizational culture made by different subcultures (Jermier et al. 1991), we agree that, within MNCs, at a certain point in time there is a dominating subculture over the others, that the subcultures are not mutually exclusive, and that the dominating subculture can change over time (Deshpandé et al. 1993). Organizational culture can be seen as “a system of shared values and norms that define appropriate attitudes and behaviors for organizational members” (O’Reilly and Chatman 1996). It consists of cognitive systems explaining how people think and make decisions (Pettigrew 1979) and governs both the process of external adaptation and that of internal integration, by defining the way in which things are done in the firm (Schein 1985). Every organizational culture is firm‐specific, resulting from a firm’s experiences and history. It also evolves over time, by shaping the firm’s identity. Building on social comparison theory, Hofstede (1991: 170) states that “no part of our lives is exempt from culture's influence”; that is, the value systems of the society we live in shape our behaviors and our view of the world. Following this argument, scholars have showed that organizational performance depends on how employees accept and share their firm’s cultural values (Denison 1990). 2.3.1 Organizational culture and KS Many studies have investigated the role of organizational culture in knowledge management processes (De Long and Fahey 2000), demonstrating that cultures that value creativity and ideas exchange are more likely to support knowledge management initiatives (Gupta and Govindarajan 2000). In MNCs, corporate culture helps in coordinating a dispersed organization by supporting the transfer of information, knowledge, processes, and people (Sørensen 2002). Nevertheless, knowledge creation and sharing mostly depend on employees’ willingness to engage in such processes. Following Hofstede (1991), we argue that, in taking part in the organizational processes, employees will behave according to how they perceive the firm’s culture. Once they internalize the corporate cultural values, they conform their conducts consistently, letting the culture guide their attitudes and behaviors. However, so far few studies have investigated the way in which corporate culture impacts MNCs’ employees’ KS orientation. Many theorized that countries’ institutional environments affect transnational transfer of practices within MNCs (i.e., institutional distance, Kostova 1999), while others suggested that technological transfer is more likely to occur when cultures of the organizations involved are compatible (Kedia and Bhagat 1988). In this paper, we are examine the prevailing culture at the time of the survey according to four different typologies of organizational culture, namely innovative, competitive, bureaucratic, and community culture. Building on Deshpandé et al. (1993), we analyzed them according to two dimensions: the focus either on organic processes (e.g., flexibility, spontaneity) or on mechanistic processes (e.g., planning, scheduling, order, stability) and the emphasis either on the internal work environment (e.g., integration, smoothing activities) or on the external one (e.g., competition, differentiation) (Figure 1). An innovative culture is mainly characterized by the focus on entrepreneurship, creativity, and the need for the firm to find new markets and growth opportunities outside. Employees’ risk orientation and rapid adaptability to evolution are central to this organizational culture. Being innovative means being able to rapidly find new solutions and offer new products by reacting to the dynamism of the market through a high degree of flexibility. Conversely, a competitive culture is mostly associated with organizations that focus on more mechanistic and rational approaches to gain competitive advantages over rivals (e.g., goals scheduling and achievement, task accomplishment) (Campbell and Freeman 1991). Work practices and tasks are usually designed to boost both internal and external competition, by stimulating employees to work long and hard to achieve the firm’s market and financial objectives. When the focus is on procedures and rules, the firm is likely to show a bureaucratic culture, which highlights the need for stability, efficiency, and formalization. Generally, such a culture emphasizes the use of hierarchical tools to coordinate actions and decisions as well as the need for accurate planning activities to facilitate smooth and efficient operations. A community culture emphasizes employees’ cohesiveness,

Vincenzo Cavaliere and Sara Lombardi participation in decision‐making, and work satisfaction rather than mere financial and market share goals (Deshpandé et al. 1993); great attention is paid to human resources as well as their loyalty to the company, trust, and respect for all workers.

Community culture

Innovative culture

Bureaucratic culture

Competitive culture

Focus outside the firm

Focus within the firm

Organic processes

Mechanistic processes

Figure 1: Matrix of organizational culture typologies. Source: adapted from Deshpandé et al. (1993) 2.3.2 Top management support and KS Besides the role of organizational culture in shaping employees’ behaviors and orientations, top management support is a critical means through which values and norms are communicated across all organizational levels. Consistently, perceived top management support is considered as one of the most relevant influences on employees’ willingness to share knowledge with colleagues. Of course, this support should be encouraging rather than coercive (Connelly and Kelloway 2003: 294); that is, employees should receive suggestions and feedback from their superior about what to share and how to do it within the firm, in order to improve their involvement in organizational activities and their motivation to contribute to the firm’s performance. Thus, the creation and maintenance of a strong KS organizational orientation requires the employees to feel supported by top management. Accordingly, prior researches (Lin 2006) have found this kind of support to be essential in building a KS climate.

3. Research 3.1 Hypotheses In order to investigate the role played by MNCs’ corporate culture in shaping employees’ KS behaviors, we developed the following hypotheses. Providing an organization with an innovative culture means valuing and exploiting employees’ creativity, that is, their capacity to generate new solutions and knowledge and to share them. Thus, culturally innovative firms will be those that support social interaction and stimulate employees to mutually exchange opinions and ideas, in order to come up with new solutions. We thus expect that: Hp1: Innovative organizational culture is positively related to employees’ orientation toward both knowledge donating and knowledge collecting. Organizations characterized by a competitive culture are mainly oriented toward goal achievement and strictly planned activities. They are likely to push workers to carefully monitor their own performance and make sure that personal goals are reached. Given this, such a culture tend to limit employees’ willing to donate their knowledge to help colleagues; conversely, employees will rather prefer to collect critical information that support their own purpose, by committing in knowledge collecting activities that are beneficial for them. We therefore offer the following hypothesis: Hp2: Competitive organizational culture is negatively related to employees’ orientation toward knowledge donating and positively related to their orientation toward knowledge collecting.

Vincenzo Cavaliere and Sara Lombardi Firms showing a dominating bureaucratic culture accentuate managers’ authority over lower organizational levels and their focus on scheduling and efficiency. By establishing rules and procedures, such an organizational culture leaves little room for employees’ creativity (Bates et al. 1995) as well as for opportunities for them to interact and exchange ideas. Prior researches (Silverthorne 2004) demonstrated that bureaucratic organizational cultures pose great challenges in maintaining employees’ job satisfaction, which is an important antecedent for KS behaviors (Cabrera et al. 2006). We thus hypothesize that: Hp3: Bureaucratic organizational culture is negatively related to employees’ orientation toward both knowledge donating and knowledge collecting. By valuing human resources and their contribution to the firm, a community culture is likely to create a work environment that supports empowerment activities and employees’ career advance, which improves internal communication by stressing teamwork and socialization opportunities and reducing internal hierarchical barriers. Consequently, we posit that: Hp4: Community organizational culture is positively related to employees’ orientation toward both knowledge donating and knowledge collecting. Several studies demonstrate the importance of an encouraging top management support for knowledge management and KS initiatives (Storey and Barnett 2000; Davenport et al. 1998). As well as the role of organizational culture is to guide employees toward the adoption of specific values and behaviors, management team support is essential in motivating workers in making this adoption successful. In case knowledge leaders engage seriously in transmitting a KS culture across all organizational levels, employees will be more willing to take part in ideas exchange activities with colleagues. The following hypothesis is thus formulated: Hp5: Top management support is positively related to employees’ orientation toward both knowledge donating and knowledge collecting.

3.2 Sample selection and research method Data were gathered from Web surveys administered to subsidiaries of six MNCs operating in Italy, randomly drawn from the population of all MNCs’ subsidiaries located in the Tuscany region. The need to analyze this specific set of firms emerged as part of a broader institutional research project, aiming to understand the distinctive features characterizing these companies, which play an important role in the region’s competitiveness. A draft questionnaire was pilot tested with 53 middle managers of three companies to ensure that content and wording were free of misunderstandings. We then revised the questionnaire and retested it with 45 employees. A meeting with each MNC’s human resource director was carried out to explain the research purpose and identify the sample of workers to be studied. Responses were required from key informants knowledgeable in a variety of strategic activities (Foss et al. 2009); we selected employees that operate at the center of strategic information flows and are thus considered nodes of knowledge. Following prior literature (Cohen and Levinthal 1990), such employees are likely to foster the firm’s absorptive capacity (i.e., learning ability), by translating information into a form that can be better understood by anyone in the firm. This sampling criterion allowed us to survey employees directly involved in KS processes. A total of 776 invitations were sent out for participating in the research and, after two follow‐up reminders, 396 questionnaires were fully filled (response rate of 51%). The average response rate across all MNCs has been 58% (29% the minimum; 84% the maximum).

3.3 Measures Self‐reported measures were used to operationalize all variables in the questionnaire (Spector 1994), which was composed of 27 items, derived from scales adopted in previous studies. All variables were measured using a seven‐point Likert type scale (from 1 = strongly disagree to 7 = strongly agree). Van den Hooff and Van Weenen (2004) provided the scale to measure knowledge donating (three‐item scale) and knowledge collecting (four‐item scale). The respondents were asked to give their opinion about their orientation toward both the voluntary donation of knowledge (e.g., “When I learn something new, I tell my colleagues about it”) and their tendency to ask for it to colleagues (e.g., “Colleagues share their knowledge with me when I ask them to”). The organizational culture scale (16 items) was adapted from Deshpandé et al. (1993). Top

Vincenzo Cavaliere and Sara Lombardi management support was measured through a four‐item scale adapted from Tan and Zhao (2003). To capture the effects of further factors that in prior studies have been found affecting KS, we include the following control variables: firm’s size and age, and employees’ age, gender, education level, and tenure.

3.4 Findings Descriptive statistics, correlation matrix, and Cronbach’s alpha for all variables are reported in Table 1. In Tables 2a and 2b we provide the results of two multiple regression analyses run using Stata and referred to our dependent variables (i.e., knowledge donating and knowledge collecting). Concerning the control variables, while a firm’s size does not show any significant impact on knowledge donating, it results in negatively affecting the process of knowledge collection among employees. Thus, the larger the firm, the less likely knowledge collection is to occur. Moreover, within our sample, older employees are less oriented toward the voluntary donation of information to their colleagues, while no significant relationship emerges for the knowledge collecting process. Finally, individuals with longer tenure engage in collecting information from others less than shorter tenure workers do. Table 1: Correlation matrix and Cronbach’s Alpha for all variables (n = 396)

Cronbach’s coefficients Alpha are shown on the diagonal. *Correlation is significant at the .05 level Table 2a: Results of multiple regression analysis on knowledge donating

By examining the impact of the explanatory variables included in our model, the results show that in case employees perceive organizational culture to be innovative, this stimulates them to donate information within the firm, while no statistical significance has been found for knowledge collection. Hypothesis 1 is thus partially supported. Hypothesis 2 is only partially supported as well, as we did not find any statistical significance for the

Vincenzo Cavaliere and Sara Lombardi impact of competitive culture on employees’ knowledge donating; conversely, our data show that a competitive culture positively supports knowledge collection among colleagues (10% significance level). We did not find empirical evidence for Hypothesis 3, since employees’ perception of an organizational bureaucratic culture results as being even positively related to both knowledge donating and knowledge collecting. A community culture, in turn, is positively related to employees’ willingness to donate knowledge, while it has not been found statistically significant when the knowledge‐collecting dimension is examined. Thus, Hypothesis 4 is partially supported. As expected, the influence of top management support on KS processes (i.e., both knowledge donating and knowledge collecting) is strongly positive (Hypothesis 5 is supported). Table 2b: Results of multiple regression analysis on knowledge collecting

3.5 Discussion Building on the literature about KS processes in MNCs, this research is an attempt to contribute to the understanding of how such companies can establish a successful KS strategy. First, we found evidence about the difficulty in delineating a definite role played by MNCs’ organizational culture toward the sharing of knowledge. The explanatory variables we examined are proven to impact on the two dimensions of KS (i.e., knowledge donating and knowledge collecting) in different ways. This is consistent with many scholars stating that these KS sub‐processes have a different nature and, as such, can be influenced by different factors (Van den Hooff and de Ridder 2004). Second, our results about competitive culture support our argument: the more the organization’s competitive culture, the more the employees will ask colleagues for information. Given the lack of statistical evidence concerning the relationship between such culture and knowledge donating, we cannot state anything about it, but we consider a competitive work environment to be likely to be conducive to the diffusion of individuals’ opportunistic behaviors. Additionally, the positive impact of MNCs’ innovative culture toward KS shows that the more the creativity is stimulated within the firm, the more employees will be motivated to share their ideas with each other, by contributing to enhance KS at both individual and organizational level. Conversely, we did not expect to find MNCs’ bureaucratic culture to be positively related to employees’ knowledge donating. We believe this result could be rooted in Sine et al.’s (2006) arguments, which point out that organizations need a certain degree of formalization and bureaucratization to let information flow across departments. Although our findings did not provide strong evidence about the relationship between community culture and KS, we believe that when organizations are mostly based on teamwork, socialization, and cross‐unit communication, both knowledge donating and collecting are more likely to occur. Finally, as our results demonstrate, we believe that employees’ perception of a strong top management support toward KS behaviors is one of the most important enablers leading to a strong KS culture.

Vincenzo Cavaliere and Sara Lombardi

4. Conclusion The paper provides the first empirical evidence about the relationship between MNCs’ KS organizational enablers (i.e., organizational culture and top management support) and KS processes within a sample of six MNCs’ subsidiaries operating in Italy. Our findings point out several implications for practitioners. They show that MNCs’ managers should be careful in considering the impact of their firms’ culture on KS outcomes. We recommend them to separately consider the two KS dimensions (i.e., knowledge donating and knowledge collecting) in order to better plan and implement targeted interventions to enhance the flows of information within their firm. Furthermore, the analysis of control variables’ impact on KS offers further interesting guidance: given that older employees are found to be less willing to donate their knowledge to colleagues, managers should find a way to stimulate their contribution to the overall KS activities. Since older workers are generally more competent and knowledgeable on specific jobs, it may be important to foster their motivation to help younger and less expert colleagues through their knowledge. Our findings may thus indicate that in larger firms (particularly in MNCs) older employees are not willing to donate their knowledge, as they are aware of the power rooted in it. Unlike small companies, where cooperative behaviors are more likely to occur, MNCs seem to build barriers to KS among older and younger workers. Because of the sampling criterion, our results cannot be easily generalized. First, we only considered MNCs operating in a specific area (i.e., Central Italy). Given the industrial cluster culture traditionally rooted in this territory, we may have controlled for it in order to rule out alternative explanations about KS processes due to such MNCs’ particular location. Moreover, being aware of the role played by national culture in MNCs, further research may improve this study by looking at how corporate national culture may influence KS processes within MNCs. In this regard, Hofstede’s (1991) contribution may prove particularly helpful. Additionally, as the paper did not consider all possible enablers that may be critical for KS, future research could take into account other factors, both at individual level (e.g., demographic determinants) and at organizational one (e.g., leadership style, organizational structure) to improve the understanding of MNCs’ intra‐organizational knowledge exchange. Moreover, as suggested by Wang and Noe’s (2010) review of KS literature, an objective measure of KS should be developed, by collecting third‐party and archival data in order to enrich the more common self‐perceptual assessment of KS activities. We also believe that more qualitative research focused on specific issues could be useful to deepen our comprehension about the findings resulting from this empirical work. A multilevel analysis (Kostova 1999) may be an interesting reason for digging into this topic in the future, in order to look at how KS occurs at the individual (i.e., among colleagues), organizational (i.e., within MNCs’ network), and country level (i.e., between subsidiaries and local firms).

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The Influence of Relational Capital on Product Innovation Performance at Innovative SMEs Pedro Figueroa Dorrego1, Ricardo Costa2 and Carlos Fernández‐Jardon Fernández3 1 Departamento de Organización de Empresas y Marketing, at Facultad de Ciencias Sociales y de la Comunicación, Universidade de Vigo, Spain 2 UNICES (Unidade de Investigação em Ciências Empresariais e Sustentabilidade), at ISMAI, Instituto Superior da Maia, Portugal 3 Departamento de Economia Aplicada, at Facultad de Ciencias Economicas y Empresariales, Universidade de Vigo, Spain figueroa@uvigo.es rvfcosta@gmail.com cjardon@uvigo.es Abstract: The authors build on the intellectual capital and new product development perspectives to study the influence of relational capital on product innovation performance. An empirical research was conducted, using a questionnaire administered to Portuguese innovative SMEs. The results suggest that relational capital does have a positive effect on product innovation performance. In particular, “Vertical relationships” stands out as the main relational capital element significantly affecting product innovation at the innovative SMEs level. The existence and proactive management of relationships with customers and suppliers emerge as critical factors to product innovation success. We find our results to be useful for both researchers and practitioners: at the intellectual capital level, we contribute to the ongoing understanding of relational capital’s impact on critical business activities; at the product innovation level, we contribute to the identification of additional critical success factors for new product development. At a time when intellectual capital and product innovation management are both considered to be critical for companies to gain a competitive edge (and even survive) in today’s unstable business environment, this study contributes to acknowledge the relevance of relational capital management on product innovation success at innovative SMEs. Keywords: intellectual capital, relational capital, product innovation, new product development, innovative SMEs, Portugal

1. Introduction Academic research on business competitiveness has for the past decades gradually changed its focus. The development of dynamic capabilities sustained in factors that are tacit, invisible or intangible by nature has arisen as a privileged mean to achieve greater resource efficiency and create competitive barriers. At the same time, innovation has materialised as one of the most crucial factors for structural development. Against this backdrop, the general purpose of this research is to study interaction effects between intangible assets and innovation at the firm level. Specifically, we will analyse the influence of relational capital on product innovation performance at innovative small and medium enterprises (SMEs). It is nowadays generally accepted that the main components of intellectual capital can be structured into three dimensions: human capital, structural capital and relational capital (Curado et al. 2011).The relational capital concept is based on the consideration that companies are not isolated systems. On the contrary, they are actively and permanently connected to multiple external entities. All valuable relationships of this kind, with customers, suppliers and other relevant stakeholders, represent relational capital (Roos et al. 2001). Bontis (1998) argues that the knowledge of marketing channels and customer relationships is the main theme of relational capital. It represents the potential an organization has due to external intangibles, including the knowledge embedded in customers, suppliers, the government or related industry associations. According to Bueno and Salmador (2000), relational capital represents the firm’s “competitive and social intelligence”. For the purpose of this study, we will thus define relational capital as all valuable relationships, channels and networks that exist between an organization and its stakeholders. Innovation, in the broadest sense, is in the heart of economic change. The vision of innovation as the main driver of long term development is today widely accepted (Leiponen 2005). At the firm level, innovation is nowadays considered to be inevitable: driven by a variety of forces (including globalization, technological evolution and demography), the economic environment is changing rapidly. To succeed in such a context, or even to remain viable, corporations must respond with innovation (Govindarajan and Trimble 2005). Product

Pedro Figueroa Dorrego, Ricardo Costa and Carlos Fernández‐Jardon Fernández innovation, due to its higher visibility in the relationship between companies and consumers, stands out as an element of particular importance to any business. Companies must develop new products, at least on occasion, to maintain or gain competitive advantages, and their ability to create new products has been linked to performance and even long‐term survival (De Jong and Vermeuler 2006, and Linzalone 2008). Several studies have succeeded in trying to empirically demonstrate that intangible assets in general are positively and significantly associated with the firms’ innovative capabilities (for example Canibaño et al. 2002; Chen et al. 2004; Del Canto and González 1999; European Commission 2006; Linzalone 2008; Santos Rodrigues et al. 2010; Subramanian and Youndt 2005; Wu et al. 2008). Nevertheless, rarely were those researches specifically oriented at assessing the impact of relational capital on product innovation performance. Similarly, several authors from the product innovation field (e.g. Abetti 2000; Bullinger et al. 2004; Cooper et al. 2004, 2004a, 2004b; Kandemir et al. 2006; Montoya‐Weiss and Calantone 1994; Shum and Lin 2007) have analysed the critical success factors for new product development (NPD), but they seldom focused on the specific impact of relational capital elements. In order to fill these gaps, the main purpose of this study is to empirically validate if the existence of relational capital elements at innovative SMEs influences its product innovation performance.

2. Relational capital and product innovation: Theoretical model Product innovation is by definition an uncertain process, with few repetitive or predictable elements. Consequently, it requires a search for knowledge outside the firm’s existing knowledge base, often in areas unrelated with its current operations. This is why relational capital, or the intensity with which the organization is connected with elements outside its walls, can be a critical source of innovation. Hargadon and Sutton (1997) studied how the development of network connections across industries can benefit product innovation. Continuous innovation, they argue, is often related to occupying a “structural hole”, that is, the gap in the flow of information between subgroups in a larger network. In fact, knowledge is often shared imperfectly through time, people, organizations and industries. The ideas that come up in a group could solve other groups’ problems, but that can only happen if there are links that can go through the existing frontiers between solutions and problems. When those connections take place, existing ideas can appear new and creative, as they change shape and are combined with other ideas to solve new problems. We thus argue that high levels of relational capital at the firm level (through a clear orientation to develop links with external knowledge sources) strengthen the firm’s ability to absorb and transform new knowledge, and thus its product innovation potential. In fact, some authors (eg. Cohen and Levinthal 1990) state that the ability of a firm to recognise the value of new, external information, assimilate it, and apply it to commercial ends (that is, the firm’s “absorptive capacity”) represents a learning process that is critical to its innovative capabilities. Empirical support was also found for the notion that developing and nurturing the existence of knowledge flows beyond the borders of the firm and through distinct scientific areas turns R&D efforts more productive (Pike et al. 2005). This kind of initiatives includes cooperation agreements with suppliers, external experts, research centres or universities, as well as contacts with regulatory entities. Nonaka and Takeuchi (1995) argue that the creation of an R&D network based on strong connections with suppliers was determinant for the success of Japanese firms’ innovation efforts in the 80’s. Other studies (for example, Ahuja 2000) tried to understand the impact of collaboration networks on the firm’s innovative capabilities. It was found that direct and indirect ties with other firms and institutions do have an influence on the firm’s innovation output. In fact, the existence of collaboration networks was found to contribute to the sharing of knowledge, know‐how and physical resources, serving as “conductors” through which the news of technological breakthroughs, new solutions to existing problems or failed approaches travel from one firm to another. Bullinger et al. (2004) also argue that the existence of vertical and horizontal networks (with customers and suppliers and with other firms) is a very important factor to the firm’s ability to innovate. Firms “need to build up a company‐wide internal innovation network of innovation actors and integrate their innovation process in mutual horizontal and vertical networks” (Bullinger et al. 2004, pp. 3346) in order to share knowledge and benefit from complementary competencies, as a critical way to timely identify new innovation options and directions. Subramanian and Youndt (2005) conclude that external relationships, alliances and collaboration networks are essential to an organization’s innovation versatility, adding that “social capital” is gaining increasing importance and visibility as an organizational resource.

Pedro Figueroa Dorrego, Ricardo Costa and Carlos Fernández‐Jardon Fernández Some studies stress the importance of relational capital as a manifestation of the organization’s market orientation. Bontis (1999) mentions the relevance of an organization‐wide generation of “market intelligence” regarding current and future needs of customers, especially through vertical relationships. This pragmatic perspective of relational capital stresses the importance of developing multiple external links as a way to increase the firm’s ability to identify the true needs of the market, and thus increase the potential success of its new products. We thus find some evidence that relational capital, representing the set of channels, contacts and initiatives that build bridges between the firm and its external environment, can be a critical source of new knowledge that feeds the firm’s innovative capabilities. Therefore, it seems acceptable to assume that the existence and proactive management of relationships with external stakeholders will have a relevant influence on the firm’s product innovation efforts at innovative SMEs. These relationships increase access to new ideas and contribute to a better understanding of the target markets, increasing the odds of success at launching new products. We will thus hypothesise that relational capital is positively associated with product innovation performance at innovative SMEs. Even if one accepts this hypothesis in a general way, another important question remains: which of the constitutive elements of relational capital are the main drivers of product innovation? Within the notion of collaborative networks lies a wide array of possibilities, namely regarding the decision to establish relationships with very different types of stakeholders. Understanding which are more relevant to product innovation at innovative SMEs is particularly critical, more so if we accept the notion that collaborative networks are harder to establish and manage at the SME level. To help identify the specific relational capital elements that improve product innovation performance is another purpose of this article. Figure 1 encapsulates the theoretical model we intend to test: RC Elements

RC1 RC2

Relational Capital

RC...

Product Innovation Performance

Figure 1: Theoretical model

3. Research methodology In order to empirically gauge the influence of relational capital on product innovation performance at innovative SMEs, and having presented the theoretical background that inspired this study, we will now focus on the research methodology that guided our field work.

3.1 Variable definition and measurement Being a volatile and uncertain process, product innovation often requires a search for knowledge outside the firm’s existing knowledge base, frequently in areas not directly related to its current activities. That is why relational capital, or the intensity with which the firm relates with external entities, can be an essential basis to acquire new knowledge. It thus seems adequate to assume that the existence of direct and indirect links with external stakeholders will have a determinant impact on the firm’s innovative capabilities, and particularly on its product innovation performance. Firms with stronger ties to its suppliers and customers also gain an increased sensitivity towards market needs, which turns their product innovation effort into a more oriented and effective process. In order to study the existence of relationships with the exterior, and also to understand to what extent they are proactively managed at the firm level, we chose to consider two relational capital elements: the existence of vertical and horizontal relationships; and the management of relationship processes (incorporating the results of our own research and also studies from Ahuja 2000; Bullinger et al. 2004; IADE 2003; Pike et al. 2005; Subramanian and Youndt 2005, and Youndt et al. 2004):

Pedro Figueroa Dorrego, Ricardo Costa and Carlos Fernández‐Jardon Fernández

Vertical and horizontal relationships surveyed the existence of relationships with customers, suppliers, competitors and other institutions with the specific goal of enriching the firm’s product innovation capabilities. We relied on three indicators to measure this element:

There are vertical relationships (with customers and suppliers) with the specific goal of strengthening our product innovation capabilities

There are horizontal relationships (with partners and competitors) with the specific goal of strengthening our product innovation capabilities

There are relationships with other institutions (government agencies, external experts, public and private R&D centres, shareholders, etc.) with the specific goal of strengthening our product innovation capabilities

Management of relationship processes relates to the proactive and systematic management of existing relationship processes and channels with the exterior. We relied on five indicators to measure this element:

The company makes a specific effort to identify and establish relationships with customers or users who are more receptive to innovative products (lead users)

The company actively manages formalized relationship processes with clients

The company actively manages formalized relationship processes with suppliers

The company actively manages formalized relationship processes with competitors

The company actively manages formalized relationship processes with institutions, shareholders and investors

In what concerns the measurement of product innovation performance, a growing number of studies is relying on the use of the so‐called “impact indicators”, which measure the financial and economic significance of product innovation to the company. With that in mind, we relied on three indicators to measure product innovation performance, incorporating the results of our own research and also studies from Cooper (2004), OECD (2005), Shum and Lin (2007) and Souitaris (2002):

Proportion of projects entering development stage that became commercial successes (met or exceeded sales goals) in the past three years;

Percentage of current sales revenue derived from new products introduced in the past three years;

Proportion of projects hitting their launch dates on time and on budget.

3.2 Sample definition and data collection As in most studies concerning intellectual capital and product innovation, this research was conducted at the firm level. The theoretical population was established as “small and medium Portuguese innovative firms”. We chose a network of Portuguese innovative SMEs, COTEC’s “Rede PME Inovação”, as being the best possible sample for our theoretical population. COTEC is a non‐profit association supported by the Portuguese Government and the institutions of the National Innovation System, aimed at promoting the competitiveness of companies established in Portugal, through the development and the diffusion of a culture and practice of innovation. COTEC’s membership list includes virtually all of the most prominent companies operating in Portugal. Among its initiatives, COTEC endorses an expanding innovative SMEs network (“Rede PME Inovação”) based in Portugal, which comprises innovative SMEs that, having applied for network membership, fulfil a set of specific criteria and enjoy a minimal score on COTEC’s “innovation scoring”. At the date of the research, this network comprised 100 firms, with a total of around 7729 employees and 782 million Euros of total turnover. Once the research constructs were operationalised and the target population and its sample were established, a preliminary version of the questionnaire was designed. A 5‐point Likert scale was used in relational capital related indicators (comprising a total of eight statements), and a choice of percentage intervals was used in product innovation performance items (three in total). A pilot study with four firms and an expert interview were conducted, and some items were refined through this purification process. The data collection took place from July 2008 to January 2009, via e‐mail, involving all 100 companies. The request included a description of the study, stating its usefulness and social value, and a statement of confidentiality. The questionnaire was directed to the CEO of each firm, as suggested by the Oslo Manual (OECD 2005). Follow‐up telephone calls

Pedro Figueroa Dorrego, Ricardo Costa and Carlos Fernández‐Jardon Fernández were made to each firm explaining the purpose of the research, and a few questionnaires were taken in person. 72 responses were received, for a response rate of 72 percent. As every response was valid, 72 was our effective sample size.

4. Data analysis and results This section is dedicated to the empirical findings of the research. Once all questionnaires were received, we proceeded to the treatment and analysis of the data, using a combination of multivariate statistical techniques.

4.1 Preliminary analysis A preliminary analysis of the data for processing and purification purposes was conducted, using SPSS software. The existence of abnormal behaviour was studied through the analysis of frequency tables and descriptive statistic measures, as well as through a joint purification by classifying the data into clusters, using k‐averages. As a consequence, two cases were considered to be incoherent, and were therefore excluded. A chi‐square test proved the geographical representativeness of the sample. Cronbach’s α coefficients of the constructs were then calculated. Cronbach’s α coefficient for “relational capital” was 0.770, and Cronbach’s α coefficient for “product innovation performance” was 0.746. Typically, the minimum threshold of Cronbach’s α coefficient is 0.7 (Hair et al. 1998).

4.2 Regression analysis and results The next step of the study was to empirically test the relationships between relational capital and product innovation performance. We started by reducing the data, using a principal components factor analysis. In what concerns relational capital, KMO’s measure of sampling adequacy was 0.696, signalling an acceptable quality of correlation between variables. Bartlett’s test resulted in a 0.000 level of significance, dismissing the hypothesis that the correlation matrix is the identity matrix. These results allowed us to proceed with factor analysis for relational capital. Two factors were extracted under the established criteria, as presented in Table 1, obtained through a Varimax rotation with Kaiser normalization that converged in three iterations. These factors account for 59,476 percent of cumulative variance explained. All item loadings are over 0.5, which is commonly considered as a high significance level. Table 1: Factor analysis results for relational capital Factor Percentage Factor 1: Vertical relationships

Variance explained Cumulative variance explained Factor 2: Horizontal and institutional relationships

39,575 39,575

Variance explained Cumulative variance explained

19,901 59,476

Item description Loadings The company actively manages formalised relationship ,893 processes with clients The company makes a specific effort to identify and ,794 establish relationships with customers or users who are more receptive to innovative products (lead users) There are vertical relationships (with customers and ,673 suppliers) with the specific goal of strengthening our product innovation capabilities The company actively manages formalised relationship ,624 processes with suppliers The company actively manages formalised relationship ,807 processes with institutions, shareholders and investors There are relationships with other institutions with the ,786 specific goal of strengthening our product innovation capabilities There are horizontal relationships (with partners and ,706 competitors) with the specific goal of strengthening our product innovation capabilities The company actively manages formalised relationship ,604 processes with competitors

According to the characteristics of the items, we labelled these two factors as follows:

Factor 1: “vertical relationships”, representing the existence and proactive management of vertical relationships (with customers and suppliers), aimed at improving the firm’s product innovation capability;

Pedro Figueroa Dorrego, Ricardo Costa and Carlos Fernández‐Jardon Fernández

Factor 2: “Horizontal and institutional relationships”, representing the existence and proactive management of horizontal relationships (with partners and competitors, as well as with shareholders, investors and other institutions), aimed at improving the firm’s product innovation capability.

In what concerns product innovation performance, KMO’s measure of sampling adequacy was 0.653, signalling a reasonable quality of correlation between variables. Bartlett’s test resulted in a 0.000 level of significance, dismissing the hypothesis that the correlation matrix is the identity matrix. These results allowed us to proceed with factor analysis for product innovation performance. One single factor was extracted under the established criteria, as presented in Table 2, which accounts for 66,657 percent of cumulative variance explained. Therefore, we aggregated the three items into one single measure for product innovation performance. Again, all item loadings are over 0.5. Table 2: Factor analysis results for product innovation performance Factor Factor 1: Product Innovation Performance Variance explained Cumulative variance explained

Percentage Item description Loadings Proportion of projects entering development stage that become commercial successes (meet or exceed sales ,871 goals) in the past three years Proportion of projects hitting their launch dates on ,811 time and on budget 66,657 Percentage of current sales revenue derived from new ,764 66,657 products introduced in the past three years

We proceeded by performing a multiple linear regression on the data resulting from our factor analysis. The results are contained on Figure 2. RELATIONAL CAPITAL Vertical relationships

Horizontal and institutional relationships

¾ ¾

0.252* (2,159)

R2 = 0.063 Product Innovation Performance

-0.191 (-1.657)

Number on top is the path coefficient (β), t-value in brackets Significance levels: *p<.05; **p<.01; ***p<.001 (2-tailed).

Figure 2: Regression analysis The results confirm that relational capital does have a positive influence on product innovation performance, but strong differences in significance levels show that not all relational capital elements we considered have such a relevant effect. In fact, only the element we called “vertical relationships” reveals a significant impact on product innovation performance.

5. Discussion, limitations and directions for future research The objective of this study was to analyse the influence of relational capital on product innovation at innovative SMEs. We conducted a linear regression analysis between constructs to test our assumption, which was validated by the results: relational capital showed a positive and significant impact on product innovation performance. However, only one of the elements that comprise it revealed such an impact. That element was identified as “vertical relationships”, representing the existence and proactive management of vertical relationships (with customers and suppliers) aimed at improving the firm’s product innovation capabilities. Other types of relationships the firm establishes and manages with the exterior, namely with partners and competitors, shareholders, investors and other institutions, did not show a significant effect on product innovation performance at the companies we analysed. Looking at the overall responses to the questionnaire, we verify that horizontal and institutional relationships reveal much lower mean scores than vertical relationships, indicating that it is clearly more common to create and cultivate links with clients and suppliers than with other stakeholders. These results are consistent with some fragmented research findings published recently in similar contexts (e. g. Bontis 1998; Cabrita and Bontis 2008; Chen et al. 2004), and allow us to generally conclude that the better

Pedro Figueroa Dorrego, Ricardo Costa and Carlos Fernández‐Jardon Fernández the innovative SMEs manage and nurture their relational capital, the more successful those firm’s product innovation efforts will be. The specific relevance of “vertical relationships” that was detected is consistent with the interpretation of relational capital as a manifestation of the firms’ “market orientation”. In fact, an effective way to gather important knowledge regarding current and future market needs is through the establishment of relationships with clients and suppliers. By doing this, firms are able to more easily obtain, absorb and internalise market knowledge and to manage their product development process in a more oriented and effective manner. This idea is reinforced by some authors from the product innovation research stream (for example Cooper et al. 2002 and Kotler 1991), who stress the importance of creating strong ties with clients and suppliers to enhance the firm’s ability to identify market needs and thus increase its product innovation effectiveness. Our research shows that these findings also seem to apply to innovative SMEs. We hope this study contributes to clarify which relational capital elements are the most important to product innovation success, offering some clues on how to address the problem of managing relational capital to increase product innovation performance. Creating and managing relationships with customers and suppliers seem to be key factors to consider. This research has some limitations that need to be addressed. The first one relates to the characteristics and size of the population being studied, COTEC’s “Rede PME Inovação”. We cannot state without reservations that these firms are representative of all Portuguese innovative SMEs, so the generalization of our results must be cautious. Conducting further research within a larger population would be useful to confirm the generalization of our results to all innovative SMEs. Another important issue to consider regards the decision to analyse the influence of only one intellectual capital component on product innovation. Some studies (for example, Bontis 1998; Bontis et al. 2000; Cabrita and Bontis 2008; Chen et al. 2004; Santos Rodrigues et al. 2010) have found that intellectual capital components often reveal relevant path dependencies among themselves, when measuring their combined 2 influence on organizational phenomena. Low R readings on our model suggest the convenience to rebuild it by incorporating other intellectual capital components (namely structural capital and human capital). In the future we will study the combined impact of all dimensions of intellectual capital on product innovation performance.

References Abetti, P. (2000) “Critical Success Factors for Radical Technological Innovation: a Five Case Study”, Creativity and Innovation Management, Vol. 9, No. 4, pp 208‐221. Abou‐Zeid, E. and Cheng, Q. (2004) “The Effectiveness of Innovation: a Knowledge Management Approach”, International Journal of Innovation Management, Vol. 8, No.3, pp 261‐274. Ahuja, G. (2000) “Collaboration Networks, Structural Holes and Innovation: a Longitudinal Study”, Administrative Science Quarterly, 45 (2000), pp 425‐455. Bontis, N. (1998) “Intellectual Capital: an Exploratory Study that Develops Measures and Models” Management Decision, vol. 36, No. 2, pp 63–76. Bontis, N. (1999) “Managing Organizational Knowledge by Diagnosing Intellectual Capital: Framing and Advancing the State of the Field”, International Journal of Technology Management, 18/5‐6‐7‐8, pp 433–462. Bontis, N.; Keow, W. and Richardson, S. (2000) “Intellectual Capital and Business Performance in Malaysian Industries”, Journal of Intellectual Capital, vol. 1, No. 1, pp 85‐100. Bueno, E. and Salmador, Mª. P. (2000) “Perspectivas sobre Dirección del Conocimiento y Capital Intelectual”, Instituto Universitario Euroforum Escorial, Octubre, Madrid. Bullinger, H.‐J.; Auernhammer, K. and Gomeringer, A. (2004): ”Managing innovation networks in the knowledge‐driven economy” International Journal of Production Research, vol. 42, No. 17, pp 3337‐3353. Cabrita, M. and Bontis, N. (2008) “Intellectual capital and business performance in the Portuguese Banking Industry”, International Journal of Technology Management, vol. 43, No. 1‐3, pp 212‐237. Cañibano L, Sanchez P, García‐Ayuso M and Chaminade C (2002) “Directrices para la Gestión y Difusión de Información sobre Intangibles”, MERITUM Project, available at http://www.uam.es/personal_pdi/economicas/palomas/DIRECTRICES%20MERITUN%20‐%20 ESPANOL.pdf Chen, J.; Zhu, Z. and Xie, H. (2004) “Measuring Intellectual Capital: a New Model and Empirical Study”, Journal of Intellectual Capital, vol. 5, No. 1, pp 195‐212. Cohen, W. and Levinthal, D. (1990) “Absorptive Capacity: A New Perspective on Learning and Innovation”, Administrative Science Quarterly, Special Issue: Technology, Organizations, and Innovation (March, 1990), 35/1, pp 128‐152. Cooper, R.; Edgett, S. y Kleinschmidt, E. (2002) “Optimizing the Stage‐Gate Process; What Best‐practice Companies Do ‐ I”, Research Technology Management, September‐October 2002, pp 21‐27.

Pedro Figueroa Dorrego, Ricardo Costa and Carlos Fernández‐Jardon Fernández Cooper, R.; Edgett, S. and Kleinschmidt, E. (2004) “Benchmarking Best NPD Practices – I”, Research Technology Management, January‐February 2004, pp 31‐43. Cooper, R.; Edgett, S. and Kleinschmidt, E. (2004a) “Benchmarking Best NPD Practices – II”, Research Technology Management, May‐June 2004, pp 50‐59. Cooper, R.; Edgett, S. and Kleinschmidt, E. (2004b) “Benchmarking Best NPD Practices – III”, Research Technology Management, November ‐December 2004, pp 43‐55. Curado, C., Henriques, P. and Bontis, N. (2011) “Intellectual Capital Disclosure Payback”, Management Decision, vol. 49, No. 7. De Jong, J. and Vermeulen, P. (2006) “Determinants of Product Innovation in Small Firms ‐ A Comparison Across Industries”, International Small Business Journal, vol. 24/6, pp 587‐609. Del Canto, J., and González, I. (1999) “A Resource‐based Analysis of the Factors Determining a Firm’s R&D Activities”, Research Policy, 28 (1999), pp 891‐905. European Commission (2006) RICARDIS: Reporting Intellectual Capital to Augment Research, Development and Innovation in SMEs, Luxembourg. European Commission (2010) Annual report on EU small and Medium‐sized Enterprises 2009, Luxembourg. Govindarajan, V. and Trimble, C. (2005) “Organizational DNA for Strategic Innovation”, California Management Review, vol. 47, No. 3, Spring, pp 47‐76. Hair, J.; Anderson, R.; Tatham, R. and Black, W. (1998), Multivariate Data Analysis, Fifth Edition, Prentice Hall, New Jersey. Hall, R. and Andriani, P. (2002) “Managing Knowledge for Innovation”, Long Range Planning, 35 (2002), pp 29‐48. Hargadon, A. and Sutton, R. I. (1997) “Technology Brokering and Innovation in a Product Development Firm”, Administrative Science Quarterly, 42/4, pp 716–49. 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(2007) “A world class new product development best practices model”, International Journal of Production Research, vol. 45, No. 77, pp 1609‐1629. Souitaris, V. (2002) “Firm‐specific Competences Determining Technological Innovations: a Survey in Greece”, R&D Management, vol. 32, No. 1, pp 61‐77. Stewart, T. A., (1997) Intellectual Capital: The New Wealth of Organizations, Doubleday/Currency, New York, NY. Subramaniam, M. and Youndt, M. (2005) “The Influence of Intellectual Capital on the Types of Innovative Capabilities”, Academy of Management Journal, vol. 48, No. 3, pp 450‐463. Thornhill, S. (2006) “Knowledge, Innovation and Firm Performance in High ‐ and Low – Technology Regimes”, Journal of Business Venturing, 21 (2006), pp 687‐703. Vaona, A. and Pianta, M. (2008) “Firm Size and Innovation in European Manufacturing”, Small Business Economics, vol. 30, No. 3, pp 283–299. Youndt, M. A.; Subramaniam, M. and Snell, S. A. 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The Role of ISO 14001 in Sustainable Enterprise Excellence Tijana Durdevic1, Cory Searcy2 and Stanislav Karapetrovic1 1 University of Alberta, Edmonton, Canada 2 Ryerson University, Toronto, Canada djurdjev@ualberta.ca cory.searcy@ryerson.ca skarapetrovic@ualberta.ca Abstract: The purpose of this paper is to explore the role of ISO 14001 in sustainable enterprise excellence (SEE). The paper investigates which management system standards (MSS), including ISO 14001, are integrated into a single management system by Canadian organizations. It also investigates the extent to which sustainability guidelines are employed in organizations registered to the ISO 14001 standard. To address these issues, an empirical survey of 32 Canadian organizations registered to the ISO 14001 standard was conducted. The paper presents data on the other MSS used in organizations registered to the ISO 14001 standard, the order of their implementation, and which ones are combined in an integrated management system (IMS). The use of existing sustainability guidelines, such as the Global Reporting Initiative (GRI), is also investigated. Future research should focus on obtaining larger sample sizes and conducting follow‐up interviews to probe the responses given in the questionnaires. Nonetheless, the paper sheds light on several areas that are currently not well understood, including the specific MSS that are integrated into a single system, the motivations for doing so, and the challenges involved. Keywords: sustainability, sustainable development, environment, sustainable enterprise excellence, ISO 14001, management system standards (MSS), integrated management systems (IMS)

1. Introduction Sustainable Enterprise Excellence (SEE) is an area of emerging interest. Edgeman and Eskildsen (2012) explain that SEE "is a consequence of balancing both the competing and complementary interests of key stakeholder segments, including society and the natural environment, to increase the likelihood of superior and sustainable competitive positioning and hence long‐term enterprise success." SEE may therefore be conceptualized as an effort to explicitly link the concepts of business excellence and sustainability. However, research on SEE is in its embryonic stages and there are questions as to how this construct can be put into practice. As a starting point, the existing literature on business excellence and sustainability is instructive. Business excellence models (BEM) were originally conceived to help improve quality and operations management (Asif et al., 2011). However, many prominent BEM, such as the Baldridge Criteria for Business Excellence (BCPE) and the European Foundation for Quality Management (EFQM), have expanded their scope to address environmental and social issues (Asif et al., 2011). This expansion in scope reflects a recognition of the need to manage performance in all areas of the "triple bottom line" (Elkington, 1998) emphasized by the concept of sustainability, namely economic, environmental, and social issues. Several authors, notably Garvare and Isaksson (2001 and 2005), have discussed the application of BEM in the context of sustainability. In many respects, SEE may be viewed as an extension of this work. However, despite the existing work on BEM and sustainability, there remains a need to identify clear leverage points that will help drive progress towards SEE. This paper argues that existing management system standards (MSS), particularly the ISO 14001 standard for environmental management systems (EMS), can play a key role in facilitating progress towards SEE. The implementation of MSS has become a widespread practice around the world (Zeng et al., 2011). MSS designed to address issues related to quality (e.g., ISO 9001), environment (ISO 14001), and occupational health and safety (OHSAS 18001), among others, are well‐established and continue to attract interest. The ISO 26000 guideline for social responsibility has received heavy coverage though it remains to be seen if its lack of auditable requirements will limit its uptake. Nonetheless, a number of authors have argued that the time, effort, and expense invested in the implementation of these MSS provide a base on which to build the principles of sustainability into an organization through the creation of "sustainable management systems" (Jørgensen, 2008; Oskarsson and Malmborg, 2005; Rocha et al., 2007; Ho, 2010a and b; Asif et al., 2011). Many of these authors have noted that the implementation of such systems may be facilitated through an integrated management systems (IMS) approach.

Tijana Durdevic, Cory Searcy and Stanislav Karapetrovic ISO 14001 has been recognized as a key leverage point in the implementation of sustainable management systems (Fortunski, 2008; Sebhatu and Enquist, 2007). Given its close association with the concept of sustainability and its auditable requirements, this paper adopts the view that ISO 14001 is the most suitable MSS to use as a potential entry point for SEE. With that in mind, the purpose of this paper is to explore the role of ISO 14001 in SEE. To do this, the paper begins with a brief review of the literature on three closely related areas: ISO 14001, IMS, and sustainability and SEE. It then draws on the results of an original survey of 32 Canadian organizations registered to the ISO 14001 standard. The methodology employed in the survey is presented. That is followed by a review and discussion of the key results obtained. Particular emphasis is placed on investigating which MSS are integrated into a single management system by Canadian organizations, the extent to which sustainability guidelines are employed, and the motivations for doing so. The paper finishes with a brief conclusion.

2. Literature survey This section presents brief snapshots of the literature in three key areas: (1) ISO 14001, (2) IMS, and (3) sustainability and SEE.

2.1 ISO 14001 Over a quarter of a million certificates have been issued indicating registration to the ISO 14001 standard and these numbers are growing rapidly (ISO, 2010). The popularity of ISO 14001 is also reflected in the academic literature. A number of papers have been published on the motivations for implementing the standard, the value of doing so, the challenges associated with implementation, and possibilities for improving the standard. Pan (2003) explains that the main motivation for implementing ISO 14001 is to improve corporate image. A number of other authors have explored other motivations, including Jiang and Bansal (2003), Poksinska et al. (2003), Psomas et al. (2011), and Rodriguez (2009). Some of the other motivations cited include market demand, enhanced management control, customer pressures, competitive advantages, and concern for the environment. The benefits of implementing ISO 14001 have also been investigated extensively. The key benefits often cited in the literature closely align with the motivations and may include improving "a company’s position in the market”, encouraging a “transition from conventional to sustainable practices”, improving the company's “relationship with society due to better environmental performance” and “improved waste processing” (Psomas et al., 2011). The challenges of implementing ISO 14001 have also been explored. Key issues associated with continual improvement (Brouwer and van Koppen, 2008) and auditing (Searcy et al., 2012a) have received particular focus, though many others have been acknowledged (Berthelot et al., 2003, Strachan et al., 2003). Searcy et al. (2012b) highlight several potential improvements to ISO 14001, including changes to environmental policy, monitoring and measurement, top management review, and a need for enhanced public reporting. Other suggested improvements have been offered by Jørgensen (2008) and Kerret (2008). However, despite the vast amount of research on ISO 14001, many issues remain largely unexplored. In particular, little is known about the influence of ISO 14001 on the adoption or use of other MSS or initiatives. For example, the extent to which sustainability guidelines, such as those offered by the Global Reporting Initiative (GRI, 2006), are used in companies registered to the ISO 14001 standard is currently unknown. Given their potential role in SEE, it is important these issues are addressed.

2.2 Integrated management systems (IMS) IMS has become a popular topic of academic research over the last 15 years. As Karapetrovic and Willborn (1998) explain, an IMS is a "set of interconnected processes that share a pool of human, information, material, infrastructure, and financial resources in order to achieve a composite of goals related to the satisfaction of a variety of stakeholders". A number of authors have explored the benefits associated with adopting an IMS approach (e.g., Zeng et al., 2011; Griffith and Bhutto, 2008; Zutshi and Sohal, 2005), the difficulties an organization may face during the process of integration (Zeng et al., 2007; Bernardo et al., 2012), and the strategies and the level of integration achieved by organizations (Douglas and Glen, 2000; Bernardo et al., 2012; Karapetrovic and Casadesus, 2009; Karapetrovic, 2002; Karapetrovic and Willborn, 1998; Bernardo at al., 2009; Labodova, 2004; Wilkinson and Dale, 2002). Many of these issues have been explored through cases studies with industrial partners. Moreover, there is evidence that an increasing number of companies are

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Tijana Durdevic, Cory Searcy and Stanislav Karapetrovic adopting an IMS approach in practice. For example, Bernardo et al. (2010) found that 65% of Spanish companies surveyed integrated quality and environmental MSS into an IMS. However, there has been relatively little research focused on IMS framed from the perspective of ISO 14001. Bernardo et al. (2009) and Karapetrovic and Casadesus (2009) conducted survey‐based research in Spain and found that ISO 14001 may be integrated in different ways depending on the organization. Searcy et al. (2012b) explored issues associated with IMS in a colloquium with 40 Canadian experts on ISO 14001. There is a slowly growing body of research focused on applying an IMS approach to sustainability management (Jorgensen, 2008; Oskarsson and Malmborg, 2005; Rocha et al., 2007; Asif et al., 2011). Much like the broader literature on IMS, this work tends to focus on the benefits of IMS, the challenges in applying an IMS approach, and the development of models and methodologies to guide the IMS process; except they are written from a sustainability perspective. Nonetheless, IMS offers some insight into the approach necessary to implement management systems for SEE.

2.3 Sustainability and sustainable enterprise excellence (SEE) Organizations are under increasing pressure to address the economic, environmental, and social dimensions of their operations. These three dimensions are often recognized as the three pillars of sustainability. However, the key aspects that must be addressed within these dimensions are interpreted and prioritized in many different ways. This is partially a reflection of the fact that there is no universally accepted definition of sustainability in an organizational context (Glavic and Lukman, 2007). The challenges associated with sustainability are amplified when the long time horizon explicitly associated with the concept (WCED, 1987) is taken into account. To act on public commitments to sustainability, organizations have undertaken a variety of initiatives. There are growing bodies of research on sustainability reporting (GRI, 2006), sustainability performance measurement systems (Searcy, 2012), codes of conduct (Bondy et al., 2008), and sustainable supply chain management (Hassini et al., 2012), among many other areas. However, many organizational sustainability initiatives remain in their early stages and none of the existing approaches provide a practical template for implementing sustainable management systems (Sealy et al., 2010). As a result, many organizations have focused on incremental approaches to sustainability rather than holistic approaches that emphasize the need to build it into the core fabric of the enterprise. SEE is a structured attempt to address an organization's key economic, environmental, and social challenges and opportunities in a cohesive and integrated way. It thus provides the big picture view needed to tackle an organization's core sustainability issues in order to deliver enhanced stakeholder relationships, efficiency, competitiveness, and profitability over both the short‐ and long‐term.

3. Methodology An empirical survey of Canadian organizations registered to the ISO 14001 standard was conducted. In June 2012 the survey was mailed to 483 organizations. The surveys were addressed to managers responsible for environmental management systems. The target organizations were identified through publicly available lists of ISO 14001‐registered organizations provided by BNQ (Bureau de Normalisation du Quebec), PWGSC (Public Works and Government Services Canada), and QMI‐SAI (Quality Management Institute, which is owned by SAI Global). For the companies in Quebec, the surveys were sent in French (44 out of 483). All other surveys were sent in English. A total of 22 surveys were returned as undeliverable. Out of the remaining 461 organizations, 32 completed and returned the survey. This represented a response rate of approximately 7%. The survey was divided into 6 sections: (1) ISO 14001 Implementation, (2) ISO 14001 Audits, (3) ISO 14001 Benefits, (4) Other Management Systems and Integration, (5) Sustainability Guidelines and, (6) Organization Profile. Some of the key results of the survey are presented and discussed in the following section. To provide some context for the results, it should be noted that 22% of the participant organizations have less than 100 employees, 44% have between 100 and 500 employees, and 34% have more than 1000 employees.

4. Results and discussion This paper focuses on addressing five key questions from the survey:

What was the scope of the standardization?

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In which order were the MSS implemented?

How much time was required for implementation?

What were the main reasons for implementing other management system standards?

What was the extent to which sustainability guidelines were employed in organizations registered to the ISO 14001 standard?

The other sections of the survey are not addressed in this paper.

4.1 Scope of standardization In addition to ISO 14001 a total of 30 organizations (94%) reported that they had implemented other MSS. Specifically, 18 (56%) organizations had implemented ISO 9001 for quality management systems, 4 (13%) ISO/TC 16949, and 8 (25%) had implemented the OHSAS 18001 standard for the management of occupational health and safety. An additional 8 organizations reported the use of another MSS, such as CSA Z809 (for sustainable forest management). Furthermore, 6 organizations (18.75%) reported that they had used ISO 9004 as a guideline for their quality management system. From those that implemented multiple MSS, 2 (6.3%) companies implemented 4 MSS (ISO 14001, ISO 9001, ISO 18001 and ISO/TC 16949) and only 1 company (3%) implemented 3 MSSs (ISO 14001, ISO 9001 and ISO/TC 16949). The participants were also asked about the priority they gave to the following actions:

Adding new MSS (e.g., SA 8000, ISO 27001, ISO 28000).

Adding new supporting standards/guidelines for specific areas or system components (e.g., ISO 14031, ISO 14064, ISO 10002).

Adding new improvement standards / excellence models (e.g., ISO 14004, Canadian Framework for Business Excellence).

Not adding any new standards / guidelines / excellence models.

Figure 1 illustrates the priority of doing the above mentioned actions, where 1 indicates the highest priority and 4 the lowest. The arithmetic mean was used to determine the average response shown in the figure.

Figure 1: Priority organizations give to listed actions

4.2 Sequence of implementation As previously noted, a total of 30 (94%) organizations had implemented other MSSs in addition to the ISO 14001 standard. Figure 2 presents the responses regarding the sequence of implementation reported by these 30 organizations.

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Figure 2: Order of implementation of management system standards (%) As shown in Figure 2, ISO 9001 was the standard most frequently implemented first, followed by ISO 14001, and OHSAS 18001. In cases where ISO 14001 was not implemented first, it was always implemented second. These findings are broadly in line with the results found by Karapetrovic and Casadesus (2009) and Bernardo et al. (2010). In studies of organizations based in Spain, these studies found that most of the respondents had also implemented ISO 9001 first, followed by ISO 14001 and OHSAS 18001.

4.3 Time for implementation The respondents were asked to indicate the time to implement MSS in their organizations. The results are shown in Figure 3. The mean time required to implement the first MSS was found to be between 18 and 23 months. For the second standard, 8 organizations (25%) responded that they required between 6 and 11 months for implementation. Another 8 (25%) indicated the time to implement the second standard to be between 12 and 17 months. Based on the responses received, it is clear that less time was required to implement the second standard than the first. It was further determined that the time needed for the implementation of the third and fourth MSS was between 6 and 11 months in both cases. These findings are in line with the results found by Karapetrovic and Casadesus (2009), where the average time for the implementation of the first MSS was 19 months, the second 15 months, and the third and fourth was the same, 11 months.

Figure 3: Time to implement MSS by order of implementation (%)

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4.4 Reasons for implementing other systems in the organization The participants were asked to indicate the principal reasons for implementing standards other than ISO 14001 in their organization. Table 1 summarizes the responses received. As can be seen from the table, the major reason was synergies among management systems, which was rated at 3.2 out of 5 (where 1 indicates very unimportant and 5 very important). Other frequently cited reasons were improvement of image and social impact and improvement in efficiency and control rated at 3.1 out of 4. Pan (2003) and Poksinska et al. (2003) had previously argued that the main reason for implementing ISO 14001 was the improvement of corporate image. In our study, this reason also fared well which shows some broad alignment in terms of motivations for implementing the other MSS. However, synergies among management systems have not previously been highlighted as a key motivation. Table 1: Reasons for implementing other management systems Reason for implementing other systems

Rank

Synergies among management systems

3.2

Improvement in efficiency and control

3.1

Improvement of image and social impact

3.1

Natural continuation of the previous standard

3.0

Decreasing problems and accidents

2.9

Customer pressure

2.6

Provision of competitive advantage

2.6

Government pressure

2.3

Others

1.1

4.5 Number of systems integrated into a single management system (IMS) The participants were asked questions regarding the extent to which their individual MSS were integrated. The responses indicate that 10 (33%) out of the 30 organizations that had implemented two or more MSS had fully integrated their MSS. An additional 6 (20%) organizations had partially integrated their MSS. 14 (47%) organizations had not integrated their systems into a single management system. It can be concluded that the majority of the organizations with multiple MSS in the sample were pursing some form of integration (53% in total). However, previous studies of this issue find that this number is relatively low. Karapetrovic and Casadesus (2009), Bernardo et al. (2010), Bernardo et al. (2009), and Douglas and Glen (2000) all found rates of integration higher than 53%.

4.6 Use of sustainability guidelines, motivation and order of implementation The participants were asked multiple questions about their use of other sustainability guidelines. First, they were asked if they used any of the GRI, the Global Compact, or AA1000. The results are presented in Figure 5. As indicated in the figure, less than 10% of the organizations (3 companies out of 32) used the GRI (Global Reporting Initiative). Other sustainability guidelines, such as AA1000 and Global Compact, were not currently being used by any of the organizations in this study. The relative lack of use of these standards was a bit of a surprise given the close linkages often made between ISO 14001 and sustainability in the academic literature. The participants were then asked to indicate the order of implementation for the sustainability guidelines in their organization. In all 3 cases, ISO 14001 was implemented first followed by the GRI. Again, no organizations reported implementing either of the other sustainability guidelines listed. Finally, the participants were asked to indicate their motivations for implementing the sustainability guideline. The results are shown in Figure 6. It should be kept in mind that the averages presented in the figure are for only 3 responses and caution should therefore be exercised in interpreting the results.

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Figure 5: Usage of the sustainability guidelines

Figure 6: Motivation for implementing sustainability guidelines

5. Conclusion The survey of 32 Canadian organizations registered to the ISO 14001 standard revealed a number of interesting findings. The results show that a high percentage of Canadian organizations with an ISO 14001 registered EMS also have an ISO 9001 registered quality management system in place. ISO 9001 was found to be the most common starting point for implementing MSS in the organizations in the sample. This was despite the fact that the participants were selected specifically due to their implementation of ISO 14001. ISO 14001 and OHSAS 18001 were found to be the second and third most common starting points, respectively. These findings align with what was previously found in studies of Spanish companies (Karapetrovic and Casadesus, 2009; Bernardo et al., 2010). The study also highlighted that the time required to implement the first MSS far exceeded the time required to implement the second, third, and fourth. These findings are also aligned with the literature (Karapetrovic and Casadesus, 2009) and indicate that organizations may benefit from the organizational learning and the establishment of infrastructure that accompany the implementation of its first MSS. Of the participating organizations that implemented more than one MSS, the majority were found to be pursuing some form of IMS. In fact, synergies in management systems was the primary motivation cited for implementing MSS other than ISO 14001. Other interesting findings were related to the use of sustainability guidelines by the organizations in the sample. Surprisingly, only 3 of the 32 respondents were using the GRI's sustainability reporting guidelines. Other sustainability guidelines, such as AA1000 and the Global Compact, were not being used by any of the organizations in this study. The low use of these guidelines may be indicative of the fact that ISO 14001 is weak on public reporting requirements, a point previously raised by Searcy et al. (2012a). Nonetheless, the findings should be viewed with some caution. First, it is important to recognize that the results may not apply to other countries. Second, the sample size was relatively small and it is possible that a larger sample would lead to some changes in the results. The study has a number of interesting implications in terms of ISO 14001's potential role in SEE. First, it is clear that many organizations that implement ISO 14001 choose to do so as a part of a broader IMS. This helps ensure that the environmental dimension of sustainability receives meaningful consideration in the overall management system. An IMS incorporating ISO 14001 provides a strong foundation for the development of sustainable management systems that will be needed to guide progress towards SEE. Second, the fact that

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Tijana Durdevic, Cory Searcy and Stanislav Karapetrovic implementation of a MSS generally reduces the amount of time required to implement subsequent MSS indicates that existing MSS provide a strong leverage point for SEE. Building on existing organizational learning and infrastructure may help minimize any potential disruption a transition to SEE may entail. Given its clear linkages to sustainability, ISO 14001 is a particularly useful leverage point in this regard. However, this must be balanced by another point: many organizations that have implemented ISO 14001 are choosing not to go beyond its relatively weak reporting requirements. This indicates that SEE may require more rigorous reporting requirements than those currently built into ISO 14001 (and other MSS). Future work should thus focus on elaborating the specific steps required to progress towards SEE, including how ISO 14001 can facilitate that transition.

References Asif, M., Searcy, C., Garvare, R. and Ahmad, N. (2011), "Including sustainability in business excellence models", Total Quality Management & Business Excellence, Vol. 22 No. 7, pp. 773‐786. Bernardo, M., Casadesus, M., Karapetrovic, S. and Heras, I. (2009), “How integrated are environmental, quality and other standardized management systems? An empirical study”, Journal of Cleaner Production, Vol. 17, pp. 742‐750. Bernardo, M., Casadesus, M., Karapetrovic, S., and Heras, I. (2010), “Integration of standardized management systems: does the implementation order matter?”, International Journal of Operations & Production Management”, Vol. 32 No. 3, pp. 291‐307. Bernardo, M., Casadesus, M., Karapetrovic, S., and Heras, I. (2012), “Do difficulties influence management system integration levels?”, Journal of Cleaner Production, Vol. 21, pp. 23‐33. Berthelot, S., McGraw, E., Coulmont, M., and Morrill, J. (2003), “ISO 14000: Added value for Canadian business?”, Environmental Quality Management, Vol. 13, No. 2, pp. 47‐58. Bondy, K., Matten, D., Moon, J. (2008), “Multinational corporation codes of conduct: governance tools for corporate social responsibility?”, Corporate Governance, Vol. 16 No. 4, 294‐311. Brouwer, M. A. and Van Koppen, C. (2008), “The soul of the machine: Continual improvement in ISO 14001”, Journal of Cleaner Production, Vol. 16, No. 4, pp. 450. Douglas, A. and Glen, D. (2000), “Integrated management systems in small and medium enterprises”, Total Quality Management, Vol. 11 No 4/5&6. Edgeman, R. and Eskildsen, J. (2012), "Stepping on the springboard to sustainable enterprise excellence" Presentation to the 18th Asia Pacific Quality Conference & 5th National Convention on Quality, Colombo, Sri Lanka, October. Elkington, J. (1998), Cannibals with forks: The triple bottom line of 21st century business, New Society Publishers, Stony Creek, CT. Fortunski B. (2008), “Does the environmental management standard ISO 14001 stimulate sustainable development? – An example from the energy sector in Poland”, Vol. 19 No. 2, pp. 204‐212. Garvare, R. and Isaksson, R. (2005), “Organizational sustainability management through minimized business excellence models”, Third International Conference on Total Quality Management ‐ Advanced and Intelligence Approaches. Glavic, P. and Lukman, R. (2007), "Review of sustainability terms and their definitions", Journal of Cleaner Production, Vol. 15 No. 18, pp. 1875‐1885. Griffin, A. and Bhutto, K. (2008), “Improving environmental performance through integrated management systems (IMS) in the UK”, Management of Environmental Quality: An International Journal, Vol. 19 No. 5, pp. 565‐578. GRI (2006), Sustainability reporting guidelines: version 3.0, Amsterdam, The Netherlands: GRI. Hassini, E., Surti, C., and Searcy, C. (2012), “A Literature Review and a Case Study of Sustainable Supply Chains with a Focus on Metrics”, International Journal of Production Economics, Vol. 140, pp. 69‐82. Ho, S. K. M. (2010a), "Integrated lean TQM model for global sustainability and competitiveness", The TQM Journal, Vol. 22 No. 2, pp. 143‐158. Ho, S. K. M. (2010b), "Integrated lean TQM model for sustainable development", The TQM Journal, Vol. 22 No. 6, pp. 583‐ 593. ISO (2012), ISO Survey. http://www.iso.org/iso/iso‐survey2010.pdf (date of visit: 02/11/2012). Jiang, R. J. and Bansal, P. (2003), "Seeing the Need for ISO 14001", Journal of Management Studies, Vol. 40 No. 4, pp. 1047‐ 1067. Jørgensen, T. H. (2008), "Towards more sustainable management systems: through life cycle management and integration", Journal of Cleaner Production, Vol. 16 No. 10, pp. 1071‐1080. Karapetrovic, S. (2002), “Strategies for the integration of management systems and standards”, The TQM Magazine, Vol. 14 No. 1, pp. 61‐67. Karapetrovic, S. and Casadesus, M. (2009), “Implementing environmental with other standardized management systems: Scope, sequence, time and integration”, Journal of Cleaner Production, Vol.17 No. 5, pp. 533‐40. Karapetrovic, S. and Willborn W. (1998), “Integration of quality and environmental management systems”, The TQM Magazine, Vol. 10 No. 3 pp. 204‐213. Kerret, D. (2008), “ISO 14001 as an environmental capacity building tool — Variations among nations”, Environmental Science and Technology, Vol. 42, No. 8, pp. 2773–2779. Labodova, A. (2004), “Implementing integrated management system using a risk analysis based approach”, Journal of Cleaner Production, Vol. 12, pp. 571‐580.

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Measuring the Impact of Services Innovation: What do we Know? Susanne Durst1 and Anne‐Laure Mention2 1 Center for Knowledge and Innovation Research (CKIR), Aalto University School of Business, Helsinki, Finland and Institute of Entrepreneurship, University of Liechtenstein, Vaduz, Principality of Liechtenstein 2 Centre de Recherche Public (CRP) Henri Tudor, Luxembourg, Luxemburg susanne.durst@aalto.fi anne‐laure.mention@tudor.lu Abstract: The aim of this paper is to review research on the measurement of services innovation to ascertain our current body of knowledge. A literature review for the period 2006 to 2012 was conducted in order to reach the aim, as prior period had been extensively covered by Adams et al. (2006). After a thorough selection process, the review resulted in eight empirical studies indicating our limited body of knowledge with regard to the topic. This study may not have allowed a complete coverage of all empirical articles regarding the measurement of services innovation, and this has to be acknowledged as a limitation of this piece of work. Nevertheless, this paper provides fresh and topical insights on recent literature focusing on innovation in services and on service innovation, thus contributing to the scarce stream of research concentrating on the measurement issues by identifying and quantifying the impact of innovation in an overlooked economic sector. Finally, this paper proposes a number of future research directions which may stimulate more intensive research in this field. Keywords: innovation, innovation management, services innovation, measurement, literature review

1. Introduction In times of austerity there is a compelling need to legitimise expenses in innovation‐related projects. It is important for all organizations, regardless of their size and type, to justify their innovation initiatives. However, measuring innovation impact is rather complex (e.g. thinking of the time‐lag between implementing an innovation project and the impact following this action) and usually poses a profound challenge to innovation managers, especially when services innovations are concerned. There is a mass of contributions discussing the relevance of innovation management, but when we consider the aspect of innovation measurement, the opposite seems to be true: there is a lack of research. This situation can be assessed as unsatisfactory as it prevents organizations from monitoring the success or failure of innovation projects and, thus, disturbs the optimal allocation of their scare resource.. Having this in mind, the purpose of our paper is to review extant literature to identify literature discussing innovation measurement and the kind of new knowledge that has been produced about the measurement of innovation since the literature review by Adams, Bessant and Phelps (2006). More precisely, we are interested in the current body of knowledge regarding the measurement of services innovation. Against the background of the increasing relevance of services innovations for economic development and employment (Tipu, 2011), there is a need for intense research activities in this area. According to the study´s aim, the following research questions are formulated: 1) Which studies have been conducted that focus on innovation measurement? 2) What were the main findings of these studies? 3) Which methods were used? The paper is organized as follows. The literature related to the research purpose is briefly discussed in the next section. Then the method employed to come close to the research problem is described. Afterwards, the results are presented and the conclusions of the study are laid out in the final section.

2. Services innovation and measurement In the literature, services innovation is an ambiguous term as it may simultaneously refer to innovation in service industries, whatever form the novelties may take, and to new services, irrespectively of their degree of novelty and of the industry in which the innovation occurs. Aside from this ambiguity issue, research concentrating on innovation in services and on service innovation has hitherto been relatively scarce, which is a paradox considering the increasing weight of services in economies, in terms of both employment and added value. Over the last four decades, the contribution of value added to GDP from service activities rose by about 18 percentage points in the Organization for Economic Cooperation and Development (OECD) countries and

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Susanne Durst and Anne‐Laure Mention reached 73% in 2008 (OECD). Service industries are nowadays responsible for the majority of employment in the OECD countries. Despite being widely recognized as an engine of growth and competitiveness, service industries remain under‐investigated and knowledge on the actual effect of innovation in services is lagging behind, compared to the research and knowledge on the impact of innovation in the manufacturing sector (Den Hertog, 2000). According to Djellal and Gallouj (2010), the persistent dominance of the industrialist approach to explore innovation in services leads to a double gap: an innovation gap and a productivity gap. The innovation gap is defined as “a measure of the difference between the reality of innovation in a service economy and innovation as it is captured and measured by the traditional indicators”. This observation resonates with the conclusions of Salter and Tether (2006), according to whom, one reason why services did not receive due credit for their innovativeness is related to their low level of R&D intensity and patenting. More generally, it can be argued that the traditional science and technology lenses lead to an overlook of innovation in services. According to Djellal and Gallouj (2010, p. 6) “the service economy probably innovates more than these indicators would suggest and that consequently there is hidden or invisible innovation in service economies that has, if possible, to be identified and supported by appropriate public policies”. The performance or productivity gap “reflects the difference between the reality of performance in a service economy and performance as measured by the traditional economic tools (i.e. productivity and growth)” (Djellal & Gallouj, 2010, p.8). According to these authors, this performance gap finds its roots in economic thought and, more precisely, in the work of Smith who “compared the productive work involved in manufacturing with the unproductive work involved in services, which vanished at the very moment they are produced” (Djellal & Gallouj, 2010, p. 8). This view emphasizes the intangible features of services, which render their measurement more challenging when compared to traditional, tangible outputs such as goods. According to Vargo and Lusch (2004), this disregard of innovation in services by academics is attributable to the traditional good‐centred dominant logic, concentrating on tangible resources, transactions and production processes, which remains predominant in economics and business thinking. Conversely, the dominant logic of service‐dominated economies should be focused on intangible resources, relationships, and production processes that co‐create value through performance (Chesbrough, 2011; Vargo & Lusch, 2004). Furthermore, a “one‐size‐fits‐all” approach may not be appropriate to explore innovation in services, as services embrace a wide variety of sub industries, which differ according to e.g. the degree of knowledge‐ intensiveness that they require to operate. In their empirical study using CIS3 across European countries and some associated states, Vence and Trigo (2009) shed light on the main discrepancies among innovation patterns across service sector firms. Their analysis indicates that the ratio of personnel engaged in R&D activities as a share of total employment ranges from relatively low in wholesale trade and commission trade to very high in the business services subsector. Only 2% of personnel is engaged in R&D activities in innovative firms belonging to the financial intermediation subsector. Furthermore, innovation and R&D expenditures, which mostly consist of acquisition of machinery, software and equipment, to total turnover are estimated as very low (Vence & Trigo, 2009). This observation is in contrast with the undeniable innovative character of this subsector. Business services, which are highly innovative, tend to have high innovation expenditures, conduct massively intramural R&D and mobilize a significant share of their highly qualified employees for R&D activities. Their study also shows that service firms in general have a higher propensity to cooperate in the innovation process than their manufacturing counterparts, and this is particularly true for business and financial intermediation subsectors. These debates and figures further ignore the servitization of manufacturing industries, which refers to the increasing trend of manufacturing firms to deliver offerings that are bundles of products and services. In his recent contribution entitled “Open Services innovation”, Chesbrough (2011) further stresses the need for manufacturing firms to shift from a product‐oriented business model towards a service‐mindset to escape the commodity trap and maintain their competitiveness. Although numerous case studies support this evidence of joint product and service offerings and exemplify it (e.g. the Xerox case in Chesbrough, 2011), data regarding new business models embracing bundles of products and services are scarce, if at all existing.

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3. Methodology In the review process, the authors adopted the principles of a systematic review as recommended by Jesson et al. (2011) namely: 1) Mapping the field through a scoping review, 2) Comprehensive search, 3) Quality assessment, 4) Data extraction, 5) Synthesis, and 6) Write up. First, a research plan was developed comprising the research questions of interest, the keywords, and a set of inclusion and exclusion criteria. The paper’s aim was to determine the current status of research in services innovation measurement. Articles that included the keywords “innovation”, “services” and “measure” in the abstract were included. Additionally, inclusion and exclusion criteria were specified. The inclusion criteria were: publications in the period 2006‐2012, peer‐reviewed academic papers, English language. Papers published prior 2006; grey literature such as reports, books and non‐academic research; and other languages than English represented exclusion criteria. Moreover, an excel data sheet was produced consisting of key aspects related to the research aim. In the given case these were: name of author(s), year of publication, research aim/objectives, theoretical perspective/ framework, method, main findings, and name of the journal. Second, once all relevant issues had been specified, both authors accessed ProQuest and looked for suitable articles. This resulted in 82 hits. The third step consisted of two procedures. Firstly, both authors worked through the abstracts to make sure that they actually covered the pre‐defined scope. This procedure yielded a final selection of 16 articles. Secondly, the 16 papers were divided among the two authors; thus each author read 8 papers. This procedure resulted in a further reduction of the number of papers. In the end, the authors reached a final selection of eight articles which fulfilled the criteria set and thus represented the basis for analysis. The search took place on October 23, 2012. Fourth, both authors read the eight remaining papers and subsequently entered the relevant data regarding the research aim in the excel sheet. Then both authors jointly went through each data entry and discussed the content. This approach helped to alleviate the risk of any inconsistency in the analysis and the conclusion drawn there from. Fifth, the final excel sheet was jointly discussed. This discussion enabled the authors to categorize the findings under themes which, in turn, helped to clarify what is known about the measurement of services innovation and to which areas the body of knowledge is limited. Sixth, the final stage of the review process was devoted to writing up the findings.

4. Presentation of findings 4.1 Studies involved The eight papers that formed the basis for our analysis are summarised in Table 1. The oldest publication is from 2009 and the most recent ones are from 2012. The majority of papers have been published in 2011, indicating rising empirical research activities.

4.2 Observations With regard to the methodology, the reviewed studies make use of a broad range of methods. Some authors used a survey approach (e.g., Ko & Lu, 2010; Steinicke et al., 2012), some based their study on secondary data sources (i.e., Aas & Pederson, 2011), and one employed a qualitative approach (i.e., Tajeddini, 2011). What is striking is the number of papers that used mixed methods approaches (e.g., Song et al., 2009; Abreu et al., 2010; Den Hertog et al., 2011; Gotsch & Hipp, 2012).

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Susanne Durst and Anne‐Laure Mention Table 1: Overview of empirical papers involved in the literature review

Song, Song & Di Benedetto

Author(s)

2010

2009

Year

Main findings Results suggest that performance is boosted if the new service provider both (i) follows a staged traditional NSD process and (ii) provides prelaunch SERVQUAL training. The authors found an unexpected significant negative relationship between proficiency of business and market opportunity analysis and idea screening, service design, and service testing. Call for alternative metrics. These metrics should meet four criteria – accuracy, longevity, comparability and ease of collection (NESTA, 2007). Any new metrics need to be used sensitively, so that they do not distort policy.

Abreu et al.

2010

Research aim/objectives Method (empirical / theoretical) Involved four in-depth case studies conducted in service development teams To develop and empirically test a new staged of four well-known companies and a service innovation model (SIM) survey approach which addressed five service industries Using the Fourth UK Community To analyse what innovation in services means and Innovation Survey (CIS4) data and the how it can be measured results of a case study analysis

Ko & Lu

Firms focusing on service innovation have significantly higher productivity (sales revenue per employee) growth than firms not focusing on service innovation. The increased sales revenues resulting from service innovation in service firms seem to be neutralized by increased costs, meaning that these firms are unable to benefit financially, in terms of operating result growth, from their innovation activities. our findings also indicate that profitability, defined as the operating result divided by asset, is not influenced by firms’ focus on service innovation activities.

Study shows a positive effect of innovativeness on OE (operating effectiveness) and CE (cost efficiency). The results reveal that a favorable combination of CE (i.e. greater productivity) and OE(i.e. superior service quality) produces a better performance, gaining higher profit goal achievement, sales goal achievement and ROI achievement.

Survey approach; initially disseminated among 120 individuals from Taiwan (who worked in communications-related To gain insight into firms’ innovation competencies The findings suggest that competencies can be measured as a five-dimensional companies and were responsible for and to develop an instrument to examine the key construct consisting of industry-specific, product-related, market-related, developing innovative services) who innovation competencies that contribute to attended a forum of innovative services. technology-related, and organization-related. integrated services. Secondly, more questionnaires were distributed within the organizations of the individuals.

Data from Community Innovation Survey (CIS2006) from Norway and a set of economic accounting data from the Norwegian Register of Company Accounts.

Questionnaire approach; personal interviews with owner of 211 Iranian restaurants

2011

Tajeddini

2011

To investigate if firms focusing on service innovation perform better financially than firms not focusing on service innovation. Two research questions posed: Do firms in (1) the service industries and (2) the manufacturing industries focusing on service innovation activities in the period 2004–2006 perform better financially in the following year (2007) than firms not focusing on such activities?

To examine potential influences of innovativeness on effectiveness and efficiency and their subsequent effects on restaurant business performance

Den Hertog, Gallouj & Segers

2012

2011

Gotsch & Hipp

2012

Aas & Pederson

Steinicke, Wallenburg & Schmoltzi

Innovation in most service industries is less formalized, less explicitly managed Attempts to measure technological and and less often budgeted as compared with innovative manufacturing firms. In nontechnological innovation, organizational aspects Based on 12 expert interviews and a terms of firm performance, telephone survey of the innovation process in the Dutch hospitality it is signalled that the impact of innovation should be perceived more widely and industry and their link to firm performance. also includes non-financial impacts. Trademarks contribute to explaining KI(B)S innovation and seem to be a suitable Data from the German section of the ‘Community Innovation Survey’ are used, innovation indicator for these types of firms. The findings further indicate that To explore how trademarks could be established and a survey with 278 participating firms trademark registration is an adequate innovation indicator in KIBS industries, at as an additional indicator for service innovation least for product innovations. is conducted. Both formal and relational governance help to promote coordination and mitigate opportunism among cooperation partners to create the setting necessary for innovation. The authors were able to confirm the proposed predominant role of Collected primary data from service companies via a key informant approach relational governance in service cooperations. This is a differentiating aspect between the service and manufacturing sectors, in the sense that formal (Web-based survey) governance is assumed to be of higher importance in cooperations of product companies. How can governance mechanisms be utilized to foster innovativeness in horizontal service cooperations in order to enhance cooperation performance?

Journal

Decision Sciences

Research Policy

Journal of Service Management

Education, Business and Society: Contemporary Middle Eastern Issues

The Service Industries Journal

Journal of Service Management

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Susanne Durst and Anne‐Laure Mention Abreu et al., (2010) highlighted, in their study, the need to think of alternative measures, clearly indicating that traditional metrics developed for product innovation cannot be directly transferred to services innovation. The authors further stressed the significance of considering the aim that different metrics should fulfil to make sure that they actually comply with expectations. In the same line, Den Hertog et al. (2011) argued that in the context of measuring the impact of innovations one needs to take into account any non‐financial impact as well. This demonstrates that a one‐sided perspective on financial impacts limits our way to develop new and deeper knowledge of innovation measurement in general and services innovation measurement in particular. This finding echoes those of Mention (2011a and b), who contended that adopting an intellectual capital perspective to capture the peculiarities of innovation in services may provide interesting insights to illuminate the firm‐level effects of innovation in services. Table 1 further underlines that regarding the journals involved, an emphasis on specialist journals is found. This is understandable against the novelty of the topic, but in order to achieve a wider acceptance, a broader selection of journals should follow in future.

5. Conclusions The aim of this paper was to identify the body of scholarship regarding services innovation measurement as found in peer‐reviewed academic papers. It is important to better understand what we know about this topic, because it has a great influence on economic development. Our literature search resulted in eight empirical studies. Current research in the area under investigation seems to be primarily driven by some researchers’ personal interests. It can be thus concluded that the existing literature provides only rather fragmented insights into the measurement of services innovation. Given the assumed importance of services innovation, there is a need for more intense research activities. This would also help to underpin the legitimacy of services innovation as a research field on its own, and to some extent, provides some support to the willingness to establish a new field of research, service science, which is defined as “the combination of technology innovation, business model innovation, social organisational innovation and demand innovation with the objective to improve existing service systems, create new value propositions and offerings or create new service systems” (University of Cambridge Institute, 2008). This situation clearly underlines that the topic offers scholars a variety of research avenues. We definitely need metrics that allows managers to measure the impact and outcome of services innovation. Thereby the metric must consider the particular nature of services. It is rather doubtful if metrics originally developed for product innovations can fulfil this requirement. Since the service sector is rather broad, which may lead to different needs regarding the metrics, researchers interested in developing the field should have this in mind. In addition to it, the metrics to be developed should be able to incorporate the different actors, e.g. organizations, entire industries, who contributed to the development of services. This would help to better understand the contributions of each actor to development, which in turn would take into account the increasing interconnectivity of market actors. The growing use of social media can also be regarded as a good starting position for the development of metrics. Many consumers share their opinions and experiences regarding services with their social network. Organizations could build on this engagement by monitoring the success of the service. Suitable metrics would help the individuals in charge of services innovation measurement by addressing those areas that need immediate actions. Djellal and Gallouj (2010) advocate for a multidimensional approach to performance, which should include multiple criteria referring to “commercial performance […], civic performance […] and relational performance […].” (Djellal & Gallouj, 2010, p. 10). The concept of intellectual capital may also provide a strong basis to define metrics to capture the peculiarities of innovation in services and of service innovation, as discussed in Mention (2011b). The present study is not without limitations. A complete coverage of all the empirical articles considering the measurement of services innovation could not have been achieved, given the search proceeding chosen. So it may have left out papers that also addressed the topic but used a different language. Yet, it seems reasonable

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Susanne Durst and Anne‐Laure Mention to assume that the review process covered a large proportion of the studies available. Finally, this paper proposes some research directions which are not exhaustive but represent initial stages.

References Adams, R., Bessant, J. and Phelps, R. (2006) "Innovation management measurement: A review", International Journal of Management Reviews, Vol. 8, Iss. 1, pp. 21‐47. Aas, T. H. and Pedersen, P. E. (2011) "The impact of service innovation on firm‐level financial performance", The Service Industries Journal, Vol. 31, No. 13, pp. 2071‐2090. Abreu, M., Grinevich, V., Kitson, M. and Savona, M. (2010) "Policies to enhance the ‘hidden innovation’ in services: evidence and lessons from the UK", The Service Industries Journal, Vol. 30, No. 1, pp. 99–118. Chesbrough, H. (2011) Open Services Innovation: Rethinking Your Business to Grow and compete in a New Era, Jossey Bass, San Francisco. Den Hertog, P. (2000) “Knowledge‐Intensive Business Services as Co‐Producers of Innovation”, International Journal of Innovation Management, Vol. 4, No. 4, pp. 491‐528. Den Hertog, P., Gallouj, F. and Segers, J. (2011) "Measuring innovation in a ‘low‐tech’ service industry: the case of the Dutch hospitality industry", The Service Industries Journal, Vol. 31, No. 9, pp. 1429‐1449. Djellal, F. and Gallouj, F. (2010) “Services, innovation and performance: general presentation”, Journal of Innovation Economics, Vol. 5, No. 1, pp. 5‐15. Gotsch, M. and Hipp, C. (2012) "Measurement of innovation activities in the knowledge‐intensive services industry: a trademark approach”, The Service Industries Journal, Vol. 32, No. 13, pp. 2167‐2184. Jesson, J. K., Matheson, L. and Lacey, F. M. (2011) Doing your literature review: Traditional and systematic techniques, Sage, Los Angeles. Ko, H.‐T. and Lu, H.‐P. (2010) "Measuring innovation competencies for integrated services in the communications industry". Journal of Service Management, Vol. 21, No. 2, pp. 162‐190. Mention, A.‐L. (2011a) “Intellectual Capital, Innovation and Performance: a Systematic Review of the Literature”, Business and Economic Research, Vol. 2, No. 1, pp. 1‐37. Mention, A.‐L. (2011b) “The impact of Innovation on Performance in Services: Disentangling Effects Through the Lenses of Intellectual Capital”, European Conference on Intellectual Capital, Helsinki, 2012. Salter, A. and Tether, B. S. (2006) Innovation in services: through the looking glass of innovation studies, background paper for Advanced Institute of Management. Research’s Grand Challenge on Service Science, April, 7th. Song, L. Z., Song, M. and Di Benedetto, C. A. (2009) "A Staged Service Innovation Model", Decision Sciences, Vol. 40, No. 3, pp. 571‐599. Steinicke, S., Wallenburg, C. M. and Schmoltzi, C. (2012) "Governing for innovation in horizontal service cooperations", Journal of Service Management, Vol. 23, Iss. 2, pp. 279 – 302. Tajeddini, K. (2011) "The effects of innovativeness on effectiveness and efficiency", Education, Business and Society: Contemporary Middle Eastern Issues, Vol. 4, No. 1, pp. 6‐18. Tipu, S. A. A. (2011) "Academic publications on innovation management in banks (1998–2008): A research note", Innovation: Management, Policy & Practice, Vol. 13, Iss. 2, pp. 236–260. University of Cambridge Institute. (2008) “Succeeding through service innovation: a service perspective for education, research, business and government”, White Paper, April 2008. Vargo, S. and Lusch, R. (2004) “Evolving to a new dominant logic for marketing”, Journal of Marketing, Vol. 68, pp.1‐17. Vence, X. and Trigo, A. (2009) “Diversity of innovation patterns in services”, Service Industries Journal, Vol. 29, No. 12, pp. 1635‐1657.

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Socio‐Ecological Innovation: Strategic Integration of Innovation for Sustainability and Sustainable Innovation Rick Edgeman and Jacob Eskildsen Aarhus University, Aarhus, Denmark rledge@asb.dk eskildsen@asb.dk Abstract: Socio‐Ecological Innovation or SEI is innovation resulting from strategic integration of sustainable innovation and innovation for sustainability. In particular SEI is regarded as critical to organizations intent on progressing toward Sustainable Enterprise Excellence (SEE) and, indeed, progressing toward the asymptotic goal of becoming a continuously relevant and responsible organization (CR2O). Sustainable Innovation is something that is attained only when innovation in an enterprise is regular, systematic, and systemic to the endeavors of the enterprise itself – that is – Sustainable Innovation is part of the enterprise cultural fabric, is foundational to enterprise strategy, and contributes to the financial security of the enterprise. Innovation for Sustainability is innovation that is specifically targeted to address ecological and / or societal considerations. That is, Innovation for Sustainability is (generally) a subset of the enterprise innovation portfolio. Effective and efficient integration of Innovation for Sustainability and Sustainable Innovation aids translation of 3E (equity, ecology, economy) enterprise strategy of the Triple Top Line into 3P (people, planet, profit) Triple Bottom Line enterprise results in operationalization of the cradle‐to‐cradle product and service design, delivery, and lifecycle philosophy. While SEI is defined, it must also be assessed. Any serious assessment of SEI requires not only understanding of what it is, but also how it manifests and in what forms, how developed or mature it is, and how future SEI strategy and results can be improved. As an aid to these efforts, non‐prescriptive approaches to SEI are discussed, maturity scale assessment for SEI is developed and discussed, and a simple assessment report that combines graphic and narrative feedback is presented. The assessment report is referred to as a SEI News Report and is intended not only to provide feedback to the organization concerning its present SEI performance, but also to deliver significant foresight that will inform future SEI efforts, strengthening the likelihood of implementing not only best practices, but next best practices and sources of competitive advantage. Keywords: enterprise excellence, innovation for sustainability, maturity assessment, sustainable innovation, sustainable enterprise excellence, SWOT analysis

1. Background In essentially any context sustainability may be regarded as a capacity to endure. As a contemporary manifestation sustainability in a larger economic context is generally regarded as a composition of three primary domains broadly categorized as financial, the natural environment, and the human dimension. This construct of sustainability stems from a macro‐level view of development provided in the report of a United Nations commission chaired by Dr. Gro Harlem Bruntland, former Director General of the World Health Organization (WHO) and former Prime Minister of Norway. The report, commonly known as “Our Common Future” (WCED 1987) provides a definition of sustainability, the enterprise perspective of which is well captured by the phrase: lean, green, ethical and real (Edgeman and Eskildsen 2012a), where:

Lean refers predominantly to conservation of non‐environmental resources;

Green is associated with conservation of non‐renewable natural resources, wise use of renewable resources, and limitation of environmental footprint;

Ethical is related to commitment to and practice of social equity and justice, community involvement and contribution, and positive regard for treatment of the enterprise’s human capital, and

Real implies lean, green, and ethical practice with concomitant results that include financial, societal, and environmental results.

Sustainability of this composition has been identified as both an emerging megatrend (Lubin and Esty 2010) and emerging source of competitive advantage (Laszlo and Zhexembayeva 2011) wherein effective environmental policy is a documented driver of firm value (Al‐Najjar and Anfimiadou 2012). In particular, innovation has been cited as a key enabler of sustainability (Nidumolu, et.al. 2009) and it is this aspect of innovation that is here of central importance so that what is desired is that innovation should serve as a key enabler of sustainable enterprise excellence (SEE), where

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Rick Edgeman and Jacob Eskildsen SEE is a consequence of balancing both the competing and complementary interests of key stakeholder segments, including society and the natural environment, to increase the likelihood of superior and sustainable competitive positioning and hence long‐term enterprise success. This is accomplished through an integrated approach to organizational design and function emphasising innovation, operational, supply chain, customer‐related, human capital, financial, marketplace, societal, and environmental performance. Just as solid environmental policy is positively correlated to firm value, so also has effective implementation of enterprise excellence models such as the Balanced Scorecard (Kaplan and Norton 1992) or the models supporting America’s Baldrige National Quality Award or the European Quality Award (Balasubramanian et.al. 2005) and various attempts have been made to integrate these approaches (Asif, et.al 2011; Avlonas and Swannick 2009; Carillo‐Hermosilla 2009; Zingales and Hockerts 2003). Of course a key question is how to reasonably integrate environmental policy and practice with enterprise excellence approaches and herein innovation of a particular form is suggested as a partial answer to this question. Innovation is posited as a key integrative thread of the gains realized by effective environmental policy implementation and effective use of enterprise excellence models. Integrative innovation of the form pursued herein, then, is innovation that addresses societal, environmental and financial performance and thus integrates sustainability and enterprise excellence and does so regularly, rigorously, comprehensively, and profitably. We refer to such innovation as socio‐ecological innovation (SEI) and see it as a result of strategic integration of sustainable innovation (Cooperrider 2008) and innovation for sustainability (Edgeman and Eskildsen 2012a). In addition to being a responsibility of enterprise leadership and human capital, innovation advances through activity at the co‐creative interfaces of the enterprise with its customers (Hoffmann 2012) and society (Edgeman and Eskildsen 2012b) or – as expressed by innovation guru Anna Kirah, the first cultural anthropologist hired by Microsoft founder Bill Gates – “innovation is for everyone” (Jokisalo 2008). Among various expressions of the value of such synergies are the following:

“Real learning gets to the heart of what it means to be human. Through learning we re‐create ourselves … become able to do something we were never able to do … re‐perceive the world and our relationship to it … (and) extend our capacity to create, to be part of the generative process of life.” (Senge 1990).

“The biggest barrier to innovation, of course, is a company’s mind‐set … barriers often stump even the best innovators. They wall in your imagination like a prison yard.” (Kelley and Littman 2001).

2. Sustainable innovation: Process and culture It is of value to distinguish between innovation as a creative spark, as a target, and as a process. While the creative innovation spark is widely recognized and valued, the process of innovation may be viewed as a portfolio of opportunities, the most exceptional among which may be identified through use of innovation tournaments (Terwiesch and Ulrich 2009). Exceptional opportunities are “exceptional” only relative to criteria that define exceptional, critical among which is value creation, where value may be non‐monetary in nature and include environmental or societal care and advancement. Given the emphasis herein on SEI it is important to understand how it is that SEI contributes to value creation (Lubin and Esty 2010):

SEI should focus on reducing cost, risks, waste, and delivering proof‐of‐value;

SEI should focus attention on redesign of selected products, processes, or business functions to optimize their performance and hence advance from doing old things in new ways to doing new things in new ways;

SEI should drive revenue growth by integrating innovative approaches into core strategies (Skarzynski and Gibson 2008);

SEI should differentiate the enterprise value proposition through new business models that use innovation to enhance enterprise culture, brand leadership, and other intangibles to secure durable competitive advantage (Sekerka and Stimei 2011).

It is not only effective integration of enterprise excellence approaches and environmental policy that positively impact firm value, as in fact embedding a culture of sustainability also produces multiple positive enterprise impacts (Eccles, et.al. 2011). In question, of course, is the issue of how to create, cultivate, and advance a

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Rick Edgeman and Jacob Eskildsen culture of sustainability. In the views of innovation experts Anna Kirah and Tim Brown, CEO of IDEO ‐ America’s leading innovation and design firm, interdisciplinary collaboration is crucial to such an enterprise culture:

“Change does not happen until different areas within the same company learn to speak the same language as the culture of the people they are innovating for and that they can speak together across disciplines” (Anna Kirah in Jokisalo 2008).

“The increasing complexity of products, services, and experiences has replaced the myth of the lone creative genius with the reality of the enthusiastic interdisciplinary collaborator.” (Brown 2008).

Indeed, Brown (2008) provides a path for embedding co‐creative socio‐ecological innovation in enterprise strategy and culture. Figure 1 provides an expression of Brown’s path, adapted to reflect integration of sustainability and enterprise excellence thinking. Table 1: Embedding socio‐ecological innovation in enterprise strategy & culture Focus SEI Strategy and Actions Innovation from Engage in the innovation process before any direction has been set in order to develop an expanded the start space of potential solutions, thus creating more concept fragments and better likely eventual result. Actively include the “eco‐voice”. People‐centered People‐centered design thinking captures unexpected insights and produces innovation more innovation precisely reflecting consumer wants and societal needs. Directly observe the user environment. Rapid Expect rapid experimentation and prototyping that is carried out with constant consideration for development environmental and societal impacts and sustainable solutions. Measure progress with creativity metrics such as time to first prototype. Co‐creation Expand the enterprise innovation ecosystem through engagement of users and society. This focus supports creation of acceptable and sustainable solutions. Innovation Manage an innovation portfolio that spans short‐term incremental ideas to long‐term evolutionary portfolio ones. Expect business units to drive and fund incremental innovation but be willing to initiate revolutionary innovation from the top of the enterprise. This ensures contribution of innovation to profitability. Requiring that a significant subset of the portfolio have positive ecological or societal ramifications ensures relevant and responsible innovation. Pace of Although innovation is often rapid, commercialization is often unpredictable and sensitive to innovation intellectual property considerations. Complicated budgeting cycles only serve to constrain the pace budgeting of innovation. Be prepared to reallocate budget with great agility as opportunities arise. Talent Human capital is a key enabler of both SEI and SEE. Build enterprise human capital with capitalization interdisciplinary talent and orientation. Provide innovation, design, and sustainability training strategically throughout the enterprise. When well‐executed this strategy ensures that more creative and diverse concepts and solutions surface. Mandate that a significant number of implemented solutions yield demonstrable societal or environmental results. Design for the Ensure appropriately rapid rotation of human capital in ways that provide experience across the cycle inspiration to ideation to implementation cycle. Experiencing the full cycle generates better judgment, creating long‐term benefits for the enterprise, including better understanding of how innovation impacts all sustainability dimensions.

Part of creating a culture of innovation and sustainability is to “make it contagious”. In this regard adaptation of “tipping point” philosophy (Gladwell 2008) suggests that the enterprise should be peopled by connectors, mavens, and salesmen where connectors build bridges across key parts of the enterprise, mavens supply creative energy, and salesmen convincingly deliver the message of value related to innovation and sustainability efforts and results. It is of course possible that a given individual might at different times assume differing and possibly multiple simultaneous roles. While Brown (2008) provides a concrete multi‐faceted SEI strategy, and Gladwell (2008) suggests enterprise human capital composition, Grayson, et.al. (2008) identify a means by which SEI strategy can be deployed. This approach is referred to as S2AVE (Shareholder and Social Added Value and Environment Restoration). Collectively these provide a “360 degree” approach (Hollingworth 2009) to integration of SEI within and throughout the enterprise across all functional areas, activities, and results. S2AVE underscores that enterprises can successfully, profitably, and simultaneously transform triple top line (McDonough and Braungart 2002a) 3E strategy into triple bottom line (Elkington 1997) 3P results and also become increasingly agile and innovative while doing so. The steps of S2AVE as adapted from Grayson, et.al. (2008) may be described as follows:

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Make innovation for sustainability integral to enterprise vision;

Place SEI at the core of enterprise strategy;

Integrate SEI into all parts of the enterprise;

Emphasize actions and results over rhetoric;

Architect a board level body with the power and will to make SEI make a difference;

Set concrete rules, fulfillment of which may be measured and monitored;

Engage key stakeholders and gain their support;

Use the individual talents and collective power of the enterprise’s human capital;

Actively participate in key networks; and

Govern and act beyond reporting (Hubbard 2009) by aligning all business systems with enterprise SEI vision.

3. SEI innovation actions and strategies SEI integrates social innovation and institutional entrepreneurship research with research on socio‐ecological systems and resilience thinking (Folke 2006). In this regard the perspectives of Olsson and Galaz (2011) may be adapted, with adaptation suggesting that SEI should:

Integrate 3E / 3P aspects;

Enrich human life and society without contributing to erosion of life‐supporting ecosystems;

Simultaneously address the equity‐people and ecology‐planet triple top line – triple bottom line connections with the intent of contributing to solutions to specific challenges without creating new ones;

Occur at the co‐creative enterprise‐culture and enterprise‐user interfaces by integrating the creativity and ingenuity of users, workers, consumers, citizens, activists, businesses and others.

Olsson and Galaz (2011) also suggest that SEI should work more directly toward social justice, poverty alleviation, environmental sustainability, and democracy than toward profits for individuals. It is of course possible to suggest a large number of both generic and context specific strategies and actions supportive of socio‐ecological innovation. Suggested herein are ten basic and ten advanced SEI strategies and actions that may be referred to as the 10R and 10A actions and strategies, respectively. Among the 10R actions and strategies are the familiar “reduce, reuse, recycle” call for environmental responsibility found on bumper stickers on American automobiles. Remaining 10R strategies and actions include replace, rethink, redirect, renew, reconsider, redesign, and reinvest. More complex and strategically oriented areas can be found among the 10A areas that include business model innovation, support for innovation, innovation insight, innovation foresight, innovation competencies and technologies, innovation readiness, new product and service innovation and design, socio‐ecological innovation strategy, and systematic change integration. The 10A strategies and actions are described in Table 2. Table 2: SEI 10A advanced enterprise level socio‐ecological innovation elements Element Description Socio‐Ecological The enterprise has explicitly identified areas of and goals for socio‐ecological innovation in Innovation Strategy relation to both revenue and reinvestment. (A1) Innovation Support Leadership encourages and supports a culture of cross‐disciplinary collaboration and co‐ (A2) creation in its development of products, processes, and service solutions. Innovation The enterprise lucidly understands its core competencies and technologies, actively engages Competencies and in identification of areas in which core competencies and technologies must be further Technologies (A3) enhanced or acquired, and has clearly linked these to its short‐and‐long‐term innovation strategies that are informed both by its innovation insight and foresight. Socio‐Ecological The enterprise cross‐functionally and collaboratively coordinates and otherwise empowers, Innovation Capacity and mobilizes its innovation competencies for action in proportions appropriate to (A4) innovation opportunities and needs. These resources are committed to development (e.g. fulfillment) of such needs and opportunities. Innovation Insight Enterprise actively engages customers, society, and surrogates for the ecological voice in the

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Rick Edgeman and Jacob Eskildsen Element (A5) Innovation Readiness (A6) New Product & Service Innovation & Design (A7)

Description innovation process as a means of identifying both articulated and unarticulated needs and, subsequently, fulfilling those needs. The enterprise has sufficient innovation and design competencies and technologies.

The enterprise has explicit targets for amount or proportion of its revenue deriving from new or recent product and service introduction, as well as targets for reinvestment of profits in research and development (e.g. new products and services innovations and designs). Innovation Foresight The enterprise actively, systematically, rigorously, and strategically engages in sensing of (A8) socio‐ecological and other innovation needs, trends, and opportunities. The enterprise also actively seeks to uncover and understand threats and risks associated with socio‐ecological and other innovation needs and opportunities. Business Model Enterprise systematically challenges business model assumptions, incorporating learning in Innovation (A9) its product and service development strategies and processes. Systematic Change Enterprise strives for systemic enterprise‐wide implementation and change and allocates Integration (A10) resources needed to accomplish this. Learning from these, together with foresight activities inform future strategy and business model innovation.

Figure 1 presents the 10A actions and strategies organized in order to illustrate SEI flow. In particular Figure 1 illustrates that the enterprise should be prepared for innovation in the sense that it has an innovation strategy, has dedicated support, has the needed competences and supporting technology, has other necessary capacity for SEI, and possesses sufficient insight. The prepared enterprise is then ready to engage in SEI with results that include generation of new foresight leading to business model innovation and change that is systematically integrated into enterprise strategy and culture. Foresight and intelligence then informs the next innovation cycle. In this way innovation for sustainability is embedded in a culture of sustainable innovation: SEI.

Figure 1: SEI advanced strategies and actions (10A) flow It should not be forgotten that the user‐community and broader society, along with a surrogate eco‐voice are integrated into this process. SEI thus implies that driving 3E triple top line strategy through the enterprise to produce superior 3P triple bottom line results aims to deliver economic, social, and environmental efficiency and effectiveness. As such, and in support of SEE, the enterprise should be skilled at marketing and branding, meaning that identification of social, ecological, and market needs is critical to long‐term prosperity and creation of tribes of sustainable consumers. SEI is thus about being lean, green, ethical and real because enterprises and communities rely on healthy, productive ecosystems. One very large‐scale deployment

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Rick Edgeman and Jacob Eskildsen approach based on so‐called quality function deployment (Ficalora and Cohen, 2010) or QFD and that spans the full enterprise ecosystem including its supply chain, customers, society and the natural environment is suggested by Edgeman and Hensler (2005).

4. SEI maturity assessment and foresight Enterprise progress toward SEI and subsequently toward SEE and becoming a CR2O should not be tacitly assumed, but rather rigorously, routinely and systematically assessed and advanced. Such activities fall within the general purview of organizational self‐assessment, an excellent tome on which is provided by Conti (1997), with a more practically oriented discussion appearing in Porter and Tanner (2012). In general self‐assessment is intended to accomplish two primary tasks, the first of which is to provide detailed feedback to the enterprise regarding its strategy, activities and results with respect to the assessed areas and the second of which is to suggest next best practices and sources of competitive advantage. Descriptive maturity scales for both SEI 10R and SEI 10A strategies and actions providing assessment on a 0‐to‐ 10 basis are available from the authors with 0 or 1 representing very low maturity, 2 or 3 representing low maturity, 4 or 5 or 6 associated with moderate maturity, 7 or 8 with high maturity, and 9 or 10 with very high maturity. High maturity achievement is ordinarily associated with industry leader status whereas very high maturity represents world‐class performance. Still, it is important to descriptively detail what such maturity levels entail. It is also of value to note that these 10 dimensions will generally be of unequal importance and priority to the enterprise and the overall SEI 10R maturity will hence not typically be derived from a simple average or sum of the maturity values, though such approaches might provide a reasonable approximation of overall SEI 10R maturity. If such a summary is desired, then a more reasonable approach would simply be to weight the 10 “R” areas by allocating 10 total points to these areas according to their relative importance and such that each weight is non‐negative. This approach allows for zero weighting of a given area as would be merited if that area is irrelevant to enterprise competitive context. These weights may then be multiplied by the corresponding assessed value with the resulting products added to provide an overall score, S 10R, that ranges from 0 to 100. Work remains to determine correspondence between these summary values and overall SEI 10R maturity. The same approach used to calculate a summary score of 0 to 100 for SEI 10R maturity may be used to deliver a summary score, S10A, between 0 and 100 for SEI 10A maturity. Hypothetical SEI 10R and 10A maturity charts based on a 0‐to‐10 assessments appear as the top graphs of Figure 3. In each of these charts it is apparent that the larger the envelope enclosed by the 10R and 10A assessment values, the greater the SEI 10R and 10A maturity of the enterprise. Once this process is complete, interested enterprises may differentially weight 10R and 10A with weights WR and WA such that WR, WA > 0 and WR + WA = 1. These weights may be multiplied by the corresponding 0 to 100 summary values, then added to yield an overall SEI maturity rating, S, that is also on a 0 to 100 scale. This process delivers S = WRS10R + WAS10A. Again, the correspondence between a given overall score and overall SEI maturity remains to be descriptively determined. In any event, use of maturity charts and summary values should be augmented by narrative that details strengths, weaknesses, opportunities and threats (SWOT) to the enterprise regarding SEI. In addition to SWOT analysis (Heuer and Pherson 2011) narrative should be directive, indicating actions, changes, tactics or strategies the enterprise should consider implementing or adopting and should also be suggestive of next best practices and sources of competitive advantage. This narrative, including SWOT analysis, will be called a SEI SWOT Plot Narrative. Combining the SEI 10R maturity chart, SEI 10A maturity chart, summary scores (if desired), and SEI SWOT Plot Narratives yields an overall SEI assessment that may be referred to as the SEI News Report. A generic SEI SWOT Plot Narrative is presented in Figure 2 while a generic overall SEI News Report maturity assessment is presented in Figure 3. The form of the SEI News Report is commonly referred to as a performance dashboard (Eckerson 2006).

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Figure 2: Generic SEI SWOT plot narrative

Figure 3: SEI news report dashboard

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Rick Edgeman and Jacob Eskildsen In general an SEI News Report will pinpoint 10R and 10A strengths and weaknesses. A useful modification of the various SEI 10A and SEI 10R maturity charts is to plot benchmark values on each axis of each chart so that the enterprise not only learns more about its own performance, but also its performance relative to its competitive landscape. Such modification reveals the gap between current enterprise practices and performance and industry high‐water marks.

5. Conclusions Socio‐ecological innovation (SEI) results from highly effective and efficient integration of sustainable innovation and innovation for sustainability. Such integration happens neither spontaneously nor accidently, but rather purposefully. Toward that end a theory of SEI has been outlined and a structured approach for building and embedding a culture conducive to SEI introduced. Various basic and advanced SEI strategies and actions have been identified and discussed. In order to assess and advance SEI it is critical that maturity may be measured. In response to this need, a simple approach to assessment referred to as the SEI News Report combines graphical and narrative forms called SEI Maturity Charts and SEI SWOT Plot Narratives was developed. While SEI is in‐and‐of‐itself important, it is also regarded as a critical enabler of sustainable enterprise excellence or SEE. SEE weds sustainability and enterprise excellence with the goal of driving the equity, ecology and economy strategy of the triple top line into people, planet and profit triple bottom line results. Success in this latter domain is consistent with cradle‐to‐cradle philosophy and practice (McDonough and Braungart 2002b) that supports social, environmental, and financial responsibility as part of the asymptotic enterprise objective of becoming a continuously relevant and responsible organization.

References Al‐Najjar, B. and Anfimiadou, A. (2012) “Environmental Policies and Firm Value”, Business Strategy and the Environment, Vol. 21, No. 2, pp 49‐59. Asif, M., Searcy, C., Garvare, R. and Ahmad, N. (2011) “Including Sustainability in Business Excellence Models”, Total Quality Management & Business Excellence, Vol. 22, No. 7, pp 773‐786. Avlonas, N. and Swannick, J. (2009) “Developing Business Excellence While Delivering Responsible Competitiveness”, In J. Eskildsen & J. Jonker (Eds.), Management Models for the Future, pp. 171‐184, Springer, Berlin. Balasubramanian, S., Mathur, I. and Thakur, R. (2005) “The Impact of High‐Quality Firm Achievements on Shareholder Value: Focus on Malcolm Baldrige and J.D. Power and Associates Awards”, Journal of the Academy of Marketing Science, Vol. 33, No. 4, pp 413‐422. Brown, T. (2008) “Design Thinking”, Harvard Business Review, June, pp 84‐92. Carillo‐Hermosilla, J., del Rio Gonzalez, P. and Konnola, T. (2009) Eco‐Innovation: When Sustainability and Competitiveness Shake Hands, Palgrave Macmillan, New York. Conti, T. (1997) Organizational Self‐Assessment, Chapman & Hall, London. Cooperrider, D. (2008) “Sustainable Innovation”, BizEd, July‐August, pp 32‐38. Eccles, R., Ioannis, I. and Serafeim, G. (2011) “The Impact of a Corporate Culture of Sustainability on Corporate Behavior and Performance”, Harvard Business School Working Paper 12‐035, November 25, 2011, Harvard Business School, Boston. Eckerson, W. (2006) Performance Dashboards: Measuring, Monitoring, and Managing Your Business, John Wiley, New York. Edgeman, R. and Eskildsen, J. (2012a) “Viral Innovation: Integration via Sustainability and Enterprise Excellence”, Journal of Innovation and Best Business Practice, Vol. 2012, 13 pp., DOI: 10.5171/2012.361451 4 Edgeman, R. and Eskildsen, J. (2012b) “The C Model of People‐Centered Innovation: Culture, Consciousness, and Customer‐Centric Co‐Creation”, Journal of Innovation and Best Business Practice, 2012, 14 pp. DOI: 10.5151/2012.932564 Edgeman, R. and Hensler, D. (2005) “QFD and the BEST Paradigm: Deploying Sustainable Solutions”, World Review of Science, Technology and Sustainable Development, Vol. 2, No. 1, pp 49‐59. st Elkington J. (1997) Cannibals with Forks: The Triple Bottom Line of 21 Century Business, Capstone Publishing, Oxford UK. Ficalora, J. and Cohen, L. (2010) Quality Function Deployment and Six Sigma: A QFD Handbook, 2E, Prentice Hall Publishing, Upper Saddle River, NJ. Folke, C. (2006) “Resilience: The Emergence of a Perspective for Social‐Ecological Systems Analyses”, Global Environmental Change, Vol. 16, pp. 253‐267. Gladwell, M. (2008) The Tipping Point: How Little Things Can Make a Big Difference, Little Brown Publishers, New York. Heuer R. and Pherson R. (2011) Structured Analytic Techniques for Intelligence Analysis, CQ Press, Washington, DC.

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Rick Edgeman and Jacob Eskildsen Hoffmann, E. (2012) User Integration in Sustainable Product Development: Organisational Learning through Boundary‐ Spanning Processes, Greenleaf Publishing, Sheffield, UK. Hollingworth, M. (2009) ”Building 360 Organizational Sustainability”, [online] Ivey Business Journal, November‐December, http://www.iveybusinessjournal.com/topics/global‐business/building‐360‐organizational‐sustainability#.UJUik2nuVJg. Hubbard, G. (2009) ”Measuring Organizational Performance: Beyond the Triple Bottom Line”, Business Strategy and the Environment, Vol. 19, pp 171‐191. Jokisalo, E. (2008) “Innovation is for Everyone. Learning is for Everyone: an Interview with Anna Kirah”, eLearning Papers, No. 8. ISSN: 1887‐1542 [online], Retrieved October 20, 2009 from: www.elearningpapers.eu. Kaplan, R.S. and Norton, D.P. (1992) “The Balanced Scorecard: Measures that Drive Performance”, Harvard Business Review, January‐February, pp 71‐79. Kelley, T. And Littman, J. (2001) The Art of Innovation, Currency Doubleday, New York. Laszlo, C. and Zhexembayeva, N. (2011) Embedded Sustainability: the Next Big Competitive Advantage, Greenleaf Publishing, Sheffield, UK. Lubin,, D. and Esty, D. (2010) “The Sustainability Imperative”, Harvard Business Review, May, pp 2‐10. McDonough, W. and Braungart, M. (2002a) “Design for the Triple Top Line: New Tools for Sustainable Commerce”, Corporate Environmental Strategy, Vol. 9, No. 6, pp 251‐258. McDonough W. and Braungart M. (2002b) Cradle to Cradle: Remaking the Way We Make Things. North Point Press, New York. Nidumolu, R., Prahalad, C. K., & Rangaswami, M. R. (2009) “Why Sustainability is now the Key Driver of Innovation”, Harvard Business Review, September, 57‐64. Olsson, P. and Galaz, V. (2011) “Social‐Ecological Innovation and Transformation”, in A. Nicholls and A. Murdoch (Eds.), Social innovation: Blurring Boundaries to Reconfigure Markets (pp. 223‐247). Palgrave MacMillan, London. Parrish, B.D. (2007) “Designing the Sustainable Enterprise”, Futures, Vol. 39, No. 7, pp 846‐860. Porter, L. and Tanner, S. (2012) Assessing Business Excellence, 2e, Elsevier Butterworth‐Heinemann, Oxford, UK. Sekerka, L. and Stimei, D. (2011) “How Durable is Sustainable Enterprise? Ecological Sustainability Meets the Reality of Tough Economic Times”, Business Horizons, Vol. 54, No. 2, pp 115‐124. Snge, P. (1990) The Fifth Discipline: the Art & Practice of the Learning Organization, Currency Doubleday, New York. Skarzynski, P. and Gibson, R. (2008) Innovation to the Core: a Blueprint for Transforming the Way Your Company Innovates, Harvard Business Press, Boston. erwiesch, C. and Ulrich, K. (2009) Innovation Tournaments, Harvard Business Press, Boston. WCED (1987) Our Common Future, World Commission on Environment and Development, Oxford University Press: Oxford, UK. Zingales, F. and Hockerts, F. (2003) “Balanced Scorecard and Sustainability: Examples from Literature and Practice”, [online] Center for the Management of Environmental Responsibility Working Paper Series 2003/30/CMER, INSEAD, http://flora.insead.edu/fichiersti_wp/inseadwp2003/2003‐30.pdf

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Sustainable Enterprise Excellence: The Springboard Model and Assessment Rick Edgeman and Jacob Eskildsen Aarhus University, Aarhus, Denmark rledge@asb.dk eskildsen@asb.dk Abstract: Sustainable Enterprise Excellence (SEE) results as a consequence of balancing both the competing and complementary interests of key stakeholder segments, including society and the natural environment, to increase the likelihood of superior and sustainable competitive positioning and hence long‐term enterprise success that is synonymous with continuously relevant and responsible governance, strategy, actions and results. This is accomplished through an integrated approach to organizational design and function emphasising innovation, operational, supply chain, customer‐ related, human capital, financial, marketplace, societal, and environmental performance. The intent of this approach is to ethically, efficiently and effectively (E3) integrate 3E (equity, ecology, economy) Triple Top Line strategy throughout enterprise culture and activities to produce Triple Bottom Line 3P (people, planet, profit) results that are simultaneously pragmatic and innovative.Many factors critical to SEE are treated seriously by the balanced scorecard or by organizational excellence models such as those behind notable international quality awards. Other such factors are addressed by, for example, the Global Reporting Initiative or in the annual Communication of Progress reports expected of United Nations Global Compact members. Various approaches to production of a single integrated model have been tried, all with limited success. A model of a first kind adds a sustainability module to an excellence model, a model of a second kind adds an excellence module to a sustainability model, and a model of the third kind aims at integration from the start. A model of the third kind referred to as a Springboard to SEE is proposed that explicitly integrates sustainability and excellence from strategy through results and back to strategy, that is, continuously. The Springboard aims to translate the 3E Triple Top Line strategy into 3P Triple Bottom Line results. Enterprise success in this effort must be assessed in order to provide performance feedback, suggest necessary changes, and point the organization toward next best practices and sources of competitive advantage. Toward that end Springboard assessment uses maturity scales and a SEE NEWS Report that combines graphical NEWS Compasses and companion SWOT Plot Narratives that focus on enterprise strengths, weaknesses, opportunities and threats relative to SEE performance. Sustainability in essentially any context may be regarded as a capacity to endure. As with many concepts, sustainability has become so vaguely defined and so broadly used that conversations surrounding sustainability often become unfocused, with many voices using words that sound the same, but to which undetected differences in meaning or intent have been attached. It is often only much later, at the point where strategy must be defined and implemented and resources committed, that such differences are revealed. Herein then, sustainability is the capacity to endure. Contextually, enterprise sustainability should manifest as economic viability and contribute to both societal and environmental sustainability. Exceptional enterprises should excel within this context, means for which are suggested. In all the objective is to foster continuously relevant and responsible organizations. Keywords: enterprise excellence, Innovation, enterprise excellence, maturity assessment, NEWS Compass, Springboard to SEE

1. Sustainability, enterprise excellence, and sustainable enterprise excellence From the late 1980s until now, management strategy, policy, and practice have been significantly influenced by convergence and unification of the sustainability and enterprise excellence movements. The enterprise excellence movement is characterized by complex business performance models such as those supporting highly influential international quality awards or the balanced scorecard (Kaplan and Norton 1992). In comparison, the sustainability movement has given birth to widely used standards and approaches as the Global Reporting Initiative (Eccles and Krzus 2010), the ISO 26000 Corporate Social Responsibility Standard (Schwartz and Tilling 2009), or the ISO 14000 Environmental Management Standard (Castka and Balzarova 2008). As regarded in this manner sustainability is commonly reduced to societal and environmental dimensions. Sustainability as cited is well captured by the phrase: “lean, green, ethical and real” (Edgeman and Eskildsen 2012a), implying great care in resource consumption, socially and environmentally constructive policy and practice, transparency of conduct, and “lean and green” results. Thus composed, sustainability has been identified as both an emerging megatrend (Lubin and Esty 2010) and an emerging source of competitive advantage (Laszlo and Zhexembayeva 2011) wherein effective environmental policy is a documented contributor to firm value (Al‐Najjar and Anfimiadou 2012). Effective and efficient implementation of enterprise excellence approaches emphasizing performance across an array of domains has also been documented to

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Rick Edgeman and Jacob Eskildsen contribute to firm value (Balasubramanian, et.al. 2005). Various approaches to integration of enterprise excellence and sustainability have been tried to date, with all experiencing only limited success. Models of a first kind add sustainability to an excellence model (Asif, et.al. 2011). Models of a second kind attempt to successfully incorporate financial performance in corporate social and environmental responsibility (CSER) oriented models (Zwetsloot 2003). Models of a third kind integrate enterprise excellence and sustainability from their beginning with examples including customized balanced scorecards (Hubbard 2009; Zingales and Hockerts 2003), a conceptual model referred to as 3C‐SR (Meehan, et.al. 2006), and the model presented herein referred to as the Springboard to Sustainable Enterprise Excellence where Sustainable Enterprise Excellence or SEE is thusly regarded: SEE is a consequence of balancing both the competing and complementary interests of key stakeholder segments, including society and the natural environment, to increase the likelihood of superior and sustainable competitive positioning and hence long‐term enterprise success that is synonymous with continuously relevant and responsible governance, strategy, actions and results. This is accomplished through an integrated approach to organizational design and function emphasising innovation, operational, supply chain, customer‐related, human capital, financial, marketplace, societal, and environmental performance. The intent of this approach is to ethically, efficiently and effectively (E3) integrate 3E (equity, ecology, economy) Triple Top Line strategy throughout enterprise culture and activities to produce Triple Bottom Line 3P (people, planet, profit) results that are simultaneously pragmatic and innovative. A SEE memory aid is: ∫∫[(E3 Governance)(3E Strategy)]dGdS Ö 3P Results It must be stressed that henceforth and in keeping with the approach taken by America’s Baldrige National Quality Award and the European Quality Award, the Springboard to SEE is non‐prescriptive. This implies that while SEE anticipates particular sorts of results in specified areas, that the important issue of how such performance should be accomplished is left entirely to the enterprise and should be guided by its competitive context and vision for the future. This includes the critical area of innovation where the expectation is that it should result from enterprise focus on integration of sustainable innovation and innovation for sustainability, with innovation occurring relative to the enterprise product and service environment and in its business models. It must be similarly emphasized that enterprise human capital cannot ordinarily simply be “ordered” to produce such results, but that commitment must be otherwise secured with Edgeman and Fraley (2008) producing one roadmap for doing so.

2. Key trends and concepts in relation to sustainable enterprise excellence Along a near parallel timeline with those of the contemporary sustainability and enterprise excellence movements, highly visible corporate scandals of the late 1990s and early 2000s produced a clarion call for governmental action mandating transparency in corporate governance. This contributed to establishment in 2000 of the United Nations Global Compact (Lawrence and Beamish 2012) and, subsequently, to the Sarbanes‐ Oxley Act of 2002 (Lander 2004; Svedin 2012). In the context of sustainable enterprise excellence these influences manifest through formal assessment of enterprise leadership and governance, including the role of these in organizational structure and design. Globalization has elevated competitive pressure and stimulated demand for and value of social and technological innovation, increasing their importance in the design and delivery of products, processes, and services. Globalization has also highlighted the criticality of effective and efficient procurement and distribution of information and materials, increasing the importance of efficient and effective supply chains. Similarly, the emerged reality of “big data” demands consideration – not with respect to enterprise excellence or sustainability alone, but much more pervasively: the need to make sense of massive amounts of information in order to more responsibly formulate and operationalize strategy and deliver results. Significant energy is being devoted to derivation of useful “big data analytics” (Franks 2012) and big data competitive strategy (Davenport and Harris 2007). Enterprises face mounting sustainability challenges that from strategy and operations perspectives represent the 3E triple top line or TTL (McDonough and Braungart 2002a) and from operations and results perspectives

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Rick Edgeman and Jacob Eskildsen are associated with the 3P triple bottom line or TBL (Elkington 1997). More explicitly equity Ö people, ecology Ö planet, and economy Ö profit synapses represent creation of enhanced sustainable societal performance, sustainable environmental performance, and sustainable financial performance. Business reality dictates primacy of the profit domain: an enterprise not sufficiently economically secure will ordinarily contribute poorly to society and will tend to have poor environmental performance. Despite economic primacy, regulatory compliance, societal concerns, consumer demands, and stakeholder expectations have generated significant urgency for sustainable performance in all three domains. Thus far however, rapidly increasing societal and environmental challenges have outpaced organizational capability and capacity to deliver needed results. Capability and capacity are multi‐faceted, with two aspects being enterprise intelligence concerning societal and environmental challenges and the ability to embed innovation of the needed scope, scale, trajectory, and velocity in enterprise strategy, policy, and practice. Innovation has been cited as a key enabler of sustainability (Nidumolu, et.al. 2009) and it is this aspect of innovation that is here of central importance as a driver of sustainable enterprise excellence. Relative to innovation, socio‐ecological innovation (SEI) weds sustainable innovation and innovation for sustainability and is related to the organizational innovation capacity factor – a factor that may be analyzed through the innovation lens of the balanced scorecard (Kaplan and Norton 1992). Sustainable innovation is an element of an enterprise’s cultural fabric wherein innovation is regular, rigorous, systematic and systemic to enterprise strategy and practice (Skarzynski and Gibson 2008). In contrast, innovation for sustainability is innovation that specifically targets societal or environmental impact and contribution (Cooperrider 2008). Innovation that is solely environmentally focused is referred to as eco‐innovation (Carillo‐Hermosilla 2009). Such are some of the many forces at work in our world and their relation to SEE. Seen from an organizational perspective then, a challenge is to formulate and effectively execute strategy leading to continuously relevant and responsible actions and results that benefit all organizational stakeholders, including economic benefit to the organization itself.

3. Convergence SEE coherently connects the kernels of the sustainability and enterprise excellence movements, as a means of delivering responsible competitiveness (Avlonas and Swannick 2009). SEE uniquely employs SEI as a key integrative factor, magnifies the importance of (E3 governance + 3E strategy) Ö 3P results transference, and explicitly addresses the impact of organizational governance and design on performance since function follows design in the same way that results proceed from strategy (Burton, et.al. 2011). In acknowledging the importance of (E3 governance + 3E strategy) Ö 3P results transference it is valuable to recognize that transference occurs not as three one‐to‐one E Ö P relationships of the form equity strategy Ö people results, ecological strategy Ö planet results, and economic strategy Ö profit results. Rather, all three (E3 + 3E) components are part of intertwined strategy with each of equity, ecology, and economy delivering results in all three TBL performance domains. Relative to the key elements of SEE, then, enterprise strategy should be formulated with such interrelationships in mind. Companion to this concept is that there are almost certainly some elements unique to sustainability and other elements unique to enterprise excellence. While some elements unique to sustainability have the sound possibility of being minimized or ignored by an enterprise, the reverse is not true: enterprise excellence is concerned with comprehensive business performance. Sustainability effectively constrains the acceptable space within which an enterprise should strive to optimize business performance. Few would argue that pure profit maximization should be pursued at the expense of substantial depletion or unreasonable consumption of a natural resource, or at the expense of eroding the social fabric. Similarly, few would argue that an enterprise should perform without regard for its financial security. Arriving at a reasonable compromise solution – joint optimization – is thus a challenge that may be approached through expanding the envelope of congruence between enterprise excellence and sustainability.

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Rick Edgeman and Jacob Eskildsen Business, societal and environmental trends drive congruence or synergy at some levels, but in general it is here regarded as the responsibility of enterprise leadership and – more pervasively – enterprise human capital to consciously and consistently drive congruence. This mirrors enterprise‐wide quality management philosophy that “quality is everyone’s job” with the parallel argument that “sustainable enterprise excellence is the responsibility of everyone in the organization” (Edgeman and Fraley 2008). While increased congruence is thus associated with SEE, in other than highly specific contexts, complete convergence may be imaginary. Instead there exists a dynamic and conditionally limited convergence envelope that contracts or expands with the influence of changing conditions, with convergence enabled by cradle‐to‐ cradle (McDonough and Braungart 2002b) (E3 + 3E) Ö 3P conversion. As an overall interpretation organizational design and innovation produce greater congruence and greater congruence simplifies alignment of (E3 + 3E) strategy and governance with 3P results. As SEE is defined relative to exceptional business governance (Shahin and Zairi 2007), behavior, and performance it is of value to explicitly embed SEE an organization’s business model and to centrally position SEI within SEE. Relative to our discussion thus far, SEI plays a central role in SEE and hence in any reasonable model of SEE. Edgeman and Eskildsen (2012a) provide a blueprint for embedding socio‐ecological innovation in enterprise culture and have in related work addressed SEI maturity assessment. Large‐scale deployment of SEI – not just within the enterprise, but throughout its ecosystem – is possible through a modified form of quality function deployment (Xie et.al. 2003) developed by Edgeman and Hensler (2005). Socio‐ecological innovation extending throughout an enterprise ecosystem is of particular interest in SEE since it contributes to socio‐ecological resilience (Olsson & Galaz, 2011) where resilience is the capacity of a system to confront challenges and change, yet continue to positively develop. Extending these concepts across multiple and connected enterprise ecosystems contributes to creation of sustainable enterprise economies (Waddock and McIntosh 2011).

4. Springboard assessment of sustainable enterprise excellence It is of course one thing to discuss SEE and quite another to assess it. The SEE system presented herein is called the Springboard to SEE. The Springboard assesses SEE performance in order to accelerate transformation of 3E triple top line strategy into superior triple bottom line 3P results. Springboard assessment will combine six NEWS Compasses with six SWOT Plot NEWS Narratives to yield a combined graphic and narrative NEWS Report of learning and foresight targeted at driving relevant and responsible actions and results. The Springboard is intended to be useful to organizations ranging from novice to sophisticated in their excellence and sustainability experience and expertise. Systems emphasize and assess that which they value: enterprise excellence (1E strategy Ö 1P performance) or sustainability (2E strategy Ö 2P performance). Comparatively, SEE values a transfer of the form that integrates E3 governance and 3E strategy to produce 3P performance. SEE is thus a system of the third kind that aids organizational quest for continuously relevant and responsible strategy, action, and results and demands understanding of and dedication to its core values, principles and criteria. Criteria enable assessment of regular and rigorous assessment of all relevant enterprise activities and results and hence progress (maturity) toward SEE. This anticipates intelligent application of assessment‐driven learning to deliver relevant change and innovation leading to next best practices and sources of competitive advantage. An initial Springboard model based on discussion to this point in provided in Figure 1. The Springboard to SEE Model portrayed in Figure 1 is intended to assess and advance progress toward SEE and hence toward continuously relevant and responsible actions and results through use of sound maturity indices or scales focused on areas relevant to system and enterprise objectives. Sources of possible SEE criteria are abundant and include the definition of SEE itself, the 10 Principles of the United Nations Global Compact (http://www.unglobalcompact.org/), the criteria associated the Global Reporting Initiative (https://www.globalreporting.org/), European Quality Award (http://www.efqm.org/) and America’s Baldrige National Quality Award (http://www.nist.gov/baldrige/) documentation, or various forms of the balanced scorecard.

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Figure 1: Springboard to SEE model The TTL is core to SEE‐motivated organizations and together with E3 governance provides the Springboard’s ethical foundation that informs ‘3E strategy and E3 governance’. This portion of the Springboard reflects key aspects of SEE including transparent, responsible, and (largely) CSER‐driven governance emphasizing sustainability and innovation. This relates to organizational value for intelligence and knowledge and drives agility that is either limited or enabled by the fit of an organization’s design to its needs, along with its capacity for and rapidity with which it is able to adapt and change (resilience). Strategy is deployed and governance administered through policies, people and partnerships and realized via process implementation and execution. Selected processes critical to SEE are cited in the “E3 + 3E Ö 3P process implementation and execution” block of Figure 1. In this block there is a distinct process associated with design and change diagnosis, readiness and implementation. This drives context‐specific human capital agility and competence building critical to processes influencing the enterprise capacity for SEI. The human capital category of the central block addresses current, short‐term, and future needs. Figure 1’s “3P results and refinement” block is divided among human capital, innovation, financial, and sustainability categories. Innovation and sustainability are threads running through each of these categories but more general results may also be incorporated – particularly in the human capital and financial categories. As the enterprise advances, new intelligence and foresight critical organizational performance is generated that lead to both current and next best practices and sources of competitive advantage. In all what may be seen in the Springboard is emphasis on innovation, organizational design, and (E3 corporate governance + 3E strategy) Ö 3P results transference.

5. SEE maturity assessment and the NEWS report The Springboard neither over‐ nor undervalues criteria relative to one another with relative weighting of criteria sensitive to its competitive landscape and vision for change left to the enterprise. The Springboard employs 0‐to‐10 maturity scales specifically derived for each area assessed, with results graphically summarized by modified radar charts (Yau 2011) called NEWS Compasses, each of which is augmented by a SWOT Plot NEWS Narrative. SWOT is an acronym for “strengths, weaknesses, opportunities, and threats” where strengths and weaknesses reflect enterprise internalities whereas opportunities and threats reflect

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Rick Edgeman and Jacob Eskildsen externalities (Heuer & Pherson 2011). Figure 2 portrays a generic NEWS Compass – SWOT Plot NEWS Narrative pair.

Figure 2: Springboard NEWS compass and springboard to SEE SWOT PLOT NARRative Use of the NEWS Compass terminology stems from selection of four key areas (N–E–W–S or NEWS) to assess with respect to each of six interrelated Springboards. There is dual intent in use of the NEWS acronym in that it can be associated with both the “North‐East‐West‐South” directional points of a magnetic compass and the “news” or feedback and direction use of organizational assessment tools. A NEWS Compass is thus constructed to deliver navigational feedback and foresight that drive the (E3 and 3E) integration Ö 3P transformation. N‐E‐W‐S selections for the six compasses are reported in Table 1 wherein one NEWS Compass each represents strategy and governance (SG), process implementation and execution (PIE), and the four categories of the “results and refinement” (RR) block: financial results (FR), sustainability results (SR), innovation results (IR), and human capital results (HCR). Table 1: Springboard NEWS compass points COMPASS POINT ASPECT Ö N E W S ASPECT Ö N E W S ASPECT Ö N

SPRINGBOARD ASPECTS & COMPASS POINTS STRATEGY & GOVERNANCE (SG) Transparency, Responsibility and Sustainability Intelligence & Knowledge Building & Acquisition Organizational Design: Human Capital Organizational Design: Innovation & Change PROCESS IMPLEMENTATION & EXECUTION (PIE) Relevance of & Capacity for Specific & Sustainable Innovation Design & Change Diagnosis, Readiness & Implementation Context Specific Human Capital Competence Building & Agility Innovation for Sustainability & Relevance FINANCIAL PERFORMANCE RESULTS (FR) Sustainability Investment & Return

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Rick Edgeman and Jacob Eskildsen COMPASS POINT E W S ASPECT Ö N E W S ASPECT Ö N E W S ASPECT Ö N E W S

SPRINGBOARD ASPECTS & COMPASS POINTS Supply / Value Chain & Other Performance Improvement Human Capital Investment & Return R&D: Return on & Reinvestment in Innovation SUSTAINABILITY PERFORMANCE RESULTS (SR) Financial Results Associated with Sustainability Efforts Societal Sustainability Results & Refinement Human Capital Results and Refinement Associated Explicitly with Sustainability Environmental Sustainability Results & Refinement INNOVATION PERFORMANCE RESULTS (IR) Innovation for Sustainability: Society and the Environment Other Context Specific Innovation Business Model Innovation Sustainable Innovation HUMAN CAPITAL PERFORMANCE RESULTS (HCR) Innovation Capacity & Performance Specific & General Competence & Agility Strategic & Tactical Continuous Improvement Sustainability Intelligence & Performance: Society and the Environment

The N‐E‐W‐S points for the first two compasses are the areas assessed in each block. RR compass N‐E‐W‐S point selections are based on well‐known contributors to FR, SR, IR and HCR largely derived from sources as per prior discussion. Further, customers are central to excellence systems and though nowhere overtly cited in Table 1, are key to the Springboard, with their importance underscored by the word relevance appearing in the PIE portion of the table, inherent in all references to innovation for sustainability, and implied in the SR NEWS Compass societal dimension. There are six primary NEWS Compass – SWOT Plot NEWS Narrative pairs with each narrative integrated and aligned with the corresponding NEWS assessments. A seventh “super pair” results from combining the six pairs into a Summary NEWS Compass and Summary SWOT Plot Narrative. Unlike all other compasses, the Summary NEWS Compass will have six axes as represented by the uppermost compass in the NEWS Compass Dashboard of Figure 3. Similarly, the Summary SWOT Plot Narrative must be aligned and integrated with its corresponding compass. Due to its narrative nature the summary must be thoughtfully formed and requires the assessment team to regard importance relative to context of the assessed areas represented by the primary compasses and narratives. The primary SWOT Plot NEWS Narratives together with a Summary SWOT Plot NEWS Narrative are portrayed in the SWOT Plot NEWS Narrative Dashboard of Figure 8. Combining the two dashboards provides a SEE assessment referred to as the Springboard to SEE NEWS Report.

6. Conclusions Whether through a mathematical formulation or purely descriptively, valuation for a given axis of any compass and, indeed, the compasses themselves should be determined according to enterprise context. It is of course unlikely that the various axes are of equal importance within a compass, let alone from compass to compass. This is of course a longer way of stating that “it is the context that counts” – one must be constantly mindful of context. Given the nature of SEE, themes such as governance, sustainability, innovation, intelligence and foresight generation, competence building, and organizational design should be emphasized and aligned throughout the assessment process. In other words, during the assessment it is critical to be aware of what is, and is not being assessed – this is a matter of focus. The purpose of SEE and Springboard assessment is to aid organizations in the quest to become continuously relevant and responsible by driving E3 (efficient, effective and ethical) governance and 3E (equity, ecological and economic) strategy to produce 3P (people, planet and profit) enterprise results. This requires a cycle of assessment, generation and implementation of usable foresight leading to next best practices and sources of competitive advantage. The value of the strategies and tactics developed herein is directly proportional to the extent such results are delivered.

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Figure 7: NEWS compass dashboard

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Coupling with Standardisation and Diversity: Intellectual Capital Reporting Guidelines for European Universities Susana Elena1 and Karl‐Heinz Leitner2 1 Institute for Prospective Technological Studies, Joint Research Centre, European Commission, Seville, Spain 2 AIT Austrian Institute of Technology, Vienna, Austria susana.elena‐perez@ec.europa.eu karl‐heinz.leitner@ait.ac.at Abstract: European universities have been immersed during the last decades in important transformation processes aiming to make them more autonomous, economically efficient and competitive. They have to demonstrate fine resource management and accountability in support of clearly defined and feasible goals, even more important during periods of financial crisis and budget cuts. From a managerial perspective, Intellectual Capital (IC) management and reporting can contribute to making the best use of available resources (Elena et al., 2011). In the realm of practice, an increasing number of universities and research centres in Europe have developed IC management and reporting models. However, their application has been, so far, based on voluntary basis. An important effort to provide a homogenous and comprehensive framework for managing and reporting IC in universities was developed by the Observatory of European Universities (Sánchez et al., 2009 and OEU, 2006). The Austrian case is a remarkable example since it has established a law that includes the compulsory delivery of an Intellectual Capital Report (“Wissensbilanz”) (Altenburger and Schaffhauser‐Linzatti 2006) by its publically funded universities since 2006. Currently, a project is being carried out in Romania, with the ultimate goal of preparing a guideline for applying IC reporting in Higher Education institutions. A series of Mutual Learning Workshops (MLW) are being organised (started in October 2012) by the Rumanian Agency for Education, Research and Innovation together with international experts. Learning from the Austrian case and from other initiatives, the MLWs are meant to discuss between researchers, practitioners, managers and policy‐makers, how to design a guideline for IC reporting for Romanian Universities considering the national context and current reforms of the university system. The guideline should be also applicable for universities in other countries undergoing a reformation process of the university system. The paper will give an overview of international experiences with IC reporting of universities. As a practical case, the paper will present the first results of this on‐going project and reflect on which methods are appropriate to report on IC in different types of European universities and to what extent is it possible and advisable to standardise IC models. Keywords: intellectual capital guidelines, IC framework, indicators, universities, standardisation, reporting, diversity

1. Introduction European universities have been immersed during the last decades in important transformation processes aiming to make them more autonomous, economically efficient and competitive. They have to demonstrate professional resource management and accountability in support of clearly defined and feasible goals, even more important during periods of financial crisis and budget cuts. From a managerial perspective, Intellectual Capital (IC) management and reporting can contribute to making the best use of available resources (Elena et al. 2011). In the realm of practice, an increasing number of universities and research centres in Europe have developed IC management and reporting models. However, their application has been, so far, based on voluntary basis. An important effort to provide a homogenous and comprehensive framework for managing and reporting IC in universities was developed by the Observatory of European Universities (OEU) (Sánchez et al. 2009 and OEU 2006). The Austrian case is a remarkable example since it has established a law that includes the compulsory delivery of an Intellectual Capital Report (ICR) (“Wissensbilanz”) (Altenburger and Schaffhauser‐Linzatti 2006) by its publically funded universities since 2006. Currently, a project is being carried out in Romania, with the ultimate goal of preparing a guideline for applying IC reporting in European Higher Education institutions. A series of Mutual Learning Workshops (MLW) are being organised (started in October 2012) by the Rumanian Executive Agency for Higher Education and 1 Research Funding together with international experts.

Apart from the authors of this paper Jan Fazlagic, Konstaninos Kalemis, Zilvinas Martinaitis, Giustia Secundo, Miguel‐Angel Sicilia, and Kristine Zaksa are involved as experts in the group on IC for universities.

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Susana Elena and Karl‐Heinz Leitner Learning from the Austrian case and from other initiatives, the MLWs are meant to discuss between researchers, practitioners, managers and policy‐makers, how to design a guideline for IC managing and reporting for Universities in Europe considering the national contexts and current reforms of the university system. Amongst others, the following issues are being discussed: the IC framework and terminology to be applied; the selection of indicators more appropriate to reflect strategic priorities of individual universities but able to allow comparability; the possibilities to reduce the risk of producing an external report with little direct links to internal management processes; the need to report the required set of indicators with the necessary descriptive elements; the possibilities to avoid opportunistic behaviour of universities; and the role IC Reporting and indicators have for funding and budget distribution among universities. The rest of the paper is structured as follows. Section 2 gives a brief overview of international experiences dealing with IC managing and reporting of universities. Section 3 describes the first Mutual Learning Workshop and present the lessons learnt on the main key issues discussed: function and purpose of ICR; selection of IC models or frameworks; definition of indicators; and implementation process. Finally, Section 3 draws some preliminary conclusions and ways forward.

2. Brief history and review of recent initiatives and studies on IC on universities IC Management and Reporting was developed within industry in the 1990ies as response to the ever increasing investments in intangible assets or IC such as employees’ training, innovation, research and development, customer relationships or software and the lack of existing accounting methods to provide sufficient information for managing these investments. In addition, information to external stakeholders should be provided to support their decision marking, for instance for investors on capital market, whereby, information asymmetries should be reduced. In the end of the 1990ies the instrument was adopted and adapted by public research organisations and universities. Austria has been the first country where the idea of the IC reporting has been adopted widely for research organisations and universities. In 1999 the Austrian Research Centers (ARC) in Seibersdorf was the first European research organisation to publish an IC report for the entire organisation. The first ICR was based on a specific IC model which addressed the specifics of a research institute (Leitner et al. 2002). The aim of the IC report was to support information for the management of the intangible investments and to disclose information for external stakeholders. Other research organisations in Austria and Germany soon introduced IC reports as well and partly started benchmarking on a set of common indicators to learn from each other (e.g. Leitner and Warden 2004). In 2001, the Austrian Ministry of Education and Science started to prepare a new university law driven by the necessity to implement the Bologna declaration and to adopt the national university systems to the new challenges in a knowledge‐based society. The reorganisation of Austrian universities was based on the principles of New Public Management with its premises of increased autonomy, output orientation and performance‐based funding (Titscher et al., 2000). The new university law specifies the organisational framework of all public Austrian universities with respect to funding, governance, management structures, evaluation, accreditation, and rights of university staff. Ultimately, in the course of the definition of a new university law in 2002 (Universities Act 2002) the Ministry has adopted the idea of IC reporting. The policy makers and experts responsible for the development of the new university law identified a demand for providing comparable indicators about the IC but also about different outputs of a university was recognised. The underlying thesis was that a proper management of IC at universities has a significant impact on the performance and efficient use of the invested financial funds. The re‐organisation of Austrian universities revealed a demand for such a new instrument since universities were provided with greater autonomy and thus have had the task to take decisions on the resource allocations with respect to their tangible and intangible assets. Based on the framework proposed by Leitner et al. (2001) the Ministry drafted the pillars for intellectual capital reporting within the new university law (UG 2002). The Universities Act 2002 (§ 13, fig. 6) defines that the ICR explicitly has to show: “Each university shall submit an intellectual capital report for the past calendar year to the Minister, by way of the university council, by 30 April of each year. This shall, as a minimum, present in itemised form: (1) the university’s activities, social goals and self‐imposed objectives and strategies; (2) its

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Susana Elena and Karl‐Heinz Leitner intellectual capital, broken down into human, structural and relationship capital; (3) the processes set out in the performance agreement, including their outputs and impacts.” IC reporting for Austrian universities should provide information for the various stakeholders of the university. Thereby, the Ministry should also benefit from a better overview of the development of the national university system, the strengths and weaknesses in specific fields and thus get information for effectively adapting the national science and education policy. Thus, the ICR should serve as a management instrument for the university as well as a communication instrument between universities and the Ministry. Besides IC reporting, regularly evaluations and performance agreements were established as important instrument for the management and governance of universities (Leitner 2004). Between 2002 and 2006 the Ministry and the Austrian Rectors’ Conference developed a decree which defined the indicators to be published by all universities (WBV 2006). Finally, this ordinance defined 53 indicators to be published in five categories, these are human capital, structural capital, relational capital, research, and education, the latter two interpreted as outputs. The logic of the Austrian IC model is similar to conceptualisations of innovation processes and research processes developed within the innovation and evaluation literature which also frequently separate inputs, processes and outputs (e.g. Dodgson and Hinze 2000, Blalock 1999). Following the Austrian model further universities have published ICR in other (neighbouring) countries. In 2004, for instance, the Hungarian Corvinius University published its first ICR. More recently, the University of Liechtenstein drafted an ICR for internal purposes in 2011. Besides Austria, Spain has the most active community aiming to establish IC managing and reporting within the university sector (e.g. Ramirez 2010). In 2002, for instance, a project on the use of knowledge management technologies to improve quality management in universities was developed by the Innovation and Knowledge 2 Management Institute (INGENIO 2002). Between 2000 and 2003 the PCI project was launched aiming to develop IC indicators applied to the research activity of four universities and two research centers in Madrid, based on the Intellect Model (Euroforum 1998). The University of Basque Country conducted a knowledge management project in a strategic cross‐organisational process called “Research‐Development‐Knowledge Transfer”. Its aim was to diagnose the current state of the management and improve the process under the IC framework (Rodríguez et al. 2004). In addition, IC reporting projects for universities (e.g. pilot projects) have been also carried out in Italy, Portugal, Poland, Greece, Lithuania and Latvia aiming also to support the management of knowledge‐based resources and to communicate with diverse external stakeholders (e.g. Secundo et al. 2010, Kalemis et al. 2010, Fazlagic 2011). Two initiatives can be mentioned which tried to establish the IC reporting for universities on the European level. In 2006, the Commission nominated an Expert Group with the aim to promote the idea of IC reporting for SMEs. The result was the document RICARDIS (Reporting Intellectual Capital to Augment Research, Development and Innovation in SME’s). The goal of the RICARDIS project was to look for ways to promote the use of IC Reporting, on the assumption that this will increase R&D activities. In the RICARDIS report, Intellectual Capital is considered a crucial factor in the Knowledge‐based economy. RICARDIS addressed also the role of universities and research organisations which are often important partners of R&D‐intensive SMEs and an important part of the Innovation System. One of the recommendations that the RICARDIS document was to promote the elaboration of IC reports at universities and research centres (European Commission, 2006). 3 A prominent work was conducted within the EU funded PRIME Network of Excellence (Policies for Research and Innovation in the Move Towards the ERA) by 15 universities and research institutes from eight European countries 4 : the Observatory of European Universities (OEU). The aim of the Observatory was to develop a common framework for the characterization of research activities undertaken in universities and produce a set of indicators for supporting universities strategy and management processes

Joint initiative created between the Spanish Research Council (CSIC) and the Polytechnic University of Valencia. http://www.prime‐noe.org/ The Project run from June 2004 to November 2006

3 4

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Susana Elena and Karl‐Heinz Leitner Its main outcome was a Methodological Guide (OEU, 2006) about how to measure research and what elements should be measured. It suggested a "Strategic Matrix" which represents the relations between strategic and transversal issues (Autonomy, Strategic Capabilities, Attractiveness, Differentiation Profile and Territorial Embedding) and five thematic dimensions (Funding, Human Resources, Academic Production, Third Mission and Governance). The analysis of the inter‐relations (which corresponds with the cells of the Matrix) was made first by formulating key questions and then by suggesting precise indicators to answer such questions (Sánchez et al. 2009; Sánchez and Elena 2007). As part of this Methodological Guideline, a specific framework for IC reporting for European universities was developed: the so‐called ICU Report. Its aim was to make recommendations for the disclosure of IC information on the research activity of European universities in a homogeneous way. According to it, an ICU Report should incorporate three sections 5 : (1) vision of the institution; (2) summary of intangible resources and activities, and (3) a system of indicators, which are both financial and non‐financial. The 43 indicators proposed were classified following the most common and widespread IC taxonomy, into Human, Organisational and Relational Capital. Besides these projects a number of workshops and networks have emerged dealing and promoting the idea of IC reporting and management for universities. Amongst others in 2001, a Working Group on Managing, Valuing & Reporting Intellectual Capital (VIAMK) for HEROs with the EARMA was established. In 2004 a workshop for IC reporting for HEROs was organised in the course of the annual EARMA conference in Bucharest. In addition, a track about HEROs was organised at the OECD Conference about Intangible Assets in Ferrara in 2004. 2009: Mini Track on Intellectual Capital for Universities and Research Organisations at ECIC 2009. Apart from a pilot projects and case studies of some universities, a study worthwhile mentioning is a work published by Bezhani (2010). He investigates UK universities referring to the Austrian framework and consequently separates between human, structural and relational capital as well as research and teaching. He used ccontent analysis to examine the amount and nature of IC disclosure of the annual reports of 30 UK universities. Generally, the amount of IC information disclosed by UK universities in their annual reports is rather low. UK universities were identified as having low awareness of IC, only one university had a section in the annual report called intellectual capital. Bezhani reports that universities most often report about their research activities (e.g. publications, contracts) followed by relational capital (e.g. number of conferences hosted) and human capital (e.g. number of staff). Many universities also report about their investments in libraries (structural capital). However, there is no relationship between the ranking and size of the university on the one hand and the amount if IC disclosure on the other hand. He studies also the motivations for IC reporting and management: “showing that we are an innovative organization” was the most important external purpose of disclosing information about IC; “generating innovation” and “supporting strategy” and “creating a certain culture” are most important goals for internal use. Ramirez et al. (2011) did an interesting study about the different needs of the various stakeholders of a university based on a Spanish survey showing that for internal purposes more detailed information is requested. The longest experience with IC reporting at universities can be reported from the Austrian case. The experiences can be briefly summarized based on Leitner (2004) and projects and interviews one of the authors of this paper has conducted in the last years. The main benefits and effects are seen in delivering information for strategy development and priority setting. In addition, IC reports are partly used, to control and monitor the achievement of goals and to define measures, mainly within the performance contracts. However, in order to use IC reports for resource allocation and strategic control managers are often focusing on a smaller set of indicators and partly define their own specific indicators. Clearly, 53 indictors (for which additional sub‐ categories are defined) are most likely too much to be controlled deliberately and universities have thus to define the most relevant measures, which on the one hand express their specific goals and strategies, and on the other hand have the strongest impact on the output. 5

For detailed information see Sánchez et al. (2009).

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Susana Elena and Karl‐Heinz Leitner In general, indicators which are related to funding have the greatest impact (e.g. scientific publications or percentage of competitive funds of the total university budget). Although at least a certain number of the indicators are used for internal management, benefits of IC reporting are (still) discussed controversially within universities, which though leads to productive discussions; however, some administrators, managers and professors still consider IC reporting as too bureaucratic and time consuming exercise. The culture within universities has changed in the last decade and some indicators are considered as “common good” such as number of publication. However, one can conclude that Austria over‐regulated the introduction and use of the IC reporting system and defined too much compulsory indicators. The Austrian experience, the OEU guideline, further studies, pilot projects and case studies deliver on the international level deliver a solid base for designing a guideline for IC reporting and management for the Romanian case and possible other European countries.

3. Mutual learning workshop: Methodology and lessons learnt In the frame of the Project "Quality Assurance in Higher Education through Habilitation and Auditing" 6 being run by the Executive Agency for Higher Education and Research Funding of Romania (EUFISCDI) a series of Mutual Learning Workshops (MLW) are being organised as a mean to bring together international experts and practitioners to share their views and experience on IC Reporting and setting up task forces to draft the Blueprints for IC Reporting for universities. The MLW is a valid tool to provide a common space, a “platform”, for practitioners, managers and policy‐ makers to reflect upon, share, consolidate and transfer experiences and lessons. Particularly, the objectives of these MLWs are the following: i) to better understanding of what IC Reporting means to improve the quality of the Romanian Higher Education, ii) setting up a tailored methodology 7 (Guidelines, Blueprints) able to help the elaboration of IC Report at the university , and iii) drafting public policy proposals for the policymakers interested in IC Management in the Knowledge Society. The first of the MLW series was organised in Bucharest at the end of October 2012 as a two days event with a group of 15 international experts on IC. Despite the preliminary idea was to develop guidelines for IC in Romanian universities, given the need to standardise procedures and homogenised approaches at European level, the guidelines will have a wider approach and should be applicable for other European universities. After the discussions the lessons learnt can be organised around the following four key issues: (i) function and purpose of ICR; (ii) IC model and framework; (iii) definition of indicators, and (iv) implementation process. These four crucial issues for developing an IC guideline for universities are discusses subsequently.

3.1 Function and purpose of ICR for (Romanian) universities When discussing the function and purpose of IC approaches and tools for universities the first element that should be incorporated to the discussion is related to university's stakeholders: Who will be interested in the ICRs? What type of information is crucial for different stakeholder groups? The identification of stakeholders is thus a starting point when developing an IC management and reporting system. In general it could be argued that there are seven users groups: Public administrators; students; business organisations; teaching and research staff; university governors; administrative staff and union organisations. As acknowledged by Ramirez et al. (2011), it is highly important that university publishes information on IC taking into account the specific needs of different stakeholders. The majority of respondents of the survey run by the authors considered essential that universities provide information on their IC in order to make their current accounting model more relevant for decision‐making processes. The study also identified the specific demands on intangibles and IC of each stakeholder group. For instance, public administrators and university governors demand more information on the university relation with business sector and on graduate 6

Project co‐funded by UEFISDI and European Social Funds (Sectoral Operation Programme Human Resources Development 2007‐2013). Similar guidelines will be produced of IC in nations and regions.

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Susana Elena and Karl‐Heinz Leitner employability while students need better information on quality of teaching and satisfaction among graduates. Administration staff is basically interested in information related to human capital and the university social and cultural commitment and teaching and research personnel is more focused on information related to the institution's research capabilities and competences and relations with other universities. Trade unions show more interest in student satisfaction and training activities for staff. There was a general agreement among the experts on the importance of conceptualising the IC systems as a management tool in first place to improve internal management, define and update the mission statement, help to identify the organisation's profile and to communicate the strategy throughout the organisation. The implementation of an IC system will also enable internal learning processes (which it is a voluntary process in any case) and help self‐evaluations. It was agreed that the goal is not just to report about IC but that the management of intangible resource should be the starting point of any exercise. In addition, as disclosure tool, it can improve the level of transparency and will serve as a tool to facilitate international benchmarking. Despite the greater attention that indicators received, an IC reporting system should not be used for rankings of universities. Based on the existent experiences from the various European countries (see also above), the IC system for universities should have the following characteristics:

It should start from the identification of the vision, mission and objectives of the institution;

It should provide a comprehensive summary of intangible resources and activities. In other words not juts inputs, but output and processes;

It should include a system of indicators (following the classical classification: human, organizational and relation capital), financial and non‐financial.

Regarding the latter item, given the clear separation of the two functions of the IC systems – internal management and disclosure tool, ‐ it would be crucial to have a different set of indicators to cope with internal and external needs.

3.2 The selection and adaptation of a IC model As mentioned before, besides Austrian universities which are obliged by law to publish IC Reports, other initiatives across Europe have been designed and implemented on voluntary and individual basis. Thus, there exist already a number of IC models which can be adopted and adapted. It is argued by most authors and guidelines that the clear staring point of an IC report is the mission statement and the strategic plan of the institution. However, not all institutions in Europe are at the same level of development of their management principles and strategic objectives. 8 Our proposal is hence to implement what we call a “Maturity Model for IC ” which is a flexible model of implementing IC approaches allowing each institution to follow the process at a certain rhythm and evolve along time without feeling the pressure of other institutions with different internal characteristics.. A maturity model can be viewed as a set of structured levels that describe the behaviours, practices and processes of an organization to sustainably produce the required outcomes. The model provides a theoretical continuum along which process maturity can be developed incrementally from one level to the next. A model based on different steps of maturity might be an answer to cope with the huge diversity of European universities. The model proposed would start from serving as learning instrument (e.g. a first analysis of strength and weaknesses of knowledge based resources and processes) to more advanced approaches using an IC system for management control. This stepwise approach could include, at least, three main phases (see Figure 1 below): (1) Strategic planning and self‐evaluation, (2) Intangible resources and capabilities, (3) Measuring, and (4) Reporting.

Similar models are used in business management processes.

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Level 1: Strategic planning and self‐evaluation. At this maturity level, the institution has to review their internal processes and understand their mission, values, long term objectives and strategic plan. If there is no strategic plan, the institution should perform an internal learning process and define their strategic goals and principles. Those institutions with a strategic plan should revise it and assess the degree of success of the objectives and actions initially stated. The effectiveness of this activity is essential for the success of the whole process.

Level 2: Intangible resources and capabilities. This level implies the identification and definition of intangible resources and capabilities of the institution. Those intangibles factors which make the institution unique and difficult to imitate. The internal actors which allow the university to differentiate in the Higher Education market.

Level 3: Measuring. At this level of the maturity model, the university will be able to propose a set of indicators to measure their intangible resources and capabilities. Indicators that capture the idiosyncrasy of the institution. The purpose of this activity is to provide university managers with a better tool to internally govern the institution.

Level 4: Reporting. The final level of maturity would allow the university to report of their IC, taking into consideration the specific information needs of the different stakeholder groups.

Source: Own elaboration. Figure 1: Maturity model of IC for universities

3.3 Definition of indicators: standardisation versus diversity The debate between the need to have standards at national level (and even at European level) for benchmarking proposes when, at the same time, respecting the diversity of universities within Europe is still unresolved 9 .

See also results from the Aquamethd Project (Advanced Qualitative Methods for the Evaluation of the performance of public sector research): Bonacorsi and Daraio (2007).

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Susana Elena and Karl‐Heinz Leitner The experts attending the MLW linked the debate of standardisation and diversity with the role of indicators and how to define them. As mentioned in the previous point, IC indicators should serve two purposes (internal management and external transparency) for that reason not all indicators should be disclosure and not all of them should serve for benchmarking or comparative analysis. Accordingly, our suggestion is to define IC indicators following a pyramid approach. Each university will have a wide set of indicators serving management purposes and thus specific to that particular institution (those define through the maturity model explained in the previous point). A second set of indicator will be more specific to the higher education sector and also to the different disciplines of faculties or department. Finally, at the top, we will have a reduced number of indicators that are common to all universities and can be useful for benchmarking analysis 10 (see Figure 2).

Source: own elaboration Figure 2: Pyramid model of indicators for IC management and reporting in European universities Following such an approach would one the one hand allow that universities benchmark and compare some common indicators (which also delivers information for policy makers) and on the other hand enables universities to adopt and use specific indicators for strategic and management control. This can be seen as one approach for coupling the demand for standardisation and diversity.

3.4 The implementation and diffusion process of ICR The implementation process of any kind of managerial and reporting model is always crucial. Although still not completely defined, a series of measures and initiatives were proposed to help universities in the implementation process and to foster a learning process among institutions. It was agreed that the implementation and diffusions of IC reporting and management should follow a bottom‐ up approach. In addition, modern forms of networking and community building (over the internet and using social media) should be employed to follow a platform‐based implementation, both on the national and international level.

4. Preliminary conclusions and ways forward The need and importance of IC managing and reporting seem to be increasingly acknowledged but methodologies, models and terminology are still very diverse. When referring to universities, the diversity of institutions across Europe and the diversity of models and initiatives make difficult to, on one hand, commit universities to put in practice IC models and, on the other hand, to have homogenous approaches to allow comparability and benchmarking. In this context, the series of MLW being organised in Romania to come up with a blueprint or guidelines for designing and implementing IC systems in universities has been very welcome by international experts and 10

The issues of data desegregation/aggregation was tackled for instance by EUO (2006) and Elena (2007).

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Susana Elena and Karl‐Heinz Leitner practitioners. First results of this work embedded in the international literature have been presented in this paper. Based on the experience and best practices with IC reporting in universities on the international level, the project has to address key issues and learnings when developing an IC guideline and promoting the IC methodology for Romania and other countries, these are:

In order to avoid a divergence between external and internal reporting, IC indicators have to be specified and targeted towards the different stakeholders and users, here the proposed pyramid approach serves as solutions;

Universities have to focus on a condensed set of indicators for effective strategic control, but should not stick exclusive on indicators but also sue more qualitative valuation methods;

The design of the implementation process is highly important for the success of a project introducing IC reporting, this should be a participatory, community‐based process;

The development of a guideline with a certain level of standardization for valuing and reporting IC by universities on the international level is also beneficial for the use on the national level as it allows comparison, benchmarking and learning over time.

One of the open issues on the IC model for universities is the tension between diversity and comparability was directly tacked by the experts. A maturity model with a step wise approach was recommended to allow institutions with different background and internal management development start with the implementation of an IC model in a flexible manner. In addition, a pyramid approach for the definition of indicators was also suggested. This approach will include a wider set of indicators for internal management not all suitable for publishing and a reduced number for common indicators for benchmarking and comparability analysis.

References Altenburger, O. and Schaffhauser‐Linzatti, M. (2006) “Controlling universities’ intellectual capital: are the recently implemented Austrian instruments adequate?”, paper presented at the EIASM Workshop on Visualising, Measuring, and Managing Intangibles and Intellectual Capital, Maastricht, October 25‐27. Blalock, A.B. (1999): Evaluation Research and the Performance Management Movement. From Estrangement to Useful Integration? Evaluation, 5, 2, 117‐149. Bonacorsi, A. and Daraio, C (2007), Universities and Strategic Knowledge creation: specialisation and performance in Europe. Edward Elgar Dodgson, M., Hinze, S. (2000): Indicator used to measure the innovation process: defects and remedies. Research Evaluation, 9, 2, 101‐114. INGENIO (Instituto de la Gestión de la Innovación y del Conocimiento) (2002) “Portal de Conocimiento del II Plan de la Calidad de las Universidades”, http://www.ingenio.upv.es. Instituto Universitario Euroforum El Escorial (1998) “Medición del Capital Intelectual”. Madrid, Spain. Elena , S., Saritas, O. Pook, K. and Warden C. (2011), " Ready for the future? Universities’ capabilities to strategically manage their intellectual capital", Foresight Journal, Vol. 13 No. 2, pp. 31‐48 European Commission (2006) RICARDIS: Reporting Intellectual Capital to Augment Research, Development and Innovation in SMEs. Internet device: http://ec.europa.eu/invest‐inresearch/pdf/download_en/2006‐2977_web1.pdf Federal Ministry of Education, Science and Culture of Austria (2002), “University Organisation and Studies Act – University Act 2002 ‐” Nº 120/2002. http://www.bmbwk.gv.at. Leitner, K.H., Bornemann, M., Schneider, U. (2002): Development and implementation of an Intellectual Capital Report for a Research Technology Organisation, in: Bontis, N., (Ed.): World Congress on Intellectual Capital Readings, Butterworth Heinemann, 266‐285. Leitner K‐H. (2004): Intellectual capital reporting for universities: conceptual background and application for Austrian universities, Research Evaluation, 13, 2, 129‐140. Leitner, K.‐H., Sammer, M., Graggober, M., Schartinger, D., Zielowski, Ch. (2001) “Wissensbilanzierung für Universitäten“, Contract project for the Federal Ministry for Education, Science and Culture, Seibersdorf. Leitner K‐H., Warden, C. (2004): Managing and reporting knowledge‐based resources and processes in Research Organisations: specifics, lessons learned and perspectives, Management Accounting Research, 15, 33‐51. MERITUM (2002) “Guidelines for Managing and Reporting on Intangibles (Intellectual Capital Statements)”, Vodafone Foundation, Madrid. Observatory of the European University (OEU) (2006) “Methodological Guide. Strategic Management of University Research Activities”. Lugano: PRIME. Available at: http://www.enid‐europe.org/PRIME/documents/OEU_guide.pdf Ramírez Y. (2010) “Intellectual Capital Models in Spanish Public Sector”. Journal of Intellectual Capital 11(2):248‐264.

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Susana Elena and Karl‐Heinz Leitner Ramírez Córcoles, Y., Santos., J., Tejada Ponce, A. (2011) "Intellectual capital in Spanish public universities: stakeholders' information needs", Journal of Intellectual Capital, Vol. 12 Iss: 3, pp.356 ‐ 376 Sánchez MP, Elena S and Castrillo R (2007) “The ICU Report: An Intellectual capital proposal for university strategic behaviour. Supporting Success and Productivity Practical Tools for Making Your University a Great Place To Work”. IMHE Conference, Paris, 3‐4 September 2007. Sanchez, P.M. and Elena, S. (2006) “Intellectual capital in universities: improving transparency and internal management”, Journal of Intellectual Capital, Vol. 7 No. 4, pp. 529‐48. Sanchez, P.M., Elena, S. and Castrillo, R. (2009) “Intellectual capital dynamics in universities: a reporting model”, Journal of Intellectual Capital, Vol. 10 No. 2, pp. 307‐24. Secundo, G., Margherita, A., Elia, E., Passiante, G. (2010) "Intangible assets in higher education and research: mission, performance or both?", Journal of Intellectual Capital, Vol. 11 Iss: 2, pp.140 ‐ 157 van Vught, F. and Ziegele, F. (Eds.) (2011) “Design and Testing the Feasibility of a Multidimensional Global University Ranking, Final Report”, CHERPA‐Network, June 2011 Titscher, S., Winkler, G., et al. (Eds.) (2000) “Universitäten im Wettbewerb”, München. Wissensbilanzverordnung (WBV), BGBl. II Nr. 63/2006, Wien.

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IC Management in Universities: Where is Teaching? Susana Elena1 and Katja Pook2 1 Institute for Prospective Technological Studies, Joint Research Centre, European Commission, Seville, Spain 2 Faculty of Psychology, University of Koblenz‐Landau, Landau, Germany susana.elena‐perez@ec.europa.eu pook@uni‐landau.de Abstract: European universities have been immersed during the last decades in crucial structural changes throughout the so‐called “Bologna process" to ensure comparability in the standards and quality of higher education qualifications. The main objectives of the process were to: develop a system of easily readable and comparable degrees, adopt a system with two main cycles (undergraduate/graduate), establish a system of credits, promote mobility by overcoming legal recognition and administrative obstacles, promote European co‐operation in quality assurance, and promote a European dimension in higher education. Our universities are still undergoing a major change process including several measures and initiatives partly started even before the Bologna agreement (Nickel 2011). The fact that a change process turns out to be more complex than envisaged before is a common phenomenon. Thus, despite the efforts, there is a danger that the changes proposed might only be "cosmetic". The effects of the Bologna process in the real organisation of European universities should be studied more carefully. To avoid a “cosmetic” reform, it is important to understand that the Bologna Process will only be a reality with the joint commitment of national and European level institutions (Berlin Conference 2003), and with the involvement of the whole university community. In this context, the paper takes a look at the current state of Intellectual Capital (IC) reporting and managing at universities, with special attention on the role of teaching while considering the three pillars of IC: human, structural and relational capital. Most of IC indicators used by universities focus on research and third mission while less on teaching. This contradicts the fact that Bologna shall strengthen the role of education and teaching in universities and improve organisation and management. How can this gap of objectives and practical implementation be overcome? The analysis of a case study of a German faculty where teaching and learning play a major strategic role serve us to illustrate how far evaluation and quality management in teaching indirectly lead to practical IC management, although not wearing this name. Finally we suggest a set of indicators for IC management, and eventually reporting, in teaching, that can practically be introduced to universities’ monitoring and evaluation systems. Keywords: teaching, bologna process, intellectual capital management, quality assurance, indicators

1. Introduction European universities have been immersed during the last decades in crucial structural changes throughout the so‐called “Bologna process" at European level and also in reforming programmes and modernisation agendas at national level (European Commission 2006 and 2011; ERAWATCH 2008). In one way or another, these reforming and modernisation processes ask for more institutional autonomy of universities, new modes of universities governance and more "business thinking" and practices to strengthen university management. The current economic and financial crisis is putting even more pressure on the Higher Education (HE) sector and stronger demands on universities in terms of greater accountability and transparency and further engagement in the development of local and regional context. Adapting the new requirements and challenges implies the introduction of management systems in order to govern universities according to criteria of efficiency and effectiveness. We argue that the Intellectual Capital (IC) framework with its three pillars of human, structural and relational 1 capital could serve as a valid tool not only for reporting, but also for internal management. However, most of IC indicators used by universities focus on research and third mission, but less on teaching 2 . This contradicts the fact that Bologna shall strengthen the role of education and teaching in universities and improve organisation and management. How can this gap of objectives and practical implementation be overcome?

See more information on the most widespread classification of IC in MERITUM (2002) See, for example, the framework and indicators proposed by the Observatory of European Universities (OEU) and the reviews done by Sanchez and Elena (2006) and Sánchez et al. (2009). 2

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In the article we present a case study run in a German faculty with a strong teaching profile that serves us to identify valuable factors and key processes, partly derived from quality management activities, and to propose a set of indicators for IC management of teaching and education. The rest of the paper is structured as follows. Section 2 briefly describes the role of education and teaching in European universities and reflects upon the implications of the implementation of the Bologna process. Section 3 explains the benefits of implementing IC models in universities. Section 4 introduces the case study and Section 5 suggests a set of processes and indicators relevant for tracking the activities of teaching and education in universities. Finally, Section 6 draws some preliminary conclusions.

2. The role of education and teaching in European universities: The implications of the 'Bologna process' Traditionally, medieval universities were considered as communities of scholars whose primary function was teaching, mainly in the field of law, theology and medicine (Malagón 2005). In other words, their main function was maintaining and diffusing knowledge. Over time, as societal needs and circumstances changed, universities gradually adapted to the new demands. At the beginning of the nineteenth century Humboldt University 3 was funded in Berlin to provide students with a broad humanist education under the idea of unity of teaching and research. This radical concept of university had a strong influence on the European HE system over the next century and it was considered the main foundation of modern universities. The so‐called Humboldtian model rested on three essential pillars. First, it emphasised that universities would be involved, in addition to teaching, in research activities. In this way, universities evolved from the initial static idea of conserving and diffusing knowledge towards a more dynamic understanding of universities as organisations that needed to actively contribute to the creation of new knowledge. Nevertheless, teaching was still considered their main mission and hence research activities were undertaken mainly because they were crucial in providing better education (Martin 2003; Malagón 2005). Second, it provided a clear idea about the role of the State in relation to HE systems. This model emerged in the belief that governments bore full and exclusive responsibility for funding universities. Third, and despite the significant reliance on public funds, these universities were characterised by a high degree of autonomy both at institutional level – a wide margin of manoeuvre to allocate resources – and at an individual level – academics were free to choose their research topics (Martin 2003). The abovementioned model can be categorised under the so‐called `classical universities´, that is to say, universities whose main purpose was education and ”knowledge for its own sake” (Martin 2003, p.14). However, the notion of university is not unique and different institutions evolved in different ways in order to satisfy socio‐economic demands. Martin’s (2003) analysis of the university landscape pointed to other `species´ of universities, known as “technical universities”. The latter concept refers to institutions whose main goal is to disseminate useful knowledge and to train students with practical skills and competence. 4 These days, we could say that `multiversity´ – that is, the existence of multiple roles and missions embodied in a single university: schools for professional education, research institutions, centres for continuing education, etc. ‐ characterises Western universities. Today the vast majority of European universities have three main functions: the transmission of knowledge – teaching‐, and the generation of knowledge – research ‐. Furthermore, empirical evidence shows that there has been an intensification of industry‐academia relations in the past twenty years, mainly as a response to 3

http://www.hu‐berlin.de/indexe.html. The Workshop “Towards Multiversity: universities between national traditions and global trends in higher education” organised by the Institute for Science and Technology Studies of Bielefeld University (Germany), held in Bielefeld November 11‐13, 2004, reflects the increasing interest in this topic. For more information see: http://www.uni‐bielefeld.de/iwt/gk/multiversity/

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public budgetary stringency and because of the new role of the university in society. This mission ‐ conceptualised under the title `third mission´ 5 or `third stream´ ‐ refers to all those activities whereby universities can directly address social welfare needs and private or public economic objectives (Molas‐Gallart 2005). Although it is clear that universities´ role in the knowledge‐based society is considered crucial (European Commission 2003, 2005 and 2006) the European research and innovation policy framework presents a very complex picture where different initiatives are running simultaneously and forcing universities to cooperate (for instance in order to get funds from the Framework Programmes) and to compete at the same time for students, researchers, teachers, funds, etc. In this context, the "Bologna process" 6 started in 1999 with the signature of the Bologna Declaration (Bologna Declaration 1999). Our intention in this paper is not to analyse it in detail but to shed light onto the important area of teaching as one of the main missions of European universities nowadays. The "Bologna process" is a dynamic and still on‐going process. One of the main driving forces for the establishment of the Bologna process and, later on, the concept of the European Higher Education Area (EHEA) was the wish of European states to increase the level of competitiveness of their Higher Education Institutions (HEIs) (Amaral and Magalhaes 2004). Although initiated by Ministers it has not been a simply top‐down process but it has a significant degree of bottom‐up approach to build on common values and traditions and to share reform agendas of universities in different European countries. Today it should not only be considered as an administrative and structural reform but also as a wider process of modernization of European HE (Scott 2012; p. 1). The process aimed to develop a system of easily readable and comparable degrees, adopt a system with two main cycles (Bachelors and Masters), establish a system of credits, promote student and staff mobility, and develop a compatible quality assurance system with consistent academic standards across the European Higher Education Area (EHEA) (Nickel 2011; Scott 2012). The very first phase of the process was more instrumental and conceptual (Scott 2012). The so‐called "Bologna +", considered the second phase of the process, implies, among other considerations, the extension of the framework to include doctoral programmes, which makes more explicit the links between education and research and thus the necessity to align the EHEA and European Research Area (ERA) processes (Scott 2012). Besides the potential benefits of Bologna for the promotion of a European dimension of HE and to ensure comparability in the quality standards of higher education qualifications, some stakeholder groups, mainly students and trade unions, have sometimes criticised Bologna as a mechanism for introducing 'market' values into HE and also for encouraging managerial modes of organization to replace collegial modes of organization for universities (Scott 2012). A thorough evaluation of success and failures of the Bologna process would go far beyond the scope of this paper, and has already been delivered in various ways and media (see, among others, Nickel 2011). In one way or another, the reforming and modernisation processes lead by the Bologna process has asked for more institutional autonomy of universities, new modes of universities governance, more "business thinking" and practices to strengthen university management, and new quality assurance processes. However, as Berndtson (2003) already suggested one decade ago, there is a danger that the changes proposed by the spirit of the process might only be `cosmetic´. The author reflects on the effects of the Bologna process in the real organisation of European universities and warns about some problems that are arising within the reform process. In the light of this, his paper argues that the reform does not address the daily problems of scholars in universities. As he points out “the only problem within universities which is touched upon is a long duration of studies. Scarce resources, problems of mass education or the role of part‐time faculty hardly figure

For further discussion on “third mission” see Observatory of the European University (2006; pp. 125‐169) and Laredo (2007). For detailed analysis of the implications of the Bologna process and the creation of the EHEA see Curaj et al. (2012), parts I and II.

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in the reform process” (Berndtson 2003, p.12). To avoid a “cosmetic” reform, it is important to understand that the Bologna Process will only be a reality with the joint commitment of national and European level institutions (Berlin Conference 2003 7 ), and with the involvement of the whole university community.

3. Intellectual capital framework for managing and reporting in European universities Adapting the new requirements and challenges mentioned in the previous section implies the introduction of management systems in order to govern universities according to criteria of efficiency and effectiveness. New modes of governance of universities and greater demands for more transparency and accountability require an adequate allocation of resources, developing managerial skills in central bodies, faculties and institutes, and the introduction of new managerial tools to help universities to improve their internal management. As argued by Paradeise (2012, p. 579) "shifting from administrative bodies to strategic actors requires as a key initial prerequisite to reinforce individual universities' steering capacities". In other words, it is not only rules and regulations that need to be changed, but also mental models, value systems and capabilities. Successful management and governance of HEIs presupposes high quality data giving reliable indications of what happens in these institutions (and in the whole HE system). The Bologna process has fed the demand for data on HE and within HEIs since the continuous changes of the European HE sector need to be monitored (Muehleck 2012). At institutional level, the IC framework could serve as a valid tool for internal management and as a reporting instrument. IC reports could comprehensively visualise inputs, processes and outputs. In addition, the proper management of IC at universities has significant impact on the performance and efficient use of resources (Leitner 2004). On an external level, as disclosure tool, the IC framework has the following benefits:

It improves the level of transparency

It provides comprehensive and valuable information to stakeholders: students, teaching personnel and researchers, Ministries, funding organisations, business sector, and society as a whole.

It can enhance competitiveness.

It can facilitate the presentation of results, which could contribute to attracting funds. However, HEROs also have intangible liabilities so if it is deteriorating, disclosure may prejudice the chances of getting future resources.

On an internal level, as a management tool:

It defines and updates the mission statement.

It helps to identify priorities in terms of research and teaching, defining the organisation's profile.

It communicates strategy throughout the organisation.

It allows the alignment of individual goals with institutional objectives.

It links strategic objectives to long‐term targets and annual budgets.

It promotes an internal process of learning about the institution's structure and performance.

It enables discussion on the intangible value drivers and success factors.

It monitors the achievement of goals and assesses the organisation's performance over the course of time.

Besides the potential benefits the there are still some challenges regarding the implementation o IC models in universities. Among others, the following points should be taken into consideration:

The high diversity and heterogeneity of fields among universities and even within the same institution.

The high diversity of stakeholders and their needs (research and teaching staff, students, government, business sector, etc.).

Communiqué of the Conference of European Ministers Responsible for Higher Education (2003)

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Different university culture (traditional vs innovative).

Not completely defined boundaries of the university.

Not all institutions have a strategic plan.

The level of data disaggregation/aggregation. How much disaggregation is useful for benchmarking analysis? How much is possible? And cost efficient?

Lack of availability of data or scattered data to fill in the proposed indicators.

The interpretation of indicators in the context of each institution.

How to implement auditing and control mechanisms.

Despite the huge variety of IC models, method and instruments 8 , all initiatives implemented and proposed in and for universities share some common characteristics:

An IC model starts from the identification of the vision, mission and objectives of the institution.

It provides a comprehensive summary of intangible resources and activities.

It includes a system of indicators (following the classical classification: human, organizational and relation capital), financial and non‐financial.

A literature review done for the purpose of this article suggests that most of IC indicators used by universities focus on research and third mission, but less on teaching. This contradicts the fact that Bologna shall strengthen the role of education and teaching in universities and improve organisation and management. Accordingly, we think that the IC framework helps addressing the necessity of universities for new managerial and reporting instruments, but new indicators on teaching should be included to balance their three strategic missions.

4. Case study: strategy and practice for quality in teaching The case presented here describes the relationship between quality management and IC management in a university setting with a focus on internal management. Decisions about in how far internal management data are used for external reporting require an excellent strategic awareness. Nevertheless, regulations for re‐ accreditation imply external reporting of data on the evaluation of teaching. The level of analysis is one faculty of a medium‐sized university with eight faculties all in all. At this university, management tasks are still held to a large part by central bodies. This implies that reporting by the faculty is compulsory, but the data remain mostly inside the university. This case study is not taken as an example of good practice. It serves for analysing the process from the definition of the strategy to the definition of indicators, leading to the analysis of the key success factors of the institution. The paper combines these practically derived factors with those that are stated in the literature. The case description is partly based on analysis of a strategy paper that has been produced following a bottom‐up process, and the following steps of selection of key processes and derivation of success factors by the faculty management. For confidentiality reasons we cannot disclose the name of the university and faculty. Also the disclosure of the indicators used by the faculty is not possible, but a suggestion of possible indicators is given.

4.1 Brief description of the faculty’s environment As the university is part of the faculty’s environment, it is briefly described here as some features influence the faculty’s strategy. The university is medium‐sized and, having been established in 1990, relatively young. It has a regionally distributed structure with two campuses and central bodies at a third city. The academic profile includes educational disciplines, humanities, cultural, social and natural sciences, information technology and

For more detailed information on different IC models and initiatives implemented in universities and research centres see Sanchez and Elena (2006) and Sanchez et al. (2009).

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psychology. Since 1990 the number of students has almost tripled and reaches now about 13.000. About 900 students study at the faculty of interest (case faculty). Located in a rural area, there is no big city to attract students, nor does the area in general have a reputation as a research‐intense environment or international hub. Specialised researchers find their own and personal reasons to join this university, but attracting potential students, especially in Bachelor studies requires other arguments. Among them are the portfolio of studies and special combinations in topics, research focuses, facilities for students with children, quality of life in the area, and – important for this study – excellent quality in teaching. Due to the origin of one of the campuses in educational sciences and education of teachers, and the university’s underlying value system, quality in education and teaching has always played a major role in the profile of this university.

4.2 Strategic relevance of quality in teaching In 2009, at the beginning of bachelor courses, the faculty initiated a strategy development process with involvement of members of the faculty and of the university’s board of management. The strategy is currently being updated by the Dean and the Faculty Council. The quality in teaching and student counselling 9 has been defined as the strategic goal with highest priority. This is one of the sources feeding the case description. The other source lays in quality management initiatives that undergo major changes at the moment, due to new rules for organising Quality Management (QM) at this university. These two developments fed the idea of applying the IC logic for internal management, first of all to the field of quality in teaching. This goal deserves special attention not only due to its high priority but also due to the challenges that came with re‐structuring the curriculums from “Diplom” studies to bachelor and master studies. It is not only a redesign of curriculums with implications for students, but implies considerably higher workload for faculty staff. The number of exams per student has increased, and the number of thesis per student, if heading for a master’s degree, has doubled. Teaching in post gradual studies, PhD programs and advanced training are less affected by the Bologna reform. They are not in the focus of this case description. Currently, the faculty scores high in CHE ranking which reports on about 60 German speaking faculties and institutes in the field. The changes due to the Bologna reform are considered a threat to this important strategic value of quality in teaching. Additionally, several professors of this faculty are retiring these years, and delays in filling these vacancies, combined with new economic restrictions due to the larger economic situation, worsen the current staff situation. On the other hand, generational changes open up new possibilities for teaching practices. The risk of loss in quality at times of the Bologna reform was addressed at national and regional and by strategic recommendations:

In July 2008 the Science Council (“Wissenschaftsrat”), Germany’s highest political advisory body in this field, published recommendations for quality improvement in studies and teaching (Wissenschaftsrat, 2008).

In October 2010 an agreement between German Bund and Laendern was launched as the base for funding 10 quality initiatives in universities (Bundesanzeiger) .

It became obvious that the Bologna reform required far more than restructuring curriculums (Nickel, 2011) and that risk management in this change process has to include quality aspects in teaching. Implications of the Bologna process for understanding universities as professional institutions with teaching being one of their core processes should be present in strategies and management of universities and faculties. We are aware of the fact that this might raise opposition in the sense of the criticism described by Scott (2012), see above. The antagonism of freedom in research and teaching and collegial governance on one side and the use of managerial skills and tools on the other side has to be overcome. 9

Student counseling by the faculty includes information and support services for organizing the individual curriculum, career planning, and all related topics, e.g. combining special life circumstances with full‐time university studies. In the following this strategic goals is referred to as quality in teaching and includes the counseling. 10 http://www.bmbf.de/pubRD/vereinbarung_qualitaetspakt_banz.pdf

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A strategy statement about quality in teaching without consequences in processes, responsibilities and cultural aspects (factors of structural capital) is useless. A sound agreement about the strategic goal can best be reached by bottom‐up processes. Adequate allocation of resources must follow. In the case study, new rules for QM were established by the university that require a special commission and a designated representative for quality management (Q‐rep). In our case the faculty manager takes over this role, a person with experience in QM and business organisation, but with academic degree in the discipline of the faculty. This personnel decision introduces business thinking into the faculty while assuring common academic background. In times of very narrow financial restrictions, this may imply that a research position needs to be transformed into a management position. One or more.

4.3 Quality management framework at the faculty The quality management framework at the case faculty consists of the following components:

the legal framework;

university rules for assuring and improving quality;

a QM representative in the faculty;

a commission supporting the QM representative where the different categories of academic staff are equally represented;

a specialised unit for methodology (methodology center);

monitoring by regular evaluation of classes 11 , including didactical aspects of teaching, an activating character of classes, communicative competences of the professor/lecturer, practical relevance, availability of rooms, material, media, literature, etc., workload, and the achievement of learning goals

agreements about consequences in case of excellent or poor results;

agreements about confidentiality;

financial incentives and official praise in the faculty for excellent teaching;

individually tailored measures for improving quality;

counselling services for students.

Personalised results from evaluation are not published, but aggregated data will be reported to the university’s board of management and to external accreditation agencies. It took about three years to establish the relevant processes, responsibilities, reporting formats, agreements, etc. The development continues and always will.

4.4 Applied IC logic to the case study The IC logic we use here is derived from the German ICR (“Wissensbilanz”) framework, developed by a working group (“Arbeitskreis Wissensbilanz” 12 ). In this paper we describe briefly selected “business” processes and then focus on IC success factors and related indicators. To enrich the case results and give a more exhaustive overview, the following section is based on the case study as well as on literature review.

5. Managing teaching activities in universities according to an IC logic Based on the lessons learnt from the case study and from previous initiatives of IC management and reporting in universities 13 , to better manage teaching and education activities in European universities we suggest a 'cascade' logic which includes four main steps: developing a strategy in line with the overall organisational strategy, selecting key internal processes with strategic relevance, identifying the main success factors and, finally, the definition of related indicators. 11

The term" classes" includes lectures, seminars, etc. http://www.akwissensbilanz.org/index‐en.htm Sánchez y Elena (2006) and Sánchez at al. (2009)

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Figure 1: IC logic of the case study, derived from the German “Wissensbilanz” Although indicators always attract a lot of attention, it is important to make clear that all the steps of the process at equally important. Our final intention is not to provide a number of indicators to rank universities at European or national level but to be used at institutional level as a managerial instrument. The proposed indicators are to be used to improve internal management and, eventually, to increase the level of transparency of universities. It could be also a valuable tool for self‐assessment exercises.

5.1 Key processes and governance principles When enough documentation exists on processes, the most relevant organisational processes can be selected and described in more detail. This could be very beneficial for the IC exercise. In the case under study, the processes needed to be defined and described. It became clear that it is not only processes, but also principles of governance and organisational structure that is fundamental as a base for deriving relevant IC success factors. In the context of the Bologna process, according to the case study and in line with the elements highlighted by the TRACKIT Project 14 the following processes and governance principles should be considered:

Teaching refers to delivering all relevant components for the curriculum of interest, e.g. classes, workshops, empirical investigations, supervising Bachelor thesis, etc. It also includes exams.

Designing curriculums describes activities to design a goal‐oriented, market‐oriented, valuable curriculums with realistic workload. The curricula structure should reflect on issues such as: how much time it takes to study a BA, MA or PhD programme; number of students allowed by programme, ratio student/professor, or future employability of graduates.

Counselling for students means offering support in organizing the career, especially under time restrictions due to work, taking care of children or elder family members, shifting classes and/or modules for internships or studies abroad

Quality management in teaching is a meta‐process or a support process that contains all necessary elements for monitoring and improving teaching, e.g. regular monitoring with adequate instruments, measures in case of very high (positive) or low (negative) results. It also includes the internal activities for accreditation and re‐accreditation. Methods of internal quality assurance should be also considered.

Lifelong learning is a concept which implies flexible, diverse and available education at different times and places pursued throughout life with the aim of improving knowledge, skills and competence for personal or professional reasons.

International mobility of both students and academic staff.

Resources allocation to the education and teaching activities, including financial and non‐financial (such as personnel, infrastructures, etc.)

Institutional autonomy, particularly on those issues related to teaching. For instance, the capacity of the institution to set tuition fees, define the number of undergraduate and post‐graduate students, etc. 14

http://www.eua.be/trackit and (Mehleck 2012).

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5.2 Success factors and related indicators Following the 'cascade' logic mentioned before, a number of success factors and related indicators are shown in the tables below. They have been organised following the most widespread classification of IC in three pillars ‐ human capital, structural capital and relational capital and can become salient in the processes and implementation of the governance principles stated in section 5.1. To suggest one set of factors and indicators, we combine the success factors that emerged during the analysis of the case study with those from the literature review. Table 1: Suggested indicators for human capital with relevance for education and teaching HUMAN CAPITAL Personnel for teaching and student counselling Total number of academic personnel involved in teaching activities / total number of personnel (per field and gender) (Full Time Equivalent) Percentage of working time devoted to teaching, to research to administrative tasks Ratio student/professor by program Training and coaching for teachers (number of courses, budget devoted to training, types of qualification measures), should include didactical competences and social competences Qualification of and training and coaching for counselling personnel Level of satisfaction of personnel with the institution (department or university) International mobility of staff Incoming: Number of visiting teachers and professors from other universities/Number of teachers and professors (per field,) (A. National, B. International country of origin and gender) Outgoing: Number of teachers and professors with visiting positions in other universities / /Number of teachers and professors (per field and gender) (A. National, B. International ‐ including country of origin and information on the funding program supporting the mobility) Personnel for quality management Personnel in charge of quality management (Full Time Equivalent) Background of and training for QM staff Personnel for counselling Number of FTE dedicated to student counselling Background of and training for staff involved in counselling Lifelong learning Total number of academic personnel involved in lifelong learning activities / total number of personnel (per gender)

Table 2: Suggested indicators for structural capital with relevance for education and teaching STRUCTURAL CAPITAL Strategy Existence of a Strategic Plan for teaching and lifelong learning in accordance with the overall strategy Quality management as a strategic goal Existence of mechanisms to evaluate the Strategic Plan (frequency) Lifelong Learning Centre Existence of a Lifelong centre (area or department) (YES, No) Description of activities carried out Target group (type of student by field) Budget devoted to lifelong learning activities/ total budget devoted to teaching and education activities Resources for teaching and learning (excl. personnel) Amount of resources devoted to teaching / Total Budget (personnel cost is not included) (3 levels: bachelor, master and PHD) Sources of funds devoted to teaching Resources available for PhD scholarships Satisfaction of staff with material and rooms available for teaching Satisfaction of students with material and rooms available for teaching and learning

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Table 3: Suggested indicators for relational capital with relevance for education and teaching RELATIONAL CAPITAL Students Incoming: Number of students coming from other universities/Total number PhD students (per field, country of origin) (A. National, B. International) Include information on the funding programme supporting the mobility. (distinguishing by BA, Master and PHD) Outgoing: Number of students studying abroad Outgoing: Number of students with internships abroad Employability of graduates (by sector of activity: academia, governmental bodies, industry) Level of satisfaction of students with programs Potential students Measures used to attract students (number per category, e.g. presentations, fairs, web forums, etc.) Criteria and mechanisms for selecting undergraduate, graduate students and PhD students Response times to potential student’s requests (from request to satisfying response) Relationship to external organisations and practitioners Number of contacts to business, non‐profit and public organisations, institutes, etc. (state only contacts that can be used for teaching, internships, research thesis, etc.) Number of external practitioners in teaching Number of bachelor, master and PhD thesis in cooperation with external organisations Activities of the system of interest (e.g. university, faculty, etc.) for relationship management with external organisations (number per category, e.g. presentations, workshops for the public, etc.) Alumni Number of registered alumni Activities with involvement of alumni that aim at teaching and development of students

Such a list of indicators can never be exhaustive, but it provides suggestions to be tailored (specified, reduced and extended) to the requirements of the individual scenario. A discussion on challenges in the application of success factors and indicators is given in the next section.

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6. Preliminary conclusions, challenges and ways forward Based on the literature review and the case study presented, this paper suggests a 'cascade logic' for the identification of key processes, success factors and related indicators for a more strategic management of teaching and education activities in European universities. The set of indicators proposed followed the IC approach according with the most widespread scheme in three pillars: human, structural and relational capital. The literature review showed that most of the IC indicators in use cover research and third mission, but offer less input in the field of teaching and education. Quality management initiatives in times of the Bologna reform provide excellent components to be considered in IC management in education and teaching. The case study served for taking a close look at the potential of QM for IC management and at the challenges to be faced when applying the cascade logic all the way from strategy development to indicators. Some of the main experiences were the following:

For strategy development, the legal framework of public universities makes a difference to the business sector. This implies that specific competences for “university management” need to be developed, and education and teaching should have its place besides science and research management.

It is important to find ways to understand and take into account implicit value systems, beliefs and mental models of staff. As described in the historic overview, the Bologna reform means a value change, recovering in some sense the value of teaching and education that was part of the Humboldt model of universities but has slowly been replaced by a focus on research outputs and international visibility (also based on research). Quality and IC management in teaching and education put a threat on some basic values in universities as they raise the importance of teaching (and counselling) and imply disclosure in an area where freedom is considered one of the main features of the profession.

Considering this, it is easy to understand that QM and IC management in teaching and education can only be successful if those areas are perceived as strategically relevant. In universities with a strong profile and international visibility in research ‐ or in a highly attractive cosmopolitan location that attracts students by itself – it might be much harder to establish reporting in teaching. In the case study analysed faculty staff is aware of the fact that excellence in teaching and counselling is crucial for the faculty’s competitive strength in the market. In general, quality in teaching should be among the strategic goals, developed by a participatory process with involvement of all staff. The way from developing a sound strategic orientation to establishing useful, practical and valuable indicators that reflect all strategic aspects is very long. This is especially the case where `business´ thinking is still considered a threat and managerial competences have not been yet developed by existing staff. It is tempting to skip the way through identifying and describing core processes and related success factors, and jump directly to selecting indicators where data are available, just to present something that satisfies the call for data based reporting. However, since indicators have to be used for managerial purposes, we strongly recommend taking sufficient time to run through the full process from the definition of the strategy to the definition of indicators. The need for involvement of staff depends on the type of governance of each faculty. For sustainable implementation of QM or IC management in teaching, reporting results (and consequences derived) need to be accepted by teaching staff. The indicators suggested in this paper, thus, serve as an orientation also for strategic thoughts. They should not be applied without a sound process of strategic anchoring. Still there is a clear need for further investigation in methodological issues concerning IC framework and indicators, especially for HE institutions. The effort might be worth it if we can support universities on their way to professional management of one of their main “products”: higher education, which is a crucial contribution to Europe’s place in a competitive world economy.

References Amaral, A. and Magalahes, A. (2004), Epidemiology and Bologna saga, Higher Education, Vol 48(1), pp. 79‐100. Berndtson (2003) “The European Higher Education Area: to Change or not to Change?”, The EpsNet General Conference. Paris, June 13‐14, 2003. Bologna Declaration (1999), “The European Higher Education Area”, Joint Declaration of the European Ministers of Education, Bologna, 19 June 1999.

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Bundesministerium für Bildung und Forschung (2010), Bekanntmachung der Verwaltungsvereinbarung zwischen Bund und Ländern gemäß Artikel 91b Absatz 1 Nummer 2 des Grundgesetzes über ein gemeinsames Programm für bessere Studienbedingungen und mehr Qualität in der Lehre vom 18. Oktober 2010, Bundesanzeiger ‐ Amtlicher Teil, Donnerstag, 28. Oktober 2010, Nummer 164, Seite 3631‐3633. Available at: http://www.bmbf.de/pubRD/vereinbarung_qualitaetspakt_banz.pdf Communiqué of the Conference of European Ministers Responsible for Higher Education (2003) “Realising the European Higher Education Area”, Berlin, 19 September 2003 ERAWATCH (2008) Activities of the EU member states with regard to the reform of the public research base. Available at: http://erawatch.jrc.ec.europa.eu/erawatch/opencms/information/reports/era_reports/ European Commission (2003). “The role of the Universities in the Europe of Knowledge”. Brussels 05/02/2003, COM (2003) 58 Final. European Commission (2005). “Mobilising the brainpower of Europe: enabling universities to make their full contribution to the Lisbon Strategy”. Brussels, 20/04/2005, COM (2005) 152 Final. European Commission (2006), “Delivering on the Modernisation Agenda for Universities: Education, Research and Innovation”, Brussels, 10/05/2006, COM (2006) 208 Final. European Commission (2011) Supporting growth and jobs – an agenda for the modernisation of Europe's higher education systems. COM(2011) 567 final. Brussels, 20.9.2011 Leitner, K.H. (2004), “Intellectual Capital reporting for universities: conceptual background and application for Austrian Universities”, Research Evaluation, Vol.13, number 2, august 2004, pp. 129‐140, Beech Tree Publishing, Surrey, England. Malagón, L.A. (2005). “Cambios y Conflictos en los Discursos Político‐Pedagógicos sobre la Universidad”, Archivos Analísticos de Políticas Educativas, Vol. 13, Nº 22. Available from: http://epaa.asu.edu/epaa/v13n22/ Martin, B.R. (2003), “The changing social contract for science and the evolution of the university”. In Geuna, A., Salter, J.A., and Steinmueller, W.E. (2005) (Eds), Science and Innovation. Rethinking the rationales for Funding and Governance. Ed. Edward Elgar, pp. 7‐29. MERITUM (2002), Guidelines for Managing and Reporting on Intangibles (Intellectual Capital Statements), Vodafone Foundation, Madrid. Molas‐Gallart, J. (2005), “Defining, measuring and funding the Third Mission: a debate on the future of the university”, Coneixement i Societat, Vol. 7, January‐April, pp. 6‐27. Muehleck, K. (2012), "On the tracks of students and graduates: methods and uses of tracking procedures in the European Higher Education Area". In Curaj et al. (Eds.) (2012), European Higher Education at the crossroads. Between the Bologna process and national reforms, Part 1, pp. 223‐243. Nickel, S. (2011) (Ed), Der Bologna‐Prozess aus Sicht der Hochschulforschung: Analysen und Impulse für die Praxis, working paper no 148, CHE gemeinnuetziges Zentrum fuer Hochschulentwicklung, Sep 2011. Nickel, S. (2011), Zwischen Kritik und Empirie – Wie wirksam ist der Bologna‐Prozess? In Nickel, S. (Ed), Der Bologna‐ Prozess aus Sicht der Hochschulforschung: Analysen und Impulse für die Praxis, working paper no 148, CHE gemeinnuetziges Zentrum fuer Hochschulentwicklung, Sep 2011. Observatory of the European University (2006), “Methodological Guide”, Final Report of the Observatory of the European University, PRIME Project. Available at: www.prime‐noe.org Paradeise, C. (2012), "Tools and implementation for new governance of universities: understanding variability between and within countries". In Curaj et al. (Eds.) (2012), European Higher Education at the crossroads. Between the Bologna process and national reforms, Part 2, pp. 573‐598. Sanchez, P. M. and Elena, S. (2006), “Intellectual capital in Universities. Improving transparency and internal management”, Journal of Intellectual Capital, Vol. 7, No.4, pp. 529‐548. Sanchez, P. M., 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. Scott, P. (2012), "Going beyond Bologna: issues and themes". In Curaj et al. (Eds.) (2012), European Higher Education at the crossroads. Between the Bologna process and national reforms, Part 1, pp. 1‐14. Winter, M. (2011), Die Revolution blieb aus: Ueberblick ueber empirische Befunde zur Bologna‐Reform in Deutschland. In Nickel, S. (Ed), Der Bologna‐Prozess aus Sicht der Hochschulforschung: Analysen und Impulse für die Praxis, working paper no 148, CHE gemeinnuetziges Zentrum fuer Hochschulentwicklung, Sep 2011. Wissenschaftsrat (2008), Empfehlungen zur Qualitätsverbesserung in Studium und Lehre, Drs. 8639‐08. Available at: http://www.wissenschaftsrat.de/download/archiv/8639‐08.pdf

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The Role of Human Capital and Customer Capital in Supporting Product Innovation Ahmed Elsetouhi and Ibrahim Elbeltagi Business School, Plymouth University, Plymouth, UK Faculty of commerce, Mansoura University, Mansoura, Egypt ahmed.elsetouhi@plymouth.ac.uk i.elbeltagi@plymouth.ac.uk Abstract: The purpose of this paper is to adopt a new model for the relationship between human capital (HC) and product innovation via customer capital (CC) in the service sector. The authors gathered data from 773 employees (65.5% response rate) in Egyptian banks. We applied structural equation modelling (SEM) by PLS to examine the proposed theoretical model. The research findings indicate that HC has a positive effect on CC. In addition to, product innovation is significantly associated by CC. Moreover, CC partially mediates the relationship between HC and product innovation. This paper should benefit for academics as it contributes to the literature by empirically testing these relationships by this sequence in banks. It should also support some practitioners in banks such as human resource manager and marketing manager. Keywords: human capital, customer capital, product innovation, SEM, banks

1. Introduction In the era of the knowledge‐based economy, HC is a key driver to create a new value for firm and to support both firm performance and competitive advantage (Chen, Shih, and Yang 2009; Dokko andRosenkopf 2009; Nordenflycht 2011). Mill (1848 cited in Ethiraj and Garg 2012) mentioned that organization which has unskilled employees introduces mercy competitive forces in a turbulent environment that may lead to failure. Firm pays more investments in HC to improve its knowledge and skills which reinforce the performance of both employees and firm (James 2000). On the same vein, although internal resources such as HC have many benefits for firms, the integration these resources with external ones for example CC will maximize the total performance of stakeholders (Lau et al. 2010). Customers are cornerstone for any business as well as they are a key factor to get successful innovations through providing a marketing knowledge which reduces uncertainty position (Rohrbeck, Steinhoffand Perder 2010). Moreover, the understanding of customer needs will maximize the ability of firms to generate new ideas which are considered a main source to get new products and services (Schirr 2012). Moreover, Kammerer (2009) confirmed that customer plays a significant role in supporting product innovations.

On the other side, innovation is a key requirement for survival and growth profitability in these environments (Bohlmann et al. 2012). It is an important factor for firms in the competitive battle (Wilson, Ramamurthy and Nystrom 1999). Moreover, Yang (2004) confirmed that most firms’ profits are developed from innovations. Therefore, many organizations have considered innovation is a critical point between life and death (Govindarajan and Trimble 2005). Furthermore, firms believed that innovation is a short way to achieve their goals but it needs many factors to sustain its activities (Cooper 2011). Additionally, innovation is a main resource to achieve sustainability and economic growth in the 21st century (Gumusluog and Ilsev 2009; Atalay and Anafarta 2011). By analysing the literature review, many researchers examined the relationship between HC, CC and product innovation, specifically, incremental and radical innovation in the manufacturing sector (Subramaniam and Youndt, 2005; Marques, Simon and Caranana 2006). Although innovations have a central role in supporting the growth in both the manufacturing sector and the service sector (Roy and Sivakumar 2012), most innovation researches have mainly focused on innovation in the manufacturing sector more than this in the service sector (Droege, Hildebrand and Forcada 2009; Perks, Gruber and Edvardsson 2012). Similarly, the previous studies proved that a firm aligning its strategies, resources and competences with the market conditions may better understand its market which consequently enhances its product innovation performance (Zhang et al. 2009). These interactions give more power to an organisation in order to increase product innovation activities and crystallize new ideas into the firm’s organizational intelligence (Song et al. 2011). Both the generation of information by customers as well as the integration of CC with employees’ skills lead to support innovation though reducing market uncertainty (Rohrbeck et al. 2010). Consequently, this

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Ahmed Elsetouhi and Ibrahim Elbeltagi study contributes to the literature by empirically testing the interaction between HC and CC in reinforcing product innovation in the service sector.

In the following section, the literatures HC, CC and product innovation are reviewed to formulate research hypotheses. Secondly, research method is presented followed by data analysis and findings. Finally, the results discussion, implementations, research limitations and future research will be presented.

2. Theoretical background 2.1 Human capital The resource‐based literature has held that HC is one of the key strategic assets of a firm. It has stressed that only firm‐specific HC is an important source to generate organizational rents. Furthermore, these assets are more likely to be inimitable and rare. Therefore they are considered a better basis for sustained competitive advantage (Galunic and Anderson 2000). HC related to employees’ competences that include knowledge, skills, capabilities, experience, education, attitude and employees’ commitment to business goals (Edvinsson and Malone 1997; Roos and Roos 1997; Ulrich 1998; Chen, Shih and Yang, 2009; Hsu and Sabherwal 2012). It is embedded in employees’ minds not in firms ( Lee et al. 2011). HC is a significant resource for both innovation and competitive advantage ( Hsu and Wang 2012; Kim et al. 2012). HC is considered the heart of intellectual capital ( Bontis 2004; Bollen, Vergauwen and Schnieders 2005). When firms have skilled employees, they generated new ideas and techniques which reflect on improving the production processes, products and services delivery methods (Wang 2006). Although competence is a main component in HC, it is not enough to achieve business goals so we also need the employees’ commitment to work hardly. Therefore, HC should have competences and commitment (Ulrich 1998).

2.2 Customer capital The Market‐based View (MBV) recognizes the role of specific marketing resources such as customer relationships in gaining and sustaining competitive advantage. In order to be successful in the high‐technology market environment, a company should not only create a more visible future for technology but also have a more effective relationship with customers (Yang and Kang 2008). CC is the knowledge which embedded in the marketing channels and customer relationships with firm ( Bontis et al. 2000; Sharabati et al. 2010). CC is a vital source of current and future revenues, and competitive edge in the knowledge economy (Stewart 1997; Edvinsson and Malone 1997; Bontis et al. 2000; Duffy 2000). So, if firms have no good relationships with customers, they will have to resort to sales promotions to draw new customers who sometimes are unprofitable to the firm ( Chen, Zhu and Xie 2004). It represents a major mechanism that converts organisational resourecs into value (Shih et al. 2011).

2.3 Product innovation Ettlie and Rosenthal, (2011) metioneded that innovation has come relatively recently to the service sector. R&D spending in that dominant sector has just recently started rising. Product innovation is a main source of organisation’s profitability as new product contributes 30.6 % of profits of firm (Bohlmann et al. 2012). Firms can gain more flexibility to adapt with new customers’ requirements by product innovation (Lichtenthaler and Ernst 2012). Product innovation is the result of a creative process involving different actors from one or more organizations. It leads to a qualitatively new means‐end combination introduced to the market (Kock et al. 2011). It must contain different features for customers through modifying an existing product or creating a new one (Bohlmann et al. 2012). Product innovations involve product‐ technology innovation and product‐ market innovation (Gallouj and Weinstein 1997; Vries 2006). The former often uses advanced technologies to develop current customer benefits (Chandy and Tellis 1998) while the latter seeks to find new customer segments (Tushman and Anderson 1986). In banks, product innovations embrace ATMs, credit card, debit card, personal banker and mortgage (Gopalakrishnan et al. 1999).

3. Research hypotheses Figure 1 suggests a conceptual model which provides a view of direct and indirect effects of the research relationships.

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Ahmed Elsetouhi and Ibrahim Elbeltagi H3b

Human Capital -Competences -Commitment

Custome r capital H3a

Product Innovation -Product-Technology- Innovation -Product-Market-innovation

Figure 1: Conceptual model

3.1 Human capital and customer capital In banks, employees directly connect to customers. Their shills and competences affect customer satisfaction and support to build long‐term relationships ( Wang and Chang 2005; Kim, Kim and Kim 2009). Bontis et al. (2000) mentioned that the positive relationship between HC and CC is significant regardless of industry type (service or non‐service). Similarly, Shih, Chang and Lin (2010) and ( Kim et al. 2012) reported that HC has a positive and direct influence on CC. Employees who have high skills to interact with customers through providing knowledge from or to customers will maintain current customers and attract new ones ( Rudez and Mihalic 2007; Hsu and Fang 2009; Chen et al. 2009). Furthermore, RBV has stressed that a firm which adheres to market orientation examines the customer needs and then seeks to develop its resources such as employees’ skills and competences to serve the customers (Paladino 2007). So Firm’s market is made up of a network of close relationships between its customers and employees. In light of the aforementioned reasoning, the following hypothesis is developed: H1: Human capital is positively associated with customer capital.

3.2 Customer capital and product innovation Services require greater customisation than manufactured goods. It is possible that a customer orientation enhances innovation in services firms more uniformly (Jelena et al. 2012). Kammerer, (2009) found that market considerations are especially important for product innovations. Firms may use environmental improvements to differentiate their products from others and thus gain a competitive advantage. Customers are a significant source of knowledge to innovation (Schirr 2012). Customer involvement has long been played an important role in producing successful product and service development (Carbonell et al. 2009). Firms create new products based on anticipating customer needs as customer dynamics significantly interact with product innovation (Bohlmann et al. 2012). In service sector, organisations recognise that customer is a cornerstone of successful service development (Carbonell et al. 2009). Moreover, the high level of customer orientation produces a high rate of innovation. Hence, firms get more chances to successfully produce new products (Paladino, 2007; Spanjol, Mühlmeier and Tomczak 2012). Similarly, Chen, Lin and Chang, (2006) found that CC is positively associated with new product development in Taiwan SMEs. Furthermore, CC has a positive effect on innovation in both manufacturing and non‐manufacturing industries (Wu et al. 2008). Following this line reasoning, the following hypothesis is proposed: H2: Customer capital is positively associated with product innovation.

3.3 Human capital and product innovation Previous studies viewed that HC is an essential resource for innovation. HC positively affects innovation (Bornay‐Barrachina et al. 2012). Marvel and Lumpkin (2007) also indicated a positive relationship between radical innovation and HC. Moreover, both Wu et al. (2008) and Zerenler et al. (2008) mentioned that HC can promote innovation however the later focused on innovation performance in an automotive supplier industry. Furthermore, HC is significantly associated with new product development performance in Taiwan’s SMEs (Chen et al. 2006). In contrast, Subramaniam and Youndt (2005) found that HC had a negative effect on radical innovation capabilities and non‐significant relationship with incremental innovation capabilities. However, these relationships were improved via social capital.

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Ahmed Elsetouhi and Ibrahim Elbeltagi Based on the above conflicting results, we directly and indirectly test this relationship. If highly – skilled employees with poor knowledge about customers, it is expected that the skills of employees will be not enough to support product innovation. Consequently, this paper suggests the following hypotheses: H3a: Human capital has a positive effect on product innovation. H3b: Human capital has an indirect positive effect on product innovation through customer capital.

4. Research method 4.1 Sample and data collection procedure Cross‐sectional survey data from Egyptian banks is employed to test the hypotheses. Due to the geographical dispersion of Egyptian banks, multi‐stage sampling is used (Saunders et al. 2009). The questionnaire was piloted and we used back‐translation technique to produce an Arabic version. After follow‐up phone calls and reminder visits, 755 of the questionnaires (1119 questionnaires) were returned, 733 of which were useable (65.5% response rate).

4.2 Measures Each construct has measures with multi‐item scales and five‐point Likert type items ranging from “1= strongly disagree” to “5 = strongly agree” (Appendix 1). HC is a product of competence and commitment. We adopted an eleven‐item scale to measure HC, five items for competence (Ulrich, 1998) and six items for commitment (Farndale et al. 2011). On the other side, Wu et al. (2008) developed a seventeen‐item scale from Bontis, (1998) to measure customer capital. After they tested the reliability and validity of questionnaire, they concluded that six items are acceptable with Cronbach’s α = 0.913. Finally, product innovation is measured by fifteen items. Product‐technology innovation is measured by a seven‐ item scale which adopted from (Bantel and Jackson 1989; Zhou, Yim and Tse, 2005; Wu et al. (2008); Camisón and López, 2010; Carmona‐Lavado, Cuevas‐Rodríguez and Cabello‐Medina 2010). While eight items based on Bantel and Jackson, 1989; Avlonitis, Papastathopoulou and Gounaris 2001; Zhou et al. (2005) are developed to measure product‐market innovations.

5. Data analysis Version 3.0 software package of partial least squares (PLS) which is a technique of structural equation modeling (SEM) was used to examine the research hypotheses. Before testing the structural model, measurement model was evaluated to determine the reliability and validity of constructs (Al‐Somali et al. 2009).

5.1 Measurement model It aims to determine internal consistency reliability and to assess discriminant and convergent validity of the measures. These tests show the strength measures to investigate the research model. Table 1 demonstrates that the factor loadings of the items. All reliability measures both composite reliability and cronbach’s alpha coefficients are above 0.70 which are consistent with the recommendation of (Hair et al. 2010) except for cronbach’s alpha (α) of HC was 0.60 but it did not affect the fit model. As well as these loadings are significant (p<0.001). Therefore, the measures had enough internal consistency. Table 1: Combined loading, reliability and validity of the constructs Construct and Loading items Human capital Competences 0.837 Commitment 0.837 Customer capital (CC) CC1 0.728 CC2 0.803 CC3 0.792 CC4 0.818 CC5 0.811 CC6 0.807

Cronbachs alpha (α) 0.600 0.882

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Composite reliability (CR) 0.823 0.911

Average variance extracted (AVE) 0.70 0.63

Ahmed Elsetouhi and Ibrahim Elbeltagi Construct and Loading items Product innovation (PI) PI1 0.810 PI2 0.800 PI3 0.799 PI4 0.800 PI6 0.641 PI12 0.721 PI13 0.826 PI14 0.841 PI15 0.829

Cronbachs alpha (α) 0.925

Composite reliability (CR) 0.938

Average variance extracted (AVE) 0.62

In addition, the average variance extracted (AVE) is actually over the tolerance threshold of 0.5 according to Hair et al. (2010). On the other side, the square roots of average variance extracted (AVE) is employed to examine the discriminative validity (Fornell and Larcker 1981). It should be higher than the correlations between the dimension and others in the measurement model as seen in Table 2. Both Tables 1 and Table 2 confirmed that the instrument has discriminant and convergent validity. Consequently, we are ready to test the research hypotheses as the measures have solid constructs. Table 2: Factor correlation matrix and discriminant validity Construct CC HC PI

CC 0.794 0.629*** 0.738***

HC 0.837 0.594***

PI 0.792

P<.001

5.2 Structural model or testing the research hypotheses Structural equation modelling was used to test the research hypotheses. Figure 2 displayed the path and determination coefficients of the research model. According to the direct relationships, H1 supposed that HC associated positively with CC. H2 suggested that higher levels of CC support higher rates of product innovation. As shown in figure 1, there is a significant effect of HC on CC (β=0.63, p<0.01). Furthermore, the coefficient of determination (R2) for CC (endogenous variable) was 0.40. This means that 40% of the variety in CC is explained by HC. As well as the influence of CC on product innovation was significant (β=0.61, p<0.01). Therefore, H1 and H2 were accepted. H3b, β=0.39 R2=0.58

R2=0.40

Human Capital

-Competences -Commitment

H1, β=0.63

Product Innovation

Customer capital

H2,β=0.61

-Product-Technology- Innovation -Product-Market-innovation

H3a, β=0.21

Figure 2: Results of structure Moreover, to test the mediated effect, Shu et al.( 2012) recommended investigating three models as shown in Table 3. The first model was a non‐mediation model that had only direct path from HC to product innovation. The second model named a partial mediation includes direct and indirect effect. The final model is full mediation model as it relates to direct relationship between HC and a mediating variable (CC); and between the CC and product innovation. Table 3: The path coefficients of models Variables HC CC CC PI HC PI HC CC PI Total effect of HC on

Direct effect β 0.60* 0.60**

Partial mediation β 0.63* 0.61* 0.21* 0.39** 0.61**

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Full mediation β 0.63* 0.75* 0.47** 0.47**

Ahmed Elsetouhi and Ibrahim Elbeltagi Variables PI R2

CC PI

Direct effect β

Partial mediation β

Full mediation β

0.36

0.40 0.58

0.40 0.56

*p< 0.01 / **p< 0.001. We chose the second model (partial mediation) as its coefficients of determination (R2) is higher than those of others. As well as the total effect of HC on product innovation in the second model is the biggest (0.61). In terms of H3, The above table confirmed that HC has directly positive effect on product innovation (β=0.21, p<0.01). Furthermore, CC partially mediates the relationship between HC and product innovation (β=0.39, p<0.001). So the total effect (direct and indirect effects) of HC on product innovation is 0.61(p<0.001). H 3 (a,b) are accepted. Finally, the integration between HC and CC can explain 58% of the variety in product innovation.

6. Discussion HC and CC are part of intangible assets. HC and CC are key sources of innovation ( Kim et al. 2009). We have suggested three hypotheses to test the interrelationships between HC, CC from a cause–effect perspective and their effects on product innovation in banks. The empirical results suggest that the research hypotheses are accepted. We investigate the direct interaction among HC, CC and product innovation. The analysis H1 shows that the effect of HC on CC is significant and positive, which is consistent with the previous results of (Bontis et al. 2000; Shih et al. 2010; Kim et al. 2012). Employees’ competence, skill and commitment support customer capital through acquiring and sharing customer knowledge. This means that banks possess HC that has an ability to build a strong relationship with customers through maintaining a customer loyalty and satisfaction, additionally; it creates a good bank image. Therefore, the more competent employees will provide the better understanding to their customers’ needs which positively reflect on their mutual relationships and market share. These skills give more flexibility for banks to adapt with unexpected customers’ needs. On the other hand, the good relationships between employees and customers will encourage customers to be pro‐active in providing suggestions (and in some cases complaints) which may improve employees’ skills to introduce the high quality banks’ services.

Furthermore, we assume that CC plays an important role in supporting product innovation in banks which is translated in H2. The analysis results confirm our expectation which means that customers believe that if they want to get a new bank service, they should cooperate with banks to innovate new products. This result is consistent with the studies of (Chen, Lin and Chang 2006; Wu, Chang and Chen 2008). Hence, banks should consider customers as an important asset for a product innovation. Customers are not only able to create their own products but also willing to participate during innovation processes. Customers’ involvement into product innovation may increase the success of new products (Bartl et al. 2012). In terms of H3a and H3b, the findings confirm that HC has a directly positive effect on product innovation (β=0.21, p<0.01) and this result is consistence with many previous studies such as Hayton (2005) and Bornay‐ Barrachina et al., (2012). Employees’ skills improved the abilities of firm to speedily adopt new technology and knowledge which are translated into new products and service. As well as CC mediates the relationship between HC and product innovation (β=0.39, p<0.001). Hence, the path coefficient of indirect effect of HC on product innovation via CC is bigger than this of direct effect. This means that the integration between HC and CC will better affect product innovation on introducing a successful product. So banks should establish close relationships between their customers and employees to produce new services. Based on the above discussions, this study contributes to the literature by (1) it empirically addresses the relationship between HC, CC and product innovation in the service sector which is was scantily studied in literature; (2) it test the mediating role of CC in supporting the relationship between HC and product innovation; (3) it introduces a new research model bringing together in this sequence; (4) it develops and validates new constructs that measure a product innovation in service industry and a new geographical area(developing countries, Egypt).

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7. Managerial implication This paper provides managers, in different levels, with benefits that can facilitate how banks leverage human capital and customer capital to innovate new products in Egyptian banks. Firstly, this study confirms that human capital is an important asset to support product innovation. Therefore, the human resource manager should recruit and retain employees who have high skills and competence to generate and apply new ideas through preparing effective procedures of recruitment and selection that objectively choose the best candidates that will add value to the bank. Secondly, this paper showed that customer capital (as a mediator) has a key role to support innovation so marketing manager should build long‐term relationships with customers by focusing on orientation customer Hence, human capital is the main source of generation new ideas and customer capital is related to the information about customer’s needs, satisfaction and loyalty.

8. Limitation and future research This paper has some limitations which should be noted. Firstly, it focused on Egyptian banks only which may give a chance for other researchers in developed countries to validate the research model. Secondly, we limited our study to focus on two factors which have affected on product innovation, but we recommend that we can add more variables such as organizational capital, social capital as independent variables or process innovation and organizational innovation as dependent ones. Thirdly, we suggest testing the benefits of innovations to employees, customers, formal and informal organization.

Appendix A 1.Human capital Competences

Our bank acquires new talent by hiring individuals from outside the bank. Our bank develops talent through programs such as formal job training. Our bank retains the most talented employees. Our bank forms partnerships with people outside the bank to find new ideas. Our bank punishes individuals with low performance.

Commitment

Externally, I say this is a great bank to work for. I am proud to tell others that I am part of this bank. I really care about the fate of this bank. I am willing to put in a great deal of effort in order to help this bank to be successful.* I feel very little loyalty to this bank. * It would take very little change in my present circumstances to leave this bank.*

4.Customer capital

Our customers would indicate that they are generally satisfied with our bank. Our bank tries to offer the best service to customers in the banking industry. We get lots of feedback out of our customers’ wants. We strive to meet with customers’ wants. Our bank is heavily market oriented. We are confident of our future with customers.

5.Product Innovation Product‐technology‐ innovations

Our bank introduces technologically new products to the market. Our bank introduces technologically improved products to the market. Our bank develops its services speedily. Our services are innovative. Our services incorporate a new technological knowledge. Our bank is able to replace obsolete service. Our bank links ATMs to the country ‐wide network.

Product‐market‐ innovations

Our new service concept is difficult for mainstream customers to understand. Our new services involve high switching costs for mainstream customers.

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Ahmed Elsetouhi and Ibrahim Elbeltagi Use of our new service requires a major learning effort by mainstream customers. It takes a long time for mainstream customers to understand our product’s full benefits. Our bank innovates many services like packaged accounts/ services for target market. Specific services are designed for market segments. Our bank offers services with new features compare to competitive products. The new service requires a change in the customer’s buying behaviour (e.g. way of buying by using Visa card). *points to the omitted items after pilot study.

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Effect of Investments on Training and Advertising on the Market Value Relevance of Intangibles Lidia Garcia‐Zambrano, Arturo Rodriguez‐Castellanos and Jose Domingo Garcia‐Merino University of the Basque Country UPV/EHU, Spain lidia.garcia@ehu.es Abstract: This paper seeks to analyse the influence that the spending on intangible resources, specifically training and advertising, has on the corporate market value. Expenditure on intangibles is an investment in business management terms. A sample of the main Spanish companies, those quoted on the IBEX‐35, was selected and the relationship was analysed between investment in intangible resources (training being representative of Human Capital and advertising of Relational Capital) and the market value relevance of the company’s intangibles (measured using Tobin’s Q) in two periods: 2006‐2009 and 2008‐2011. We thus obtain that both investing in training and investing in advertising separately have a positive impact on Tobin’s Q. Regarding the joint effect, there is a positive and significant relationship with Tobin’s Q in the first period analysed and no significant relation in the second period analysed. That is, investment in intellectual capital dimensions generates increases in the future market value of the firm. The results are similar using different rates of depreciation. Keywords: intellectual capital, investment, depreciation, business value

1. Introduction 1 It is widely accepted that intangible resources are those that generate economic growth in many economic sectors. Investing in intangibles is the path that companies must follow to consolidate their competitive position and ensure long‐term growth. Recent studies (Rodríguez‐Castellanos et al., 2010) show how the companies that believe it is important to invest in intangible resources obtain better economic results than those that do not. Numerous authors acknowledge that knowledge is the main value source of corporate intangible resources (Marr and Ross, 2005). Given the speed at which the required skills and knowledge change and the employees being considered as an essential competitive resource to obtain a lasting competitive advantage (Guerrero and Sire, 2001), training is one of the essential factors that determine the efficiency of the organisations. Furthermore, more and more companies are striving to maintain customers; the main reason is based on the value of the clients for the companies (Aaker, 1992). Therefore, this paper focuses on the influence of Intellectual Capital on business value. The aim is to highlight, in the Spanish case, the impact of investment in two dimensions of Intellectual Capital on the corporate market value. We have taken the Human Capital and Relational Capital as the main dimensions of Intellectual Capital. Some models are put forward that analyse the impact of the accumulated investment in each dimension on the market value of the company and a further one that analyses the joint impact of IC dimensions on the market value of the company. The paper is structured as follows: the second section, on the grounds of Resource Based View, justifies the key role of intangible resources in business competitiveness. It likewise reviews the literature that considers the relevance of knowledge‐based intangibles, that is, Intellectual Capital. The same section also considers the effect of the two dimensions of Intellectual Capital, that is, Human Capital and Relational Capital, and the investments made in it, on the value of the company. The methodology used is subsequently set out in the third section. The results obtained are contained in Section four and the paper then concludes.

2. Resource based view and intellectual capital: Concept and value creation The Resource Based View has made a decisive contribution to strategic management. Different authors noted that companies have or control a wide variety of resources and combinations of them (capabilities) that are 1

The research reported in this paper has been funded by the University of the Basque Country (Research Project EHU 11/37and Unity of Training and Research UFI 11/51) and the Basque Government (Research Project S‐PC12UN018)

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Lidia Garcia‐Zambrano, Arturo Rodriguez‐Castellanos and Jose Domingo Garcia‐Merino essential for the company to be able to operate. These resources have intrinsically different levels of efficiency, some of which are superior to others. Therefore, companies endowed with superior resources will have a greater likelihood of performing better, provided that the cost of acquiring them is lower than the value obtained as the result of the competitive advantage generated by those resources (Wernerfelt, 1984; Barney, 1986, 1991, 2001; Grant, 1991; Teece et al., 1997). This is the origin of the Resource Based View. There are different names for these resources that are so fundamental: “critical resources” (Wernerfelt, 1984), “strategic factors” (Barney, 1986), etc. Amit and Shoemaker (1993) highlighted five characteristics that differentiate them: inimitable, rare, valuable, non‐transferable and non‐substitutable. We have added durability to the aforementioned characteristics. Intangible resources are those that, lacking a financial or physical form and being constructed by the company over time, combine all these requirements with greater facility, and therefore become the key factor of business competitiveness more frequently (Hall, 1992; Lev, 2001). This statement is particularly applicable to the intangible resources based on knowledge, that is, to Intellectual Capital. According to Sánchez Medina (2003), Intellectual Capital (IC) is the combination of intangible resources or intangibles of an organisation, including personal knowledge, capacity for learning and adapting, relations with customers and suppliers, brands, internal processes, R&D capacity, etc., that, regardless of whether they are reflected on the financial statements, are directly or indirectly controlled by that organisation and generate or will generate future value for the company, so that they can underpin sustained competitive advantage. Another more synthetic definition, proposed by Subramaniam and Youndt (2005: 451), state that IC is “the sum of all knowledge firms utilize for competitive advantage”. In short, as we have previously stated, IC is the sum of all knowledge‐based intangibles. When grouping and classifying the elements which make up IC, there is a certain consensus about the three components or basic dimensions: Human Capital (HC), Structural Capital (SC) and Relational Capital (RC) (Stewart, 1997; Cañibano et al., 1999; Brennan and Cornell, 2000; Roos et al., 2001; Kauffman and Schneider, 2004; Rodríguez‐Castellanos et al., 2006; Alama, 2008; Bueno, 2011; Miles, 2011). IC is also related to the creation of value in the company (Lundqvist, 2000; Ordóñez de Pablos, 2002; Chen et al., 2005; Rodríguez et al., 2006; Tan et al., 2007; F‐Jardón and Martos, 2009; Cheng et al., 2010). Elsetouhi and Elbeltagi (2011) explained how the management of IC has a positive impact on innovation, and therefore, on the creation of value. We are going to focus on the HC and RC dimensions of IC. There are many studies that argue that the main resources for obtaining a sustainable competitive advantage are those related to HC and RC (Marr and Roos, 2005; Chen et al., 2006). HC is the set of productive capabilities that the staff of the firm acquires by accummulating specific or general knowledge (Becker, 1967). Therefore, it is an intangible resource supported by individuals that can be accumulated and used simultaneously in different operations. Fernández et al. (1998) and Marr and Roos (2005) consider that it is the dimension that provides greater value for the company. There are a significant number of empirical studies that have shown the positive relationship between HC and results (Guerrero and Sire, 2001; Bontis and Fitz‐enz, 2002; Cabanelas and Arévalo, 2003; Hermans and Kauranen, 2005; Alama, 2008; Miles, 2011; Rodriguez‐Castellanos et al., 2011). Investment in employee training is considered as the main variable covering investment in HC (Koch and McGrath, 1996; Bukowitz and Petrash, 1997; Ordiz Fuertes, 2002). Danvila del Valle (2005) notes the strategic importance of employee training as a factor to generate HC, which involves obtaining sustainable competitive advantages that lead to better business results. On the other hand, Relational Capital can be defined as “the combination of knowledge that is incorporated in the organization and people, as a consequence of the value derived from the relationships which they

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Lidia Garcia‐Zambrano, Arturo Rodriguez‐Castellanos and Jose Domingo Garcia‐Merino maintain with market agents and with society in general” (Bueno, 2011: 23). We know that nowadays in such a fiercely competitive environment, the key for creating profit and improve performance is to win the loyalty and trust of customers and to build long‐term friendly relationships with them (Huang and Hshueh, 2007). There are some authors that have established a direct link between customer satisfaction, value, and/or loyalty indicators, and measures of actual market or financial performance (Wiley, 1996; Vavra, 1997; Rucci, Kirn, and Quinn, 1998; Charles, 1999, Allen and Wilburn, 2002). According to Chen et al. (2005) and Miles (2011), RC is the dimension that has the higher impact on business results. There are a great many empirical studies that consider investment in advertising as the main variable to represent RC (Corrado et al., 2006; Kohen y Kaimenakis, 2007). However, it happens frequently that investments in intangible resources do not generate immediate returns: an intermediate period is necessary for those investments to have an effect on the firm’s performance. Therefore, the value of this intangible reflects part of the investments made in previous periods. Thus, we can define the “HC value stock in training” as the accumulated investment in training with a depreciation rate and the “RC value stock in advertising” as the accumulated investment in advertising with a depreciation rate. Thus, once we have seen the different empirical studies that relate the dimensions of intellectual capital, and the performance of companies, our hypotheses of the paper put forward are: H1: Investment in employee training positively impacts the value relevance of the intangibles of the companies. H2: Investment in advertising positively impacts the value relevance of the intangibles of the companies. H3: Investment in employee training and in advertising jointly positively impact the value relevance of the intangibles of the companies

3. Methodology Designing the research, population, sample and variables The study focused on Spanish companies quoted on the Spanish Stock Market and specifically those that are quoted on the IBEX‐35. The study considers the impact on the accumulated investment in intangible resources on company’s value. We analyse the effect that the investment in intangible resources related to HC and RC dimensions has on the market value relevance of intangibles: concretely, we chose the sum of accumulated investments in employee training as a main component of HC (Koch and McGrath, 1996; Bukowitz and Petrash, 1997; Ordiz Fuertes, 2002), and the sum of the accumulated investment in advertising (“marketability capital”) as the main variable covering investment in RC (Allen and Wilburn, 2002). The procedure followed to check the hypothesis was divided into three steps: first (model 1), we checked the impact that accumulated investment on training has on the relevance of intangibles on market value. This relevance is measured using Tobin’s Q, calculated as the market value of the company divided by the book value equity, with the latter calculated as the difference between the total assets and liabilities. We then checked (model 2) the impact on Tobin’s Q of accumulated investment in advertising. Finally, (model 3), we analysed the joint effect of the three dimensions on Tobin’s Q. Models 1and 2 are simple linear regressions. Model 3 is a multiple linear regression. The data needed to conduct the study were obtained from the CNMV (Spanish Stock Market Commission) for the stock market value and from the websites of the companies for the spending on training, and from Infoadex website for spending on advertising. The accumulated investments were obtained by means of the spending summary on each original variable (investment in training and investment in advertising) in the three periods prior to moment t, depreciated by a percentage. The linear depreciation rates used are 15%, 20% and

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Lidia Garcia‐Zambrano, Arturo Rodriguez‐Castellanos and Jose Domingo Garcia‐Merino 30%. For the accumulated investments, three annual periods were used as the depreciation rate of 30% did not permit previous effects to be considered. Previous studies considered similar depreciation rates. Estimates by Whittard et al. (2009) and Awano et al. (2010) for training and reputation & branding are 3‐5 years, and for other intangibles, as the combination of R&D and software, 4‐7 years. Previous literature also considered that investment in R&D has a life that ranges between two years (Leonard, 1971; Hirschey and Weygandt, 1985) and seven years (Sougiannis, 1994; Ballester et al., 2003).We likewise took 15% as a depreciation rate, chosen by other authors (Lev et al., 2005). The figures obtained were subjected to a normality test, using χ2 test. Given that the variables were not normal, the logarithm of each of the variables, which did present normality, was used for the analysis. Regarding the size bias, the logarithms of accumulated investments were divided by the logarithm of “Net Amount of the Turnover” to alleviate that problem, as suggested by other previous studies (Lev and Sougiannis, 1999). We considered two periods of time: a period at the beginning of the financial crisis (2006‐2008) and a second one, in deep recession (2008‐2010). Table 1 sets out the technical details of the study. Proposed model 1: Relationship between investment in employee training and the market value relevance of the firm’s intangibles As has been previously put forward, a simple linear regression model was put forward that relates the accumulated investment in HC, through employee training, and the Tobin’s Q for the company. This enabled us to test whether investing in HC really increases the intangibles impXR on the company’s market value. Tobin's Qj,t = f HC Investmentj,t + ε t [1]

(

)

As has been discussed, there is a lag effect of the investments in Human Capital on the value of the intangibles; that is, the value of the intangibles of company j in time period t will not only be influenced by the investments performed in period t−1, but they will also be influenced, although to a lesser extent, by the outgoings in k previous years. As has been already pointed out, an accumulative linear annual depreciation rate (15%, 20% and 30%) was used to consider this effect. Table 1: Technical details of the study Population Sample

Analysis periods Data type Data sources Data analysis technique

Schedule

35 companies belonging to IBEX‐35 31 companies belonging to IBEX‐35 31 companies belonging to IBEX‐35 24 companies belonging to IBEX‐35 2006‐2009, 2008‐2011 Expenditure on training, expenditure on advertisement, stock market value, book value CNMV (Spanish Stock Market Commission), Infoadex website, websites of the companies Model 1 Simple linear regression Model 2 Simple linear regression Model 3 Multiple linear regression May‐June 2012

Model 1 Model 2 Model 3

Proposed model 1: Relationship between investment in employee training and the market value relevance of the firm’s intangibles As has been discussed, a simple linear regression model was put forward that relates the accumulated investment in HC, through employee training, and the Tobin’s Q of the company. This enabled us to test whether investing in HC really increase the intangibles relevance on the company’s market value.

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Lidia Garcia‐Zambrano, Arturo Rodriguez‐Castellanos and Jose Domingo Garcia‐Merino

Tobin's Qj,t = f (HC Investmentj,t ) + ε t [1] As has been pointed out, there is a lag effect of the investments in Human Capital on the value of the intangibles; that is, the value of the intangibles of company j in time period t will not only be influenced by the investments performed in period t−1, but they will also be influenced, although to a lesser extent, by the outgoings in k previous years. As has been said, an accumulative linear annual depreciation rate (15%, 20% and 30%) was used to take this effect into consideration. Given that there is a time lapse between the moment of performing the investment in training and the results obtained, the investment in year t has not been considered but rather the investment in previous years. We likewise considered two time periods, 2006‐2008 and 2008‐2010. In the first period in question, t is the year 2009 and periods 2006, 2007 and 2008 (taking a 3 lag structure) investments, with their relevant depreciations. And in the second period, t is the year 2011 and periods 2008, 2009 and 2010 (taking also a 3 lag structure) investments, with their relevant depreciations too. Therefore, the proposed model 1 would be as follows: Q t* = f STt*-i + ε t [2]

(∑

)

Where: Q*t: Tobin’s Q in t ∑ST*t‐i: Log AITt‐i/LogNTt AITt‐i: Accumulated investment in training in the three prior periods: 3

AITt -i = ∑ STt -i (1 − i × i =1

α ) 100

STt‐i: Annual value of the spending on training during the three years prior to the current year t NTt: Net Turnover of year t α: Applied depreciation percentage (α = 15, 20, 30) εt: Random error in t Therefore, equation [2] includes the lag impact of the expenditure on training of the employees, which thus does not consider the impact of the current investment (period in t) on training on Tobin’s Q in t, but rather the costs or expenditure performed in the three years prior to t. It should also be pointed out that α indicates the linear depreciation percentage (accumulated year after year) of the training costs. Proposed model 2: Relationship between investment in advertising and the market value relevance of firm’s intangibles Likewise we have checked if investment in RC, through the investment in advertising, has a positive impact over the value relevance of intangibles in the company. Tobin's Qj,t = f RC Investmentj,t + ε t [3]

(

)

Therefore, the model 3 would be: Q t* = f SA *t -i + ε t [4]

(∑

)

Where: ∑SA*t‐i: Log AIAt‐i/LogNTt AIAt‐i: Accumulated investment in advertising in the three prior periods: 3

AIA t -i = ∑ SA t -i (1 − i × i =1

α ) 100

SAt‐i: Annual value of the spending on advertisement during the three years prior to the current year t Q*t, NTt, α and εt are as in models 1.

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Lidia Garcia‐Zambrano, Arturo Rodriguez‐Castellanos and Jose Domingo Garcia‐Merino Proposed model 3: Relationship between joint investments in training and advertising and the market value relevance of the firm’s intangibles In order to check the joint effect that the two IC dimensions considered has over the total market value of the companies, we performed a multiple linear regression including investments in training and in advertising. The variables used to measure each one were the same as in models 1 and 2. The procedure followed in this regression is the same that in the previous ones. Equation [4] sets out the proposed model. Q t* = f STt*-i , SA *t -i + ε t [4]

(∑

∑

)

All the variables are as in previous models.

4. Results Table 2 sets out the results of the regressions for models 1 and 2 2 for a depreciation rate of 30% 3 . Therefore, the regression models are as follows: MODEL 1: Period 1: 2006‐2008Æ Qt* = 0.639 + 0.314 STt*-i + ~ εt Period 2: 2008‐2010Æ Q

* t

∑ = 0.637 + 0.384∑ ST

* t -i

MODEL 2: Period 1: 2006‐2008Æ Qt* = 0.833 + 0.357 Period 2: 2008‐2010Æ Qt*

∑ ST = 0.675 + 0.354∑ ST

+ ~ε t

+ ~εt * + ~ε

* t -i

t -i

Table 2: Results of the regressions: Models 1 and 2 30% depr. rate

PERIOD

MODEL 1 Invest. in training

No standard coefficients B St. Error. 0.639 0.110 0.314 0.103

2006‐2009 MODEL 2 Invest. in advertis. MODEL 1 Invest. in training MODEL 2 Invest. in advertis.

2008‐2011

0.833 0.357 0.637 0.384 0.675 0.354

0.066 0.103 0.168 0.163 0.165 0.166

Typified Coefficients Beta 0.512 0.622 0.496 0.399

Sig.

5.789 3.040

0.000 0.005

12.646 3.460 3.785 2.357 4.083 2.130

0.000 0.003 0.001 0.031 0.000 0.044

Total Sig. Corr. R2

0.007

0.234

0.044

0.124

0.026

0.202

0.003

0.354

Turning to the table, we can see how in the two periods considered, the models are statistically significant at 5%, using the depreciation rate of 30%, according to the regression, in all the cases. The coefficients of the exogenous variables are positive and, at the same time, statistically significant at 5%. This means that both investing in training as investing in advertising have a positive impact over the value relevance of a firm’s intangibles. 2 We further consider the corrected R determination coefficient that shows the goodness of the adjustment, according to the depreciation rate applied. In all the cases for each period considered, the value is between 0.124 and 0.35, being statistically significant 4 . 2

We have checked the relationship between Structural Capital dimension, studied through the accumulated technology investments, and Tobin’s Q, but there is no link between both variables. There are available on demand. That is why we have decided to exclude the Intellectual Capital dimension to analyze its impact on the business value. 3 We have checked the regressions likewise using the depreciation rates of 15%, and 20%. The best results are obtained with the depreciation rate of 30%. We therefore only have shown the results using this last rate.

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Lidia Garcia‐Zambrano, Arturo Rodriguez‐Castellanos and Jose Domingo Garcia‐Merino These results are similar to other studies (Novales, 2008), which a priori means that the goodness of fit test is a little weak in some cases. However, if we look at other studies Novales (2008), this result does not mean that the model is not valid or reliable, but rather that it may be due to the sample being too small or that one or two residuals are too high and cause a relatively low value of the coefficient of determination. There could likewise be other variables that have the capacity to explain the endogenous variable. Therefore, we have deemed it convenient to study the correlations between the investments in each dimension during the three years in question and the intangible value of the companies in year t. Tables 3 shows the aforementioned correlations. Table 3: Correlations between investment into training and advertisement in each year and Tobin’s Q in year t (first period) t = 2009 Tobin’s Q 09

Pearson Correl. Sig. Sig.

Tobin’s Q 09

Pearson Correl. Sig.

Investment in training 2006 0.400 0.027 0.001 Investment in advertis. 2006 0.707 0.000

Investment in training 2007 0.400 0.031 0.030 Investment in advertis. 2007 0.714 0.000

Investment in training 2008 0.500 0.007 0.064 Investment in advertis. 2008 0.671 0.000

Investment in training 2009 0.373 0.042 Investment in advertis. 2009 0.471 0.007

Investment in training 2010 0.372 0.043 Investment in advertis. 2010 0.453 0.009

t = 2011 Tobin’s Q 11

Pearson Correl. Sig.

Tobin’s Q 11

Pearson Correl. Sig.

Investment in training 2008 0.373 0.042 Investment in advertis. 2008 0.488 0.005

As can be seen, the correlations between the “investment on training” variable and Tobin’s Q are positive, which means that there is a relationship between the investment in training and the intangible value of the company, a relationship that is positive and strong and, according to the model studied, is statistically significant at 5%. Furthermore, the correlations between the variable “investment in advertising” and Tobin’s Q are positive, which means that there is a relationship between the investment in advertising and the value of the intangibles, a relation that is also positive and statistically significant at 5%. Regarding the third model (model 3), we can see the results in table 4. Given that the behaviour of the models 5 is better in the two previous models using the highest depreciation rate, we have taken this last rate (30%) into account . Turning to the table, we see the poor significance of the model for each period considered. The model is statistically significant at 10% for the first period considered. The beta of the investment in employee training variable is positive high value and also statistically significant at 5%. However the beta of the investment in advertising variable is not statistically significant at 5%. We can therefore see the highest importance of human capital dimension to increase the market value of the company. Turning to the second period, although the model is not statistically significant at 5%, we can see that the investment in human capital, although the accumulated investment in employee training variable has the highest Beta value, with this result not being statistically significant at 5%. That result may be due to the deep crisis that means that the market value does not capture the real value of intangibles. Given the small sample in this last model, we can argue that the 2 corrected R value is too small. 4

The value of the R increases when we increase α in the first period that means the recent investments on each dimension of Intellectual Capital strongly affects the firm’s total value. 5 We have used the other two depreciation rates considered, but we have not included the results in the paper as they are very similar to ones that presented here. The full results are available on request.

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Lidia Garcia‐Zambrano, Arturo Rodriguez‐Castellanos and Jose Domingo Garcia‐Merino Table 4: Results of the regressions: Model 3 Period

2006‐ 2009 2008‐ 2011

Constant Invest. in training Invest. in advert. Constant Invest. in training Invest. in advert.

No standard coefficients B St. Error 0.649 0.187 0.287 0.121 0.023 0.159 0.784 0.232 0.114 0.101 0.189 0.210

Typifies Coefficients Beta

Sig.

Total Sig.

Corr.R 2

0.473 0.029 0.302 0.240

3.473 2.380 0.146 3.374 1.132 0.898

0.002 0.027 0.885 0.006 0.280 0.387

0.075

0.151

0.388

0.004

We have deemed it convenient to perform a Collinearity Diagnostic to establish whether a multi collinearity exists between the two exogenous variables used in the model. Therefore, turning to table 5, we can see that the indexes of condition don’t get up the maximum of 30. From that, we can deduce there is no co linearity between the variables. Table 5: Collinearity Diagnostic A PERIOD

2006‐2008

2008‐2011

Dimension

Autovalues

Index of condition

Proportions of the variation Constant

ITe

2.980

1.00

0.00

2 3

0.014 0.006

14.673 22.903

0.02 0.98

0.81 0.19

0.32 0.67

2.921

1.00

0.00

0.01

0.00

2 3

0.075 0.004

6.261 26.059

0.02 0.98

0.96 0.03

0.02 0.98

Given the results, we can accept H1 and H2, but, in terms of H3, we can accept it only partially. The hypothesis is accepted for the first period, , but not for the second.

5. Conclusions The importance attributed to the intangible resources is increasing in interest, which encourages researchers to turn to knowledge and its management. This paper seeks to establish the importance that investment in IC dimensions has, and more specifically spending on training and advertising and its impact on the corporate intangible value, in the Spanish case. The results show on the one hand, that there is a positive relationship, and at the same time statistically significant, between the investment in employee training and the market value relevance of the intangibles of the companies; on the other hand, there is also a positive relationship, and at the same time statistically significant, between the investment in advertising, and the market value relevance of the intangibles of the companies. Regarding the joint effect of investment in employee training and investment in advertising, there is a positive relationship, and at the same time statistically significant, only in the first period considered (2006‐ 2009). Therefore, the results show that relationship exists in all the cases studied, using depreciation of 15%, 20 and 30%, except for the join effect in the second period (2008‐2011). This means, on the one hand, that investing in Human Capital, and more specifically, in training the employees, increases the value of the company; on the other hand, that investing in Relational Capital, and more specifically, in advertising, increases the value of the company; and finally, that investment in both variables increases the value of the company, although the deepening of the crisis, in the Spanish case, may have diluted this last effect. Apart from that, we can deduce from the results that the human capital dimension could be considered the most important dimension of IC to increase the value of the company. The third model (for the first period) suggests that the investment in employee training variable, as the main one representing the HC dimension, is the variable that has the highest beta value, and it is likewise statistically significant at 5%. We can therefore conclude that investment in the HC dimension has a higher impact on the market value of the company, and therefore, on the overall value of the company.

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Lidia Garcia‐Zambrano, Arturo Rodriguez‐Castellanos and Jose Domingo Garcia‐Merino Furthermore, this influence is higher in the case of recent employee expenses; that means the impact in training investment on the intangible value of organizations seems to run out quickly. That means investors have given higher value to efforts to build Human Capital. Possible lines of future research are, on the one hand, to expand the selected sample and include European companies quoted on the stock market, and on the other hand, to further research the effect of the diverse variables related to Structural Capital on the market value relevance of the company’s intangibles.

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Intellectual Capital: An Accounting Change Perspective Marco Giuliani Department of Management, Università Politecnica delle Marche, Ancona, Italy m.giuliani@univpm.it Abstract: There is, both in literature and in practice, an increasing attention on Intellectual Capital (IC) as it is generally recognized that its performance contributes to improve the firm overall performance (Dumay, 2012). From the originates a plethora of IC measurement methods and tools have been proposed. Although there is a strong belief in the possibilities of using measurements for managing organizations (Catasús, et al., 2007), both academics and practitioners have questioned IC measurements potential in “practice” (Dumay, 2009; Guthrie, et al., 2012). Stemming from this and considering the call for case studies on Intellectual Capital “in practice” (Dumay, 2009; Guthrie, et al., 2012), the purpose of this study is to investigate IC from an accounting change perspective. In order to achieve this aim, three research questions will lead this analysis: 1) To what extent is it possible to talk about IC as an accounting change? 2) Which logic of change can the implementation of an IC accounting system (ICAS) follow? 3) Which management approach can the implementation of an ICAS follow? This study is based on a field study developed adopting an action research approach. These are the main results. First, IC can lead to a technical and to a substantial change. While new measurements always bring technical innovations, after the introduction of an ICAS pre‐existing measurements can assume different meaning. Second, IC accounting change seems to be linear from a technical perspective but the use of the ICAS can have unexpected managerial effects. Third, when IC is introduced from an accounting perspective, the focus is on the measurement of IC. Nevertheless, this situation can change and the focus can be moved to another perspective such as the managerial one. Keywords: intellectual capital, intangibles, accounting change, measurement

1. Introduction Both in literature and in practice there is an increasing attention on Intellectual Capital (IC) as it is generally recognized that it can contribute to improve the firm overall performance (Dumay, 2012). According to the adage “you can manage what you can measure”, a plethora of new measurement methods and tools specifically designed for IC have been proposed in order to support the managerial decision process (Andriessen, 2004; Giuliani and Marasca, 2011; Sveiby, 2004). Unfortunatly, most of these Intellectual Capital Accounting Systems (ICAS) are not tested or applied in practice,. This situation has led to a call for case studies on IC “in practice” (Dumay, in press; Guthrie, et al., 2012). The implementation of an ICAS can be seen as a management accounting and organizational change as they shed light on areas or resources previously escaping the accounting eye and they can influence operational practices, professional routines and way of thinking (Vaivio, 2004). Adopting this perspective, it is possible to investigate three dimension of the change: the epistemological nature, the logic and the management of change (Burns and Vaivio, 2001; Dent, 1991; Ittner and Larcker, 1998; Vaivio, 2004). Stemming from this, the purpose of this study is to investigate IC from an accounting change perspective. In order to achieve this aim, three research questions will lead this analysis: 1) To what extent is it possible to talk about IC as an accounting change? 2) Which logic of change can the implementation of an ICAS follow? 3) Which management approach can the implementation of an ICAS follow? This research is based on a field study developed adopting an action research approach. The structure of the study is the following. The next session proposes a brief review of the literature on the basic elements of the study. Then, the empirical part will be illustrated in terms of methodology and data collected. In the central part, an attempt will be made to make sense out of the findings and to develop the theoretical arguments of the study. Finally, some valuable insights are extracted and systematized to draw some conclusions and to outline a research agenda.

2. Intellectual capital as an accounting change In over 20 years of debate, most of the IC discourse has focused on designing new IC accounting systems while there are only a limited amount of studies dedicated to understand what IC does within the organisation (Chiucchi, 2008; Giuliani, in press; Mouritsen, 2006). Within this stream, an aspect that can be investigated is to what extent implementing an ICAS in an organization can lead to an accounting change. The content of

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Marco Giuliani accounting change is an area of research that has been largely unexplored (Quattrone and Hopper, 2001). Three perspectives on management accounting change will be used as the starting point for the following exploration: the epistemological nature, the logic and the management of change. Regarding the epistemological nature of change, it aims to identify whether there has been a change or not, i.e. if the change is a real change or just a mere re‐articulation of the previous situation. Within the real changes, it is possible to distinguish between technical changes and substantial changes. Technical changes are regarded as changes in the management accounting systems in terms of magnitude and multitude of measurements (Ittner and Larcker, 1998), how the system is designed and how the system is operated and used (Sulaiman and Mitchell, 2005). Substantial changes are related to the influence of management accounting change on operational practices, routines, ways of thinking and cultural changes (Dent, 1991; Vaivio, 2004). In the IC discourse, the idea is that the introduction of IC concepts, methods and tools leads to technical and substantial changes, i.e. to a complete change of the accounting system, as IC requires ad hoc measurements and managerial practices. Therefore, according to the mainstream literature, there is no room for previously existing measurements within an ICAS. The second perspective regards the logic of change. An accounting change is linear if it follows a sequential process consisting of a set of phases that are orderly structured and rather unproblematic (Burns and Vaivio, 2001; Kaplan and Norton, 1996; Malina and Selto, 2004). Other authors have highlighted that an accounting change can be a chaotic, unpredictable and controversial process (Lowe, 2004; Vaivio, 2004). From this point of view, the few cases on IC commented in literature tends to present an intermediate situation between the linear one and the chaotic one. In particular, even if most of the studies on IC implementation processes tend to present a linear process consisting in three main steps (visualising, measuring, and reporting/understanding) (Giuliani, 2009), some of them highlight the challenges that have to be faced in order to make the IC concept understandable and to grasp IC “in action” and also some unexpected effects of implementing an ICAS (Chiucchi, 2008; Cuganesan, 2005; Cuganesan and Dumay, 2009; Giuliani, in press; Giuliani and Marasca, 2011). The last perspective on management accounting change is related to the management of change, i.e. if it is a top‐down driven effort or if it can be promoted by key actors from decentralized organisational units (bottom‐ up process) (Burns and Vaivio, 2001). In the IC discourse, the role played by the actors who “champion” IC and “speak on its behalf” by guiding the design and implementation of its measurement tends to be overlooked or underestimated even if it seems to be relevant. In fact, according to Chaminade and Roberts (2003, p. 746) the actors who introduce IC determine the implementation trajectories of the IC project and also play “a significant role as driving forces during the early stages in measurement routine development” (Johanson, et al., 2001, p. 418). This situation is described as “lock‐in” phenomenon in order to underline that the point of entrance of an IC project, in companies that do not have previous experience with IC, has a strong influence on the focus of the project (Chaminade and Roberts, 2003). With this respect, some authors have highlighted that the ambiguity of IC can help preventing a ‘lock‐in’ phenomenon and thus allows managers to apply IC to their problems (Chiucchi and Dumay, 2012; Cuganesan, et al., 2007).

3. Design of the study The case study method was chosen as an appropriate means of exploring the research question since it allows to collect “rich data” and answer to if and how questions (Yin, 2003). Such an approach has the potential advantage to find elements, dimensions and events that are important for accounting for time that have not been adequately considered into the accounting models, in general, and into the IC models, in particular. Thus, this approach allows achieving a potential discovery of new conditions and interactions that are important for understanding the construction of time within organizations. In particular, this paper examines the Ankon case study which was undertaken using an action research methodology which is based on a collaborative process between the researcher and client of critical inquiry into problems of social practice in a learning context (Argyris, et al., 1985, p. 237; Coghlan and Brannick, 2001). More precisely, as it will be described in the following paragraph, a modest interventionist approach have been adopted (Jönsson and Lukka, 2005). This research methodology has been chosen to achieve the purpose of this paper because it allows to develop scientific research and innovative practical solutions (Kaplan, 1998). Moreover, the possibility offered to the researchers to collaborate to the project offering his opinion and

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Marco Giuliani scientific support made it possible to achieve a better knowledge of the context, of the variable and of the process under analysis and to have access to information usually available just for insiders (Labro and Tuomela, 2003; Middel, et al., 2006). Finally, an interventionist method has been chosen because, in accounting studies, in general, and in the IC literature, in particular, there is a strong call for case studies developed with this methodology in order to test and observe in practice concepts, methods and tools (Dumay, in press; Guthrie, et al., 2012). Ankon was deemed as appropriate to achieve the aim of this study for the following main reasons. First, the management of Ankon is focused on IC: it strongly believes that their company can compete and survive on the market only creating a value added products which is possible only making lever on the people and on the organization. Second, Ankon adopted a “new” measurement system in order to manage IC, i.e. it occurred in a management accounting change. Third, Ankon was approached as a longitudinal case study, which allows capturing and understanding how a phenomenon changes over time. Finally, the company allowed the researchers to take part to the meetings where the valuation was discussed and consequently it was the opportunity to understand a valuation process “in vivo” instead, as it usually happens, “in vitro”. Data were collected from multiple sources such as annual reports, stakeholder impact reports, internal strategy reports, and semi‐structured interviews. More in depth, the largest amount of information had been collected taking part to the focus group, promoted by the CEO and formed by the researchers, the CFO, the area managers, the purchase manager, the R&D manager, the production manager and the CEO itself. The purpose of the focus group was to design and implement a new intangible oriented accounting system. The focus group meetings were based on a semi‐structured agenda proposed by the researchers and initially discussed with the CEO and the CFO, modified and then put into action. As planned, five meetings of about four hours each were carried out together with some meetings with the CEO and the CFO and some interviews with single members of the focus group and with some employees. Based on the specific requests of the focus group, the researchers supported Ankon’s management in coordinating and supporting the discussions useful to design and implement the system. More details regarding the activities carried out by the researcher in each stage of the valuation process are illustrated hereafter.

4. The Ankon case study The case study under investigation was developed between 2007 and 2011. Alpha is an Italian firm (turnover Euro 23mln; 98 employees) and an important European player in die‐casting processes of zinc and aluminium alloys and related activities for the production of gas burners and components for the automobile and household appliance industries. The company is composed by three business units: the first is dedicated to the contract co‐design and production of household appliances for other companies; the second realizes own designed and branded burners; the third produces automotive and technical parts (e.g. parts for gear boxes, car heaters, etc.). The management accounting system of Ankon was mainly based on financial indicators; non‐financial indicators were used only for limited and specific aspects such as the control of the production process and the analysis of the customer satisfaction. In all, IC was not monitored, except for specific dimensions (e.g. customer satisfaction or turnover of the employees), and the focus was on financial measures; thus, the introduction of an ICAS represented a “big step” as it implied the introduction both of a new accounting object and of non‐financial measures. In Ankon the IC project was promoted by the CEO and the CFO in order to: … have a picture after 20 years of history and have a measurement of the value achieved by the company thanks to our people, our customer, our organisational model and our business idea… (the CEO). The idea was to measure IC mainly to support the managerial decision process but also to have “numbers” to show to some customers and banks in order to highlight the differences between them and other players. After having defined the aim of the project, the first step was to achieve a consensus on the meaning of the term “IC”. According to the aim of the research project, the researcher proposed to define IC as the system of intangibles that have strategic relevance (Meritum, 2002). This definition was then discussed and adopted by

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Marco Giuliani the project group also because it allows developing a selective accounting system able to focus only on the most relevant intangibles. To support the managerial activity and according to the Meritum Guidelines (2002), the researcher proposed to focus both on IC resources and on IC activities. This proposal was discussed and then accepted by the project group because it was considered appropriate to monitor both the efforts made (activities) and the results achieved (performance) in order to obtaine the desired “picture” of the IC of the company. Moving from the strategic targets of the company, the resources were mapped adopting a cause‐and‐effect approach based on the perceptions of the members of the focus group. By way of example, to achieve the desired level of technical know‐how they needed to have qualified and stable human resources supported by an up‐to‐date information system and specific technologies, databases and processes, i.e. specific IC resources, tangible and financial resources. Afterwards, the focus group identified the activities useful to create and develop the mapped resources. Once the IC resources and activities were identified, to facilitate the identification and visualization, the Meritum tri‐part model was adopted. The results of this stage are shown in Table 1. Table 1: Ankon IC resources and activities

Activities

Resources

Human Capital - Design competences - Die-casting competences - Production competences - Loyalty - Quality of the workplace relationships -… - Training activities - Coaching activities - Retention activities - Team-building activities -…

Structural Capital - Procedures - Manuals - Database - Strategic Software -…

- Renewal activities - Maintenance activities -…

Relational Capital - Relationships with customers - Relationships with suppliers - Relationships with the institutions - Brands -… - Marketing activities - Activities with with suppliers - Activities with the institutions -…

After the identification of IC, the research proposed a panel of measurements able to monitor the IC resources and activities. The underlying idea was that the measurements of the activities are useful to understand the trend recorded by the stocks. According to the requests of the focus group, the measurements were identified considering the already existing data in the company and the information needs that emerged during the discussions and that the researcher tried to “operationalize” by designing new indicators. The researcher proposed an initial draft of the panel of measurements to the project group, which was then discussed: some measurements were immediately accepted, other modified and then re‐discussed while others deleted as considered to be too difficult to calculate or not particularly relevant. Hereafter some examples are reported. In defining the measurements, two main different situations need to be pointed out. The first situation is the one of measurements that were already existing (e.g. costs and time of training activities, marketing investments, etc.) but controlled and owned only by a specific area of the company (e.g. HR, marketing and sales, etc.) or considered to be “stand alone” measurements, i.e. not related to others (e.g. customer satisfaction). We have always measured the hours of training but only for human resource management purposes… It’s the human resource manager that calculates and uses them. Nobody else is involved… (the CFO). We have a specific internal report about customer satisfaction… the data that are reported there are not indicated in other reports and this happens also for other information. Then it is difficult to control everything… (BU1 marketing manager). We already have detailed measures about some activities (e.g. training, marketing, etc.) and we are fine with them… rather than inventing something new why don’t we use them? (BU2 marketing manager)

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Activities

Resources

Human Capital - Level of design competences - Level of die-casting competences - Level of production competences - Level of managerial competences - Labor costs for design, diecasting and production processes - Number of people with critical competences - Turnover index - Quality of the workplace index - Etc. - Costs and time of training activities - Costs and time of coaching activities - Costs and time of recruitment activities - Costs and time invested to guarantee horizontal mobility - Etc.

Structural Capital - No. of quality/environment procedures - Rating of the procedures (usefulness, size, update rate, timeliness of updates, etc.) - No. of databases for each type (design, production, marketing, etc.) - Size of databases - Rating of the databases (usefulness, size, update rate, timeliness of updates, etc.) - Investments in strategic/non strategic software - Etc. - Index of renewal of the procedures/databases - Investments in procedures/databases - IT investments in strategic software - Etc.

Relational Capital - No. of relevant & loyal/other customers - Customer Loyalty index - Customer profitability for each category - No. of strategic supplier in Europe, South America, Asia, USA. - Cost-savings generated by strategic suppliers - Supplier Loyalty - Etc.

- Marketing investments - Investments to develop existing/new customers for each category - No. of sustained audits - No. and investments in fairs - No. of visits to suppliers - No. and value of sponsorships - Brand investments - No. of improvement plans - Etc.

Table 2: Ankon IC indicators The second situation is related to the “new” indicators adopted. In this case, whether some aspects were considered to be relevant but not monitored, the focus group tried to define specific indicators to implement. Here the focus group had to balance the willing to design “the best indicator” with the potentiality of the information system and the capabilities of the organization to run the measurement over time. For example, in order to measure the level of the competences the best method identified was to carry out structured meetings between managers and employees and assessment sessions. Nevertheless, on second thought, some of the managers considered these method too complicated to be applied as they did not have “the competences to judge” or “a place where recording the judgements”. Therefore, a second best solution was adopted. The IC reporting has also highlighted some lacks in the management tools of the company and thus the system has contributed to develop an organisational change. We have noticed that we lack some information about competences of our personnel, the quality of the workplace and the customer satisfaction… Till now we used proxies or simple methods to measure them but they are not satisfactory anymore in a systemic perspective… (the CFO). Summarising, the introduction of an ICAS has given a different meaning to the existing indicators, has introduced new routines and practices useful to calculate the new measurements and has highlighted some information gaps. After the introduction some follow‐up interviews have been carried out. These were useful to gain some insights about the use of the ICAS. First it emerged that the IC report initially made “to have a picture”, i.e. focusing on the stock of IC, after it was used to reflect on the connections between activities and IC performance, i.e. focusing on IC flows. In other words, the system was used for controlling the processes of the company. In spite of this, even after 2 years from the introduction, the system was not considered “mature” enough for supporting the planning activities and it was still used to have a measure of IC ex post. Second, some of the IC measurements moved into the daily‐used accounting systems used by top and middle managers, such as the indicators implemented to monitor the customers, while others remained locked within the IC report, owned by the top management. Third, the introduction and use of an ICAS can actually lead to a different managerial style as it shed a light on organizational dimensions that tend to be overlooked. For example, during one of the meetings the CEO said regarding the IC report:

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Marco Giuliani It’s always interesting to discuss about these things, to have an overview on different aspects of the company. Usually we don’t talk about them in these terms… (the CEO). This suggests that implementing an IC report allowed connecting and discussing in a more structured and systematic way different aspects of the organization. In other words, the IC report was considered to be a platform for discussing about aspects that are generally overlooked and it allowed to look at indicators from a different perspective and to mobilize the firm IC.

5. Discussion and conclusions The aim of this study was to investigate the introduction of IC as a management accounting change. A longitudinal field study was developed adopting an action research approach. The starting point of the analysis was the distinction between the epistemological nature of change, the logic of change and the management of change (Burns and Vaivio, 2001). The first point here examined is the accounting change from an epistemological perspective. When the IC measurement project was initiated, the managers were already using an array of measurements in order to monitor some of the IC resources and activities (e.g. hours of training, turnover of the personnel, marketing costs, etc.) but these measurements were often approached as “stand alone”, i.e. not related to other indicators, or used only by a specific manager (e.g. the human resource manager or the area manager). Therefore the company was not able to have “a complete picture” of its IC and wanted to implement an IC accounting system. Analysing the development process it emerges that some of the adopted indicators have been created ad hoc (e.g. the indicator related to the level of the competences) while others have been “recycled” from the previously adopted system. Therefore the degree of accounting change can be questioned with specific reference to the recycled measurements. Regarding the pre‐existing indicators, Ankon was already using suitable measurements related to what were after defined as “IC resources and activities”. Thus, as the manager reasoned, why do not make use of them? This evidence is consistent with the previous studies regarding the importance of existing measures (Vaivio, 1999; Wouters and Sportel, 2005). Another aspect that emerges is that the implementation of an IC accounting system has changed the meaning assigned to them. Including existing measurements in an IC report allows to read them in a more systematic way and giving emphasis to the connections between measurements and, thus, to the value creation process (Cuganesan, 2005; Cuganesan and Dumay, 2009; Kaplan and Norton, 2003; Mouritsen and Larsen, 2005). In all, pre‐existing measurements, once they are included into an IC report, are observed from a different perspective. For example, the number of mistakes and waste in the production is not only a production performance indicator but becomes an indicator of the competences and experience of the people involved in the production. This means that the introduction of an IC accounting system can represent a technical change for the part related to new indicators, methods and tools. More relevant is the fact that the implementation of an IC report seems to bring a substantial change even without being accompanied by any major technical change. Concluding, reporting and discussing IC gave the opportunity to focus also on the interrelations among them and to have a more complete view of the indicators. In other words, measuring IC gave the opportunity not only to develop new indicators but also to give different meaning and relevance to indicators and data already existing in the company, i.e. IC can be seen as a platform useful to integrate different measurements of the organization and to discuss about them adopting a holistic perspective. The second point here examined is the logic of change. In the project, probably due to the intervention of the researcher, the change was quite linear and followed the traditional phases of visualising, measuring and reporting/understanding. Nevertheless, some unexpected changes happened. In particular, the way of discussing data changed as they were approached in a more systemic way, as aforementioned. Moreover, the IC report, even if designed for managerial purposes, was considered so interesting that it was presented to some partners and afterwards used also to select the right one to establish a joint‐venture (“…this is what we can offer… what can you give to us?” as the CEO said). Thus, the IC technical change seems to develop in a linear way while the substantial change and the use of the ICAS can present unexpected effects. This results contributes to the debate about the use of IC measurements and its ambiguity: an IC project, even if developed with a specific purpose, can have unplanned managerial effects.

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Marco Giuliani The last aspect here examined is the management of change. In Ankon, the change was promoted and managed by the CEO and the CFO. Their strong “managing by numbers” approach has led to the accounting “lock‐in” phenomenon, i.e. the IC project was mainly focused on measuring. This result confirms the one of Chaminade and Roberts (2003, p. 746) that if an IC project entries from an accounting perspective, the stress on it is not on its management dimension but on the measurement. Nevertheless, at the end of the project, there was the idea to use IC more from a managerial perspective. In all, even if the “lock‐in” phenomenon occurs it seems that it is also possible to “un‐lock” IC. The limitations of this study are related to the adopted action research methodology (Middel, et al., 2006) and to the size of the investigated case study (Shen and Reuer, 2005).The findings provided by this research should be useful to those interested in studying IC in action and accounting change. In fact, the study shows that implementing IC does not always means doing something completely new but can also lead to look at “old” measurements from a “new” perspective. Moreover it offers some insights about an IC accounting system implementation process. Future research opportunities can be found in more empirical studies about the accounting and organizational changes due to the introduction of the IC concepts, methods and tools.

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The Five Cs of Intellectual Capital: Two Additional Dimensions of Assessment Uma Gupta1 and Joseph Azzopardi 2 Business Department, State University of New York, Buffalo, New York, USA 2 Department of Management, Faculty of Economics, Management, and Accountancy, University of Malta, Malta

exec@usasiaedu.com joseph.g.azzopardi@um.edu.mt Abstract: Ulrich defines intellectual capital as Competence times Commitment. The model was later extended to include Control as a key ingredient to measure and leverage intellectual capital. This research expands on this model to include two additional dimensions, creativity and culture. It explores the relationship between intellectual capital and these five critical variables: Competence, Commitment, Control, Creativity, and Culture. These variables, and the inter‐relationships between them, can help firms to define and describe their intellectual capital assets. By systematically measuring these five factors, service organizations can identify their strengths and weaknesses in capitalizing their intellectual prowess and resources. In addition, looking at intellectual capital through this lens can help organizations determine where they should invest in their human capital. It can form the basis of developing training programs for employees and lay the foundation for becoming a true learning organization. The purpose of this paper is to expound on the two additional dimensions and outline the benefits of assessing IC within the five dimensions as a means to gain a deeper understanding of the competitive positioning of an organization within its industry. This paper is organized as follows: identify the common theme that emerges from the many definitions of IC, explain the multiple challenges in assessing organization IC. Keywords: intellectual capital, IC framework, expanding IC framework, assessing IC

1. Introduction It is a myth that intellectual capital (IC) is a modern day concept. The idea that the individual and collective knowledge repository of an organization is the driving force behind its ability to survive, grow, and sustain its competitive advantage in the global marketplace has a rich and long history as early as the 1930s. In fact, it has a long and rich history that dates back to more than a century. The Romans, Greeks and Egyptians valued knowledge as treasures and history shows the extraordinary and creative efforts that many empires undertook to create, disseminate, and protect knowledge for future generations. Further, the concept that not all resources are tangible is also ancient and widely accepted (Pike 2005.) (Pike, Fernstrom, Roos, 2005). While this is almost universally true for many industries, the capability and capacity of service industries, in particular, to leverage their intellectual capital is even more critical for their survival and growth (Lim and Dalimore, 2004). Without the knowledge that drives the ability to provide value‐added service there is no “product.” Hence the individual and collective brainpower of employees in service industries combined with the ability of individual firms to create, collect, coordinate, retrieve, leverage, and collaborate the brainpower of its employees is key for survival. Intellectual capital is made up of tangible and intangible resources. Intangible resources are an inherent part of a resource‐based view of organizations (Herremans and Issac, 2004). Although some elements of intellectual capital are tangible (physical or monetary) and can be measured and valued according to widely understood and accepted accounting principles, many elements of IC are intangible and are subjective, ambiguous, difficult to measure, identify and value (Marr, 2002). This paper focuses on the intangible assets of intellectual capital. Although there are many definitions of intellectual capital in the literature (Manzari et al), there is no one unambiguous or universally accepted definition (Burr). There are both prominent and subtle variations in how researchers and practitioners view IC. Arriving at a universal definition is further complicated by the notion that the terms used to define and describe intellectual capital varies among industries and job functions, and even within cultural, social, political contexts of societies and nations (Marr). Regardless of the nuances in definitions, ideas related to intellectual capital converge on the notion that an organization’s brainpower is singularly one of its most critical, intangible, invaluable resources, one that offers a direct pathway to strengthen and enhance its market power.

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2. Intellectual capital worth of firms Assessing the IC value of firms has many implications for organizations and the industries within which they operate. However, researchers encounter several challenges and complexities in developing and advancing a set of structured methodologies, metrics, and frameworks for the intangible elements inherent in intellectual capital of firms.

Definition issues: Lack of a universal definition means that the linguistic use of the term may mean different things to different stakeholders. Different stakeholders view the IC of a company differently. Starting with managers of different functional areas, senior management, CEOs, Boards, and stock holders may view intangible assets differently.

Macro and micro forces: Intellectual capital is influenced and shaped by a set of complex and ever‐ expanding micro and macro forces. While many researchers hold “constant” the macro forces while studying the IC issues and challenges of a firm, this can skew the analysis of the IC value of a firm. Hence context becomes a key factor in the research and development of intellectual capital (Pike and Fernstorm).

Functional Differences: Intellectual capital of a company is shaped not only by the characteristics of the company and the industry in which it operates, but also by the function or functions within the firm that is under study. For example, intellectual capital assessments of the accounting and financial functions of a firm (Mauritsen and Rosinder) may be different from that of patents and intellectual copyrights of the knowledge transfer department which in turn, may be very different from that of marketing or production. The subtle nuances of each discipline and the functional areas within a firm, therefore, creates an intricate “link” of sub‐domains of intellectual capital which must then be integrated in a cohesive and useful way for it to be beneficial to the company. Further, the granularity of knowledge within each function can also problematic to capture and assess (Pike).

Comparative Analysis: The assessment of the worth of the intellectual capital that resides in a company is relative. In other words, intellectual capital cannot be viewed in isolation. Instead, the true potential of a company’s IC is related to the IC assets of competitors, both current and future. A knowledge‐based theory of the firm requires knowledge to be “defined precisely enough to let us see which firm has the more significant knowledge and explain how that leads to competitive advantage” (Spender 1996). Hence any framework of IC should include at the very least the fundamentals of an objective and comprehensive comparative analysis structure. This includes key characteristics of the company and the industry in which it operates along with the macro forces that shape the structure and competitive nature of its industry. Company and industry characteristics and the interplay between them influence the competitive positioning of an organization, but assessing the impact of this complex set of dynamic variables is a challenge.

Interplay and use of IC: Intellectual capital as the collective knowledge resources of an organization must also take into account the permutations and combinations of intellectual resources within the organization. Hence IC is not just an inventory of intangible resources, but the interplay of each resource with other resources. In addition, how each resource is used in different contexts is also influential and impactful. The permutations and combinations of different resources in and of itself demands a certain level of intellectual capital in order to achieve sustainable competitive advantage. Thus intellectual capital is about the availability, interplay, engagement, and permutations and combinations of organizational resources in a dynamic and ever‐changing world.

3. Expanding the framework of IC A core issue of the role and purpose of IC is to help firms strengthen the link between their IC and market value. (Stewart 1997) However, leveraging IC to achieve market worth requires a comprehensive framework that takes into account the complexities and intricacies of micro and macro forces that define and shape the firm’s intellectual capital as discussed in the previous section. Ulrich defines intellectual capital as Competence times Commitment (Ulrich 1995) This model was later extended to include Control as a key ingredient to measure and leverage intellectual capital (Burr 2005) This paper expands on this model to include two additional dimensions, Creativity and Culture, as essential components in defining and measuring a firm’s IC. We explore the relationship between intellectual capital and these five critical variables: Competence, Commitment, Control, Creativity, and Culture to develop a framework that can help companies assess, build and leverage their IC.

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Uma Gupta and Joseph Azzopardi The advantages of this framework are that it is comprehensive and takes into account different dimensions that impact the ability of an organization to create and leverage its intellectual capital. By assessing its competence, commitment, control, creativity, and culture, a firm can use this knowledge and information to assess its competitive positioning in the industry in which it operates along with the weak links in the organization. In addition, looking at intellectual capital through this five‐dimensional lens can also assist organizations to focus on areas where they should invest in order to build their human capital. At the very least, it can form the basis of developing training programs for employees and can lay the foundation for becoming a true learning organization. It is important to recognize that since IC lacks a universal definition, any framework that builds on a convergence of key ideas and concepts is likely to be subjective. As our knowledge and understanding of intellectual capital expands, it will be meaningful and advantageous to expand the elements of existing frameworks and further strengthen them to effectively capture the complexities and uniqueness of different businesses. In the next section, we briefly look at the two components (capacity and competence) in Ulrich’s model and the third component (control) that was added to the model. We then elaborate on the two newly added elements, namely creativity and culture.

3.1 Capacity The words capacity and competence are sometimes used interchangeably in the intellectual capital literature (Burr). Competence or capacity refers to structured and unstructured information, knowledge, skills, abilities, pattern recognition, problem‐solving, critical thinking and other knowledge elements. It is not only the availability and possession of these individual knowledge elements that constitutes capacity, but also the ability to retrieve the “suitable” elements from one’s knowledge repository and apply them effectively to the problem or situation at hand. Capacity is often learned through a combination of education, professional experiences, and continuous learning. However, Burr et. Al propose that “the concept of competence must extend beyond capacity to include more dynamic elements such as skill utilisation and efficacy beliefs… Competence needs to be measured as a function of rationalist measures of capacity, interpretative measures (skill utilisation, determined by the worker’s understanding of job requirements) and cognitions of capability (efficacy beliefs) (Burr et al)

3.2 Commitment While a firm or an organization can mandate a certain level of commitment, true and deep commitment is voluntary. It should be encouraged and nurtured to produce meaningful results. Ulrich (1998) calls it “emotional energy” which in turn depends on a complex and dynamic array of factors over which the firm may have only little control. Meyer and Allen (1992) identified that organizational commitment is made up of three types of commitment: continuance commitment, normative commitment, and affective commitment. It is beyond the scope of this paper to offer in‐depth discussion on the above three types. For purposes of this discussion, affective commitment which is described as “loyalty to the organisation, demonstrated by emotional attachment and identification with organisational goals.” (Meyer & Allen 1984). When affective commitment is present then the individual displays free will not only to expand his or her own IC assets but also uses it effectively to advance the well‐being of the organisation. Affective commitment is voluntary and deep commitment to the success and goals of an organization. This happens when an employee’s personal and professional values are aligned with that of the organization. In such cases, the individual is willing to “go the extra mile” for the welfare of the organization.

3.3 Control Intellectual capital that is beyond the control of the firm is of limited use. While not all IC resources can be brought under the full and effective control of an organization, some form of identifiable control is essential in order to reap the benefits of IC. There are many cases where companies have lost valuable IC to competitors because they did not pay attention to issues such as ownership, transference, transformation and use of the IC assets. Without some measure of control, translating IC assets into value for the company becomes a challenge. Hence, Roos et al (1997) expanded Ulrich’s model and included control as an essential component. Thus, the current framework of IC measurement and evaluation includes the three elements of competence, commitment and control. While these three elements provide the basis for an initial assessment of the IC

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Uma Gupta and Joseph Azzopardi assets of an organization, the model can be enriched by exploring and including two additional important parameters that are essential components of any value discussion of an organization. These two parameters are Creativity and Culture.

3.4 Creativity Human capital, which is one of the essential components of intellectual capital, is made up of (Davenport 1999):

Ability

Behaviour

Effort

Time

These four elements of human capital are necessary but not sufficient to assess the IC of a firm. Ability or competence/capability has been discussed in an earlier section. Behavior and effort relates to commitment. Time refers to the amount of time that an individual is willing and able to apply his or her IC to the welfare and well‐being of the organization. It, too, can be tied to ability and commitment. However, one component that is missing in this discussion on human capital is creativity. At the heart of developing and leveraging intellectual capital of an individual or organization lies the creativity factor. This factor has been indirectly referred to in other discussions on IC. We believe it deserves to be explicitly discussed and incorporated into the IC framework. “Creativity is the general term we use to describe an individual’s attitude and ability for, and style(s) of creative thinking that leads to a structured and intentional activity, mental and/or physical. This activity may be personal and/or collective, occurs in a specific space–time, political, economic, social, and cultural context, and interacts with it. The creative activity aims to realize the creative potential of the creator(s) and leads to tangible or intangible product(s) that is (are) original, useful, and desirable at least for the creator(s). The creative product(s) should be used for ethical and constructive purposes.” (Kamplyis and Valtanen, year and page number) Creativity is vital for the enhancement of declarative knowledge and the effective use of rich, complex and intricate procedural knowledge. Intellectual capital calls for the use of declarative knowledge in complex problem‐solving and hence, it has short‐term and long‐term implications for a firm. It impacts how IC that resides within an organization is used to solve problems and create future growth opportunities. While creativity is almost always referred to in a positive context, the improper or unethical use of creativity can also diminish the value of a firm. Hence the ability to assess the creative index of a firm and relate it to the intellectual capital resources of a firm is of value for researchers and practitioners.

3.5 Culture Intellectual capital of employees is of value only if an organization capitalizes on it. In other words, if intellectual capital of employees is viewed as a seed, the organization that the employee is associated with can be viewed as the soil where the seed is planted. Thus the characteristics of the soil and its nurturing elements impact the ability of the seed to grow and bloom. The soil for IC is the culture of an organization. Often, there are indirect references to the role of organizational culture in IC literature, particularly as it relates to structural capital where structural capital is defined as the organization’s “strategies, processes and policies.” (Dzinkowski 2000). However, an organization’s culture is more than the sum of its strategies, processes and policies and hence use the following definition of organizational culture (Wu 2007): “Organizational culture is defined as organizational cultures consist of interactions among critical masses of people with different preferences and past choices that have the capacity to wield critical influences upon each other, both in the short and long terms, within and beyond the confines of organizations and resource constraints.” However, culture is immensely powerful and impactful and hence must be singled out and its impact should be carefully studied. While many other elements of an organization can be copied, such as structures, systems, and even products and services, an organizational culture that promotes learning and knowledge leveraging is hard to imitate and further, provides competitive and sustainable advantage to firms. In addition, a healthy

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Uma Gupta and Joseph Azzopardi organizational culture contributes to other critical components of intellectual capital, such as human capital, technology capital, business capital, and social capital (Martin‐de‐Castro, 2006)

4. Conclusion This paper expanded the existing assessment framework of intellectual capital (capacity, commitment, and control) by two additional elements, namely creativity and cultre. These forces, taken together, influence the ability of a firm to capture, assess, monitor, grow and leverage its intellectual capital as a vehicle to build a competitive and sustainable enterprise. Once the intellectual capital of an organization is assessed using the above variables, it should be studied within a larger framework that helps a firm to assess its positioning with other competitors within its industry.

References Barney, J. (1991), Firm resources and sustained competitive advantage, Journal of Management, Vol. 17 No.1, pp.99‐120. Cegarra‐Leiva,D., M. Eugenia Sánchez‐Vidal, Juan Gabriel Cegarra‐Navarro, (2012) Understanding the link between work life balance practices and organisational outcomes in SMEs: The mediating effect of a supportive culture, Personnel Review, Vol. 41 Iss: 3, pp.359 – 379 Herremans, Irene, and Robert G. Isaac, (Summer 2004) The Intellectual Capital Realization Process (ICRP): An Application of the Resource‐based View of the Firm, Journal of Managerial Issues , Vol. 16, No. 2, pp. 217‐231 Housel, T.J., Sarah K. Nelson, (2005) Knowledge valuation analysis: Applications for organizational intellectual capital, Journal of Intellectual Capital, Vol. 6 Iss: 4, pp.544 – 557 Ienciu, N.M., (2011) Retrospective of intellectual capital literature review. Review of Business Research 11.3 (2011): 74+.Academic OneFile. Web. 22 Dec. 2012. Jacobsen, K., Peder Hofman‐Bang, Reidar Nordby Jr, (2005) The IC Rating™, Journal of Intellectual Capital, Vol. 6 Iss: 4, pp.570 – 587 Lim, L.L.K., Peter Dallimore, (2004) Intellectual capital: management attitudes in service industries, Journal of Intellectual Capital, Vol. 5 Iss: 1, pp.181 ‐ 194 Manzari, M., Mostafa Kazemi, Shamsoddin Nazemi and Alireza Pooya, (2012) Intellectual capital: Concepts, components and indicators: A literature review, Management Science Letters 2, 2255–2270 Marr, B. (2012) Intangible asset measurement: in order to keep tabs on how your organisation's intellectual capital is performing, you must ask all the right questions. Financial Management [UK] June 2008: 32. Martín‐de‐Castro, G., José Emilio Navas‐López, Pedro López‐Sáez, Elsa Alama‐Salazar, (2006) Organizational capital as competitive advantage of the firm, Journal of Intellectual Capital, Vol. 7 Iss: 3, pp.324 ‐ 337 Mouritsen, Jan, Roslender, Robin., Critical Perspectives on intellectual capital: Critical Perspectives on Accounting, Vol 20, Issue 7, ‐ 7 Pike, S., L. Fernström, G. Roos, (2005) Intellectual capital: Management approach in ICS Ltd, Journal of Intellectual Capital, Vol. 6 Iss: 4, pp.489 – 509

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Developing & Measuring Intellectual Capital: A Conceptual Model for High Technology Companies Harold Harlow Wingate University, Charlotte, USA h.harlow@wingate.edu Abstract: Developing intellectual capital at companies often results in large numbers of patents filed with little results other than protection of firm historical patents against intellectual property intrusion by current competitors or future competitors. This paper presents exploratory research to classify patents filed in past years by several major technology companies which shows the need for the new strategic conceptual model presented in this paper. A new approach to align corporate intellectual property strategies, management capability and process with strategic intent is presented which enables firms to assure that all needed considerations are present in a comprehensive strategy of intellectual capital property development, especially at technology firms. Keywords: technology strategy, intellectual capital, innovation

1. Introduction This paper addresses the gap between the stated objectives of many high technology companies to maintain a market leading position in the acquisition of intellectual capital and the outcomes that lead technology companies to explore a high number of intellectual property dead ends that result in no improved processes or new products to market or minimal protection of existing products’ intellectual property at great expense. The strategic implications of this gap are lowered earnings in the future coupled with faster firm decline to a follower or imitator position, often without the firm recognizing this fall. Patents are the major intellectual property/capital of high technology companies as well as trade secrets and tacit knowledge. The question is what management strategic process or methods can be applied to allow companies to direct their resources toward the most productive of these major intellectual property categories. This question is answered by the intellectual property development strategy employed by the firm. Patents are the most visible major intellectual property of these companies and they serve as an example of what is happening at these firms. Only two US companies were in the top ten filers of USA patents in 2011. IBM was number 1 with 6180 filings followed by Microsoft at #6 with 2311. Trends indicate that Samsung Electronics is quickly gaining on #1 IBM and may gain the #1 spot as early as 2014. According to the United States Patent and Trademark Office the following Table 1 presents the top 10 companies filing US patents in 2011: Table 1: Patent filings 2011 by Company (USPTO, 2012) Rank

Grants

Company Name

Country

6180

International Business Machines Corp

United States

4894

Samsung Electronics Co Ltd KR

Korea

2821

Canon K JP

Japan

2559

Panasonic Corp JP

Japan

2483

Toshiba Corp JP

Japan

2311

Microsoft Corp

United States

2286

Sony Corp JP

Japan

1533

Seiko Epson Corp JP

Japan

1514

Hon Hai Precision Industry Co Ltd TW

Taiwan

1465

Hitachi Ltd JP

Japan

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Harold Harlow The implication is that intellectual property is quickly moving from the developed to the newly developed world at an increasing rate and further that patent filings are increasingly seen as defensive tactical moves by many filers, not as part of an integrated strategy to develop strategic intellectual outputs. From Table 1, it is clear that this means that strategies employed by companies to protect intellectual capital using patents is increasingly a strategy employed by non‐US technology companies as well as US companies and any US leadership in this area is fading quickly. A new conceptual framework is needed for all technology companies to maintain their intellectual capital whilst meeting the competitive thrust of the other technology companies. It is clear that a trap is being sprung with the idea that patents equal intellectual capital protection and optimal resource usage in the pursuit of business applicable intellectual capital. Table 2 below presents this researcher’s analysis of four USA companies and their patent usage developed from United States Patent and Trademark Office (USPTO) that was researched and categorized into the various patents awarded (name withheld 2012) over the previous year. The research shows that only about a third of the patent filings were directed toward new products or processes and that the vast majority were either basic research or product extensions or incremental improvements. While patents in these non‐new product areas are necessary at times, the over reliance on numbers of patents filed seems striking. Table 2 shows that for these four companies usually perceived as high technology innovation companies the overwhelming numbers and percentages of new patent filings are in process or product improvements (35‐59% of the patent filings of the companies researched) indicating an incremental approach to innovation, not disruptive new processes or products. In addition, a large number are classified by the researcher as of “dubious” quality (10‐23% of the patents filed by the four companies) and most likely will never see usage in a customer product or company process. This analysis is only a preliminary start toward the research needed to change the intellectual property development direction of technology companies away from their dubious non‐new product focus innovations and protective patents toward a strategic focus on truly new market driven inventions. Table 2‐Analysis of patent outcomes /usage by company by total numbers and percentages (USPTO, 2012; name withheld, 2012) Type of Patent/#

All

Dubious Value

% D

Basic Research

% BR

Product /Process Extension

% PP Ext

New Product

% New

IBM Microsoft GE QCOM

6180 2311 1448 928

632 451 212 211

10 20 15 23

1278 164 345 161

21 7 24 17

2992 1367 649 321

48 59 45 35

1278 329 235 235

21 14 16 25

A new conceptual framework is needed to strategically drive the creation of usable intellectual capital along with the coupling of strategic intent to create new and novel products rather than expend resources and talent on broadly defined protection patents, patents of dubious quality and product/process extension patents. Further, a new model of intellectual capital development and strategic choice is needed for top managers that matches strategy with intellectual capital management capability and that model is presented in this paper.

2. Literature review Intellectual capital has been used as a proxy for knowledge and as a proxy for tacit knowledge as well. All of the definitions of intellectual capital imply that knowledge is both known to management and can be converted into value (Edvinsson and Sullivan, 1996) and is about knowledge and knowing capability of a social collectivity (Nahapiet and Ghoshal 1998), packaged useful knowledge (Stewart 1997), “ and Intellectual capital= competence × commitment” (Ulrich 1998). From this notion that management knowledge can be converted into value the idea of an intellectual property strategy can be developed. It is not sufficient to have knowledge assets, patents, or other marketable intellectual property. In a knowledge creating company, managers have the responsibility to unleash that knowledge into value‐creating actions aimed at customers and to generate and exploit that knowledge‐either public or proprietary‐more effectively than their competitors. In addition, managers are also responsible to generate and exploit current firm knowledge better than their competitors and to use public knowledge better than their rivals (Von Grogh 2000). Von Krogh, Roos and Slocum (1994) suggest that there are essentially only two strategies used and that those two are 1) advancement and 2) survival.

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Harold Harlow The late Peter Drucker (1999) said that “knowledge has become the key economic resource and the dominant‐ and perhaps even the only‐source of competitive advantage.” The firm specific concept of intellectual capital was introduced in the early 1990s which connected the idea of a firm’s knowledge to the concept of firm intellectual capital to address valuation of intangibles and to further explain the idea of value creation and its relationship to firm performance (Roos and Roos 1997) Since this time, researchers have attempted to understand how intellectual capital is generated at firms and what effect this intellectual capital has on firm performance. Understanding how intellectual capital can be converted into metrics/methodology and which methods produce the most valuable tacit knowledge has been presented in prior work (Harlow 2008) A stated intellectual capital strategy for technology firms becomes more important as firms participate in more “turbulent” (Ansoff 1990) environments. Turbulent environments that are those where lack of visibility to the future and increasing complexity dictate a managerial climate of strong competence, high rewards and flexible risk taking. Incremental innovation uses the tried and true method of following the trend line for the product innovation and developing an improvement, not a breakthrough or new product or process. While this type of innovation is needed to increase product performance and efficiency of production, novel new products (as well as business areas) are left unfunded while the corporation slowly matures out of its industry. This is the strategy that appears from the research presented in this paper to have developed in patenting intellectual property of dubious value and without strong customer centric focus. The first step is to understand the concepts of knowledge and intellectual capital and how these relate to how we see knowledge generation and more specifically tacit knowledge creation. Somech (1999) details how tacit knowledge is quantified in college freshmen and can be measured as the students gain more tacit knowledge as they progress to seniors. The term practical intelligence has been used as a proxy for tacit knowledge (Sternberg, 1997). Others have developed tools for measuring tacit knowledge as part of their work on quantifying managerial intelligence (Wagner and Sternberg 1992). Measuring tacit knowledge is also seen as “risky business” (Nonaka and Takeuchi 1995). O’Dell and Grayson (1998) detail ways that internal knowledge can be transferred using “best practices” that supports this paper’s development of a theory. The Intellectual Capital Services (IC Index), originally developed in Scandinavia and Australia by Johan and Göran Roos (1998), identifies four categories of intellectual capital: relationship, human, infrastructure and innovation; it then looks at the relative importance of each, and also at the impact of changes in intellectual capital. This is an important input to the conceptual model presented in this paper. Knowledge Management Systems have been employed at many companies in an attempt to capture the tacit and explicit knowledge of the firm. A lack of empirical information on the effect of Knowledge Management Systems (KMS) that includes both tacit and explicit methods has meant that firms often choose technology solutions that are designed to capture and disseminate mostly explicit knowledge (Almeida and Kogut 1999). While these mostly explicit knowledge capturing systems offer the advantage of ready usage metrics, their actual contribution to effective knowledge management (KM )within the firm is less clear (Berman et al 2002). The degree of explicit codification‐ more manuals, patents or product plans do not presage success at firms and does not indicate that the knowledge encoded is valuable or unique. Firms may have extensive libraries of codified knowledge (patents) that is rarely accessed or is bypassed by unmapped tacit processes. This is another prior research that indicates a strong need for a new conceptualization of firm intellectual property development driven by people and processes. Gaps in the current research reveal that there is only one study (Harlow 2008) that addresses the validation of which KM methods (either explicit or tacit or a combination of both) are more or less effective, and there has been little research that looks at the relationship of KMS to the firm’s outcomes (Grant 1996). The Harlow (2008) study proposes an innovation strategy based on the use of knowledge management learning systems to promote measureable outcomes in innovation. Firms are able to develop a sustainable competitive advantage in KM by developing a mix of KM methods that complement and enable their core strategies (Hansen 2002). However, despite large investments in KM technology, many of the performance outcomes are not clear and the causal relationship between what works and what does not work has not been established empirically (Liebeskind 1996). This gap in the causality is another rationale for this research. A firm’s overall economic, strategic, and innovation performance is dependent on the degree to which the firm can use all of the knowledge created by the firm and turn this knowledge into value‐creating activities (Krogh 1998). Tacit knowledge extraction, dissemination, and collaboration are difficult to effect (Markus 2001). Tacit

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Harold Harlow knowledge may be best understood by the assertion that ‘we know more than we can tell’ (Polanyi 1966). This observation is quickly supported if people are asked to write out a certain process or workflow. Persons asked to explain how to drive a car cannot fully describe how to accomplish this common task of everyday life. Much of the process and workflow is contained in a mutual understanding of the work or process and it is not easily documented nor can it be easily communicated. While tacit knowledge and explicit knowledge coexist in a continuum (or as a knowledge spiral) complementing each other, the explicit knowledge forms are more easily extracted and measured (Nonaka 1998). The measurement of tacit knowledge is less clear. Tacit knowledge can be part of the group collective knowledge (Spender 1996). This socio‐cultural knowledge (Castillo 2002) drives the organization, but it is difficult to measure. Insch, McIntyre and Dawley (2008) have developed a model for the measurement of tacit knowledge. “They present 6 hypotheses that support their proposed academic outcomes tacit knowledge model regarding the role of cognitive (self‐motivation, self‐organization); technical (individual task, institutional task); and social (task‐related, general) skills” (Insch et al, 2008) All six of their hypotheses are supported in their research. Value‐creating capability resides in the know‐how or tacit knowledge of the engineers, managers and marketing staff and this dynamic tacit knowledge capability creates sustainable competitive advantage (Teece 1998). These subject matter experts must be able to fit into an automated system that allows tacit knowledge dispersal and tacit knowledge use by both the experts and the rest of the firm’s staff and depends to a large degree on the KM systems that are employed (Maybury et al 2000). This is needed for both the firm’s survival and advancement strategy (Von Krogh and Ichijo 2000). It is apparent to anyone researching in this area that the idea of what tacit knowledge is a confusing and often divergent set of ideas. Many studies ‐Hennert (1992) Kim and Hwang (1992), Hansen(1992), Sveiby (1987), Sternberg (1993), Ruggles (1998) and Harlow (2008)‐have demonstrated empirical approaches to quantify tacit knowledge this research forms a solid foundation to imply that measurement of tacit knowledge can be successful. Generic strategies of firms include several types. Strategy types have been proposed (Miles & Snow 1978) and empirically tested (Dess and Davis 1984). Key components of strategy types include scope of the business or domain, resource deployment in marketing or R&D, asset management or parsimony, degree of vertical integration, and so forth (Miles and Snow 1978). All of these strategies are driven by the use of firm‐specific competencies gained through effective use of knowledge. Generic strategies are closely related to the Prahalad and Hamel core competence models because they target developing core competencies for cost leadership, differentiation, and market and product focus. Porter’s (1998) discussion of sustainable competitive advantage relates sustainable competitive advantage as the ability to “outcompete” other businesses in the chosen industry. Empirical studies (Dess and Davis 1984) of these strategies have been made to determine the validity of these strategies. The results are that high profitability contains information on investment strategy, relative cost position, technology leadership, timing of market entry, new product introduction, product quality, marketing expenditures relative to competitors, characteristics of the market served and market environments, the nature of competition, barriers to market entry, and operating results and financial performance of each business. Each of these measures can be used as a proxy for effective use of Knowledge Management Systems (KMS).

3. The model While Polyani (1965) is correct that we “know more than we can tell”, his work reflects a time of early and first thoughts concerning tacit knowledge and intellectual capital. It is time to bring this concept up to present day standards and proceed to apply this concept more thoughtfully to business success by adding both qualitative and quantitative measures driven by a conscious company strategy of intellectual capital generation and intellectual property management capability. A conceptual model is presented in this paper which guides top management at innovative technology companies in the development of intellectual capital which is not necessarily a derivative of past patent or

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Harold Harlow product successes but which focuses corporate resources on the strategic direction and processes needed to increase the intellectual capital of the firm and the delivery of new products to customers. This new intellectual capital would be either patents or trade secrets and would represent a conscious strategy of offensive intellectual capital creation rather than the defensive strategies apparent by the company patent usage research presented earlier in this paper. The model is straightforward and uses the idea that intellectual capital strategy drives firm processes which are dependent on management capability. This ultimately drives firm objectives (Ansoff 1990). Those end point objectives are measureable and include measures such as patents or copyrights as well as other market performance measurements. The following model starts with an identification of which strategy to pursue and uses the following scaled input factors. Intellectual capital aggressiveness indicates a firm that uses all of the following strategic parameters to create a cohesive intellectual capital strategy illustrated in as the first element of the model:

Positioning

Product Dynamics

Positioning Dynamics

R&D Investment

Technology Dynamics

Competitive Dynamics

Environmental Turbulence

Aggressiveness of firm’s strategy

Positioning refers to leadership position relative to the research product and process that the firms employ. It is a market assessment parameter. Product Dynamics refer to the frequency of new products, length of product life cycle and successive technology product advance. R&D investment is measured as a percentage of the firm’s sales invested in R&D, with a breakdown by R as a percentage of profits and D as a percentage of profits. Technology Dynamics is the length of the product life cycle, frequency of new technologies and the number of competing technologies. Competitive Dynamics is a market parameter that includes technological product differentiation, technology as a competitive tool, competitive intensity and rivalry between firms, forced product obsolescence, technological response to government regulations and technological response to consumer pressures. An overall measure of the firm’s strategic aggressiveness coupled with an assessment of the environmental turbulence of the market completes the strategic factor analysis (Ansoff, 1990). Strategic aggressiveness is the degree of discontinuity between successive strategy implementations such as from a follower to an innovator strategy. Environmental turbulence is defined by Ansoff (1990) as a continuum of levels from 1 to 5 with increasing turbulence as you proceed from repetitive (scaled as a 1) business environments to discontinuous (4) or surprising environments and most turbulent (5). Turbulence is also determined measured as to your firm’s ability to predict and assess the future couple with the future complexity of the environment. An assessment of these factors leads to a clearer understanding of the current strategy as well as any “gaps’ that exist that must be addressed by your strategic plans. The second major element of my model is the Intellectual Knowledge and Management Capability. This part of the model is based on the work of Harlow (2008) and others indicating that tacit knowledge (knowhow) can be developed and a measure applied to that parameter and let managers know how capable (tacit knowledge management capability) their firms are toward the meeting the strategic objectives of the firm. People and processes support his layer of the model and enhance the intellectual capital and management capability enhancing parts of the model. The organizational processes include the culture of the firms and enhancement of a knowledge creating environment with proper rewards for managers (climate), capacity and managerial slack to apply to new ideas and technologies and a tilt toward highly competent managerial measures rather than only a focus on the technology aspects of new products. Knowledge management and sharing knowledge and creating social capital within the organization are crucial to development of this Intellectual Capability (IC). The Outputs from the IC development model include new products, patents, trademarks, copyrights, trade secrets, as well as more effective marketing due to market knowledge from customers and meeting new customer needs. These outputs represent the final part of this model and are depicted in Figure 1 below:

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Figure 1: The model of intellectual capital development The identification of what is intellectual capital coupled with applicable metrics and the model developed enables firms to create a platform for intellectual capital generation that starts with the strategy, is enabled by knowledge creating and capability enhancing processes and ends with measureable outputs. The model takes intellectual capital out of the realm of unknown phenomena into that of scientific inquiry. My model has immediate use for practicing managers to integrate their technology strategy, knowledge management methods and performance outputs in a logical and both qualitative and quantitative way.

4. Conclusion While the aforementioned “questions” are open to discussion in the realm of “knowledge” research, there can be little doubt that some method of identifying tacit ways of knowing is necessary. In addition, the combination of both ways of identifying tacit knowledge with a methodology to measure through manager’s ratings is a giant step forward in the intellectual capital generation of tacit knowledge toward a more scientific approach to measuring firm intellectual capital and tacit knowledge. The prior research in the intellectual capital and knowledge management field of study can be summed up as concentrating on the processes that produce outputs such as patents and new products and processes. While this is descriptive for understanding the concept’s impact on business it is not sufficient to understand what intellectual capital and knowledge creation is and what it is not. What is intellectual capital? According to Roos (2006) intellectual capital is “an extension of strategic innovation, an integrated part of any business model thinking and an extension of the resource and competence based views of the firm”. Roos (2006) also describes intellectual capital as a process of transforming assets of the firm using financial, competence, physical, organizational and relational assets into intellectual capital. These assets are a matrix which enables the firm to alter the intellectual property creating paths to create a different business model or intellectual capital output. My conceptual model builds on Roos (2006) and his idea that paths can be created for intellectual capital output (IC) and can be measured and intellectual capital increased by managers stating their innovation and intellectual capital strategy as part of their firm’s innovation strategy (prospector /innovator, analyzer/follower, defender/ imitator). Employing knowledge management capability methods that identify the novel and new for the company while measuring their effect on the firm’s innovation metrics results in a more complete understanding of why R&D is focusing on particular research agendas. Firms may find that increasing their knowledge management capability leads to more trade secrets and process improvements and

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Harold Harlow less need for expensive and unproductive R&D where the chance of success in the marketplace is often 10% or less. The research implications for this model are that changes in IC inputs and IC capabilities can be measured and specific methods related to the most gain for the investment made. A new strategy for knowledge creation would be accompanied by a change in intervening processes using the tacit knowledge measured changes as a proxy for the intellectual capital generated as well as a new set of outputs. The research using this model could be targeted toward technology companies to identify whether or not the chosen knowledge generation strategy is having the intended effect and what changes need to be made to processes and people that would yield the desired outputs. Further research to advance this preliminary research that uses this model to explore the model’s prevalence in technology companies by using a four‐step process of 1) determining which patents are less or more useful toward the development of new products to see which firms are creating useable intellectual property and 2) assessing the strategic intent of the firm and 3) determining the strategic management capability of the firm and 4) assessing the use of people and processes that drive the creation of those strategic intellectual outputs.

References Almeida, P. & Kogut, B. (1999). “Localization of Knowledge and Mobility of Engineers in Regional Networks”. Management Science, Vol. 45: 905‐917. Ansoff, I. (1990) Implanting Strategic Management. Oxford Press. Harlow, England. Barney, J. B. (1991) “Firm Resources and Sustained Competitive Advantage”, Journal of Management, Vol. 17: 99‐129. Brock, E. N., & Anthony, W. P. (2002) “Tacit Knowledge and Strategic Decision‐Making”, Group & Organization Management, December, Vol.27: 436‐455. Castillio, J. (2002) “A Note on the Concept of Tacit Knowledge”, Journal of Management Inquiry, Vol. 11(1): 46‐57. Cook, S. D. N., Brown, J. S. (1999) “Bridging Epistemologies: the Generative Dance between Organizational knowledge and Organizational Knowing”, Organization Science, Vol.10: 381‐400. Drucker. P. (1999) Managing in a Time of Great Change, Harper Business, New York. Drucker, P. (1959) The Landmarks of Tomorrow, Harper and Brothers, New York. Edvinsson, L., Sullivan, P. (1996) “Developing a Model for Managing Intellectual Capital”, European Management Journal, Vol. 14(4): 356‐365. Edvinsson, L., Malone, M. (1997) Intellectual Capital:Realizing Your Company's True Value by Finding its Hidden Roots, Pratkus, London. Grant, R. M. (1996) “Toward a Knowledge‐Based Theory of the Firm”, Strategic Management Journal, Vol. 17 (summer special issue): 109‐122. Hansen, M. T. (2002) “Knowledge Networks: Explaining Effective Knowledge Sharing in Multiunit Companies”, Organization Science, Vol.1: 232‐249. Hambrick, D. C. (1983). “Some Tests of the Effectiveness of Functional Attributes of Miles and Snow’s Strategic Types”. Academy of Management Journal, 26, 5‐27. Harlow, H.D. (2008) "The Effect of Tacit Knowledge on Firm Performance", Journal of Knowledge Management, Vol. 12 Iss: 1, pp.148 – 163. Harlow , H.D.(2012) “An Analysis by Patents Issued by Major USA Companies”, Unpublished paper. Holthouse, D. (1998) “Knowledge Research Issues”, California Management Review, Vol. 40: 277‐292. Insch,G.S., McIntyre, N., &Dawley,D.(2008) “Tacit Knowledge: A Refinement and Empirical Test of the Academic Tacit Knowledge Scale”. Journal of Psychology, 142(6), 561‐580. Leonard, D., Sensiper, S.(1998) “The Role of Tacit Knowledge in Group Innovation”, California Management Review, Vol. 40(3): 112‐132.Liebeskind, J.P.(1996) “Knowledge, Strategy and the Theory of the Firm”, Strategic Management Journal, Vol.17: 93‐107. Loshin, D. (2001) Enterprise KM, Morgan Kaufmann,San Francisco. Lubit, R. (2001) “Tacit Knowledge and KM: The Keys to Sustainable Competitive Advantage”, Organizational Dynamics. Vol.29 (3): 164‐179. Markus, M. L. (2001) “Toward a Theory of Knowledge Reuse: Types of Knowledge Reuse Situations and Factors in Reuse Success”, Journal of Management Information Systems, Vol. 18(1): 57‐94. Martinez‐Brawley, E. E. (1995) “Knowledge Diffusion and the Transfer of Technology: Conceptual Premises and Concrete Steps for Human Services Innovators”, Social Work. Vol. 40: 670‐682. Maybury, M., D’Amore, R., & House, D.(2000) “ Automating the Finding of Experts”, Research Technology Management, Vol.43(6):12. Miles, R.E., Snow, C.C., (1978), Organizational Strategy, Structure and Process, McGraw‐Hill, New York, Nahapiet, J. and Ghoshal, S. (1998) “Social Capital, Intellectual Capital and the Organisational Advantage”, Academy of Management Review, Vol. 23(2): 242‐266. Nonaka, I. (1994) “A Dynamic Theory of Organizational Knowledge Creation”, Organization Science, Vol. 5(1): 14‐38.

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Harold Harlow Nonaka, I., & Takeuchi, H. (1995) The Knowledge Creating Company: How Japanese Companies Create the Dynamics of Innovation, Oxford University Press, New York. Nonaka, I., & Konno, N. (1998) “The Concept of “Ba”, Building a Foundation for Knowledge Creation”, California Management Review, Vol. 40 (3): 1‐15. O’Dell, Carla and Grayson, C Jackson (1998). If Only We Knew What We Knew: The Transfer of Internal Knowledge and Best Practice, The Free Press, New York. Polanyi, M. (1966) The Tacit Dimension, Doubleday Anchor, Garden City. Porter, M. E. (1980). Competitive Strategy: Techniques for Analyzing Industries and Competition. New York: Free Press. Porter, M. E. (1998). On Competition. Harvard Press, Boston. Probir, R. (2002). “Tacit KM in Organizations: A Move Towards Strategic Internal Communications Systems”, Journal of American Academy of Business, Vol.2(1): 28‐33. Roos, G., Pike, S.,Fernstrom, L.(2006). Managing Intellectual Capital in Practice, Butterworth‐ Heineman, Elsevier, New York. Roos, G. and Roos, J. (1997) “Measuring Your Company's Intellectual Performance”, Long Range Planning, Vol. 30(3): 413‐ 426. Roos, J., Roos, G., Dragonetti, N. and Edvinsson, L. (1998), Intellectual Capital: Navigating in the New Business Landscape, University Press, New York. Roy, P. (2002) “Tacit KM in Organizations: A Move towards Strategic Internal Communications Systems”, Journal of American Academy of Business, Vol. 2(1): 28. Ruggles, R. (1998) “The State of the Notion: Knowledge Management in Practice”, California Management Review, Vol.40 (3): 80‐89. Spender, J.C. (1996) “Competitive Advantage from Tacit Knowledge?”, In Moingeon, B., Edmundson (Eds.), Organizational Learning and Competitive Advantage: 56‐73. London: Sage Publications. Somech, A., & Bogler, R. (1999). “Tacit Knowledge in Academia: Its Effects on Student Learning and Achievement”, Journal of Psychology, Vol.133: 605‐616. Spender, J. C. (1994) “Organizational Knowledge, Collective Practice and Penrose Rents”, International Business Review, Vol.3: 353‐368. Sternberg, R. J. (1997) “Managerial Intelligence: Why IQ Isn’t Enough”, Journal of Management, Vol. 23, pp. 475‐493. Sternberg, R.J., Wagner, R.K. (1995) “Testing Common Sense”, American Psychologist, Vol. 50(11): 912‐927. Stewart, T.A. (1997). Intellectual Capital: The New Wealth of Organizations. Nicholas Brealey Publishing, London. Teece, D. J. (1998) “Future Directions for KM”, California Management Review, Vol. 40(3): 123‐126. Teece, D. J. (2001). Managing Intellectual Capital: Organizational, Strategic, and Policy Dimensions, Oxford University Press, Oxford. Ulrich, D. (1998) “Intellectual Capital = Competence x Commitment”, Sloan Management Review, Vol. 39(2): 14‐18. United States Patent and Trademark Office, (2012). “US Patent Filings by Company Report”. Retrieved from USPTO online database November 6, 2012. http://www.uspto.gov/web/offices/ac/ido/oeip/taf/asgstc/usa_ror.htm. Von Krogh, G., Ichigo, 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. Von Krogh, G. (1998) “Care in Knowledge Creation”, California Management Review, Vol.40 (3): 133‐153. Von Krogh, G., Roos, J., and Kleine, D., (1994) “An Essay on Corporate Epistemology”, Strategic Management Journal. 15: 53‐72 Special Issue. Wagner, R. K., & Sternberg, R. J. (1985) “Practical Intelligence in Real‐World Pursuits: The Role of Tacit Knowledge”, Journal of Personality and Social Psychology, Vol.49: 436‐458.

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The Impact of Gender and age on Knowledge Absorption: An Empirical Study on NGO Beneficiaries in Bangladesh Sheikh Shamim Hasnain Bedfordshire College, UK majshamim2004@yahoo.com Abstract: This empirical study investigates the impact of gender and age on knowledge absorption among the NGO (Non‐ Governmental Organisation) beneficiaries in Bangladesh. About 22,000 NGOs are working in Bangladesh with a view to developing the socio‐economic conditions of the people. Knowledge transfer is a crucial element of the Knowledge Management (KM) process. Successful knowledge absorption by the recipients of the knowledge is an indication of a successful knowledge transfer. The NGO beneficiaries are the main actors who are engaged in receiving and absorbing the knowledge transferred by the NGOs. So it is imperative to know the knowledge absorption capacity of the beneficiaries. This study follows a multi‐stage sampling procedure. Fourteen NGOs (7 large and 7 small of the NGO Affairs Bureau enlisted NGOs) from each administrative division of Bangladesh are purposefully selected. Fifty‐four semi‐structured interviews consisting of top, middle and lower levels based on the salary grades (18 interviewees from each level) were conducted. Simultaneously, Thirty‐five questionnaires were also administered among the beneficiaries (5 from each administrative division, e.g. 5X7=35). Content analysis technique is used to analyse the interview transcript. The study reveals that female beneficiaries are more capable of absorbing knowledge than male beneficiaries. The study also finds that females of the age group 36‐55 years have more knowledge absorption capacity than groups of below 20 and 21‐35 years. This research has been carried out on enlisted NGOs (e.g. foreign fund recipients) in Bangladesh. Future researchers may carry out a similar study with non‐foreign fund recipient NGOs. Future researchers may also conduct a similar study in a different country. Keywords: knowledge transfer, knowledge absorption, NGOs, Bangladesh

1. Introduction: The IQ levels of males and females have been investigated by many researchers (Naderi, Abdullah and Aizan, 2008;Zaidi, 2010; Lynn and Kanazawa, 2011). However, hardly any knowledge management guru (Nonaka and Takeuchi, 1995; Chae and Bloodgood, 2006; Hasnain, 2012; Jashapara, 2004; Prusak, 2001; Hasnain and Jasimuddin, 2012; Argote, Ingram, Levine and Moreland, 2000) has investigated the issue. Naderi, Abdullah and Aizan (2008) investigated the differences of IQ levels between men and women. Lynn and Kanazawa (2011) carried out a longitudinal study on sex difference in intelligence. Zaidi (2010) made a review on gender differences in human brain. She exhibits the anatomical differences of male and female human brains and finally concludes “both sexes are equal in intelligence, but tend to operate differently” (p. 37). Similarly, Jensen and Johnson (1994) find that there is no impact of sex on IQ. Chowdhury, Butel, Hakki and Ismail (2009) argue that “transfer of knowledge includes two actions; one is transmission which means sending knowledge to potential receiver, and another is absorption meaning that knowledge must be incorporated either by a person or a group” (p. 53). So knowledge transfer is useless if it is not absorbed by the knowledge recipient (Davenport and Prusak, 2000).The gradual expansion of the NGOs has made this sector a powerful and strong industry in Bangladesh. Due to their activities in human resource development, they could become an inseparable entity in the culture of the country. NGOs transfer knowledge on microcredit (Rahman, 2006a), social awareness (Ahmad, 1999), health (Mahamud, 1998), education (Buckland, 1998), agriculture (Lewis, 1997), income generating skills (Begum, 2008),disaster management (Rahman, 2000). Thus they could reach the doorsteps of millions with new hope, where government activities have yet to start (Ahmad and Townsend, 1998). Presently NGOs in Bangladesh are at the top in respect of contributions to the country’s development (Devine, 2003). Najam (1996) identifies the beneficiaries/clients, the donors/patrons and the NGO itself/employees as the most vital stakeholders in the NGO sector. In the NGO sector, knowledge transfer mainly occurs between the NGOs and their beneficiaries. So it is crucial for the NGOs to know the impact of gender and age of the beneficiaries on knowledge absorption. According to the Bangladesh Bureau of Statistics (2009), the total population of the country is 140.6 million (male population: 72 million; Female population: 68.6 million), out of which 34.6 million people (24.61%) live in rural areas. 50.62 million (36%) of the population live in poverty (World Development Report, 2002). The literacy rate of the country is 43.1% (Bangladesh Bureau of Statistics, 2009). From the above statistics, it is

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Sheikh Shamim Hasnain clear that more than 40% of the population live under the level of poverty. More than 50% of the population is illiterate. 35% of the country’s population is enjoying the benefits of the NGOs (Devine, 2003) as more than 22,000 NGOs are providing their services to the people in Bangladesh (Rahman, 2006). It may be mentioned that a huge number of the country’s illiterate population are NGO beneficiaries. The illiterate beneficiaries find it difficult to absorb knowledge transferred by the NGOs. Several researchers (Szulanski, 1996; van Wijk et al., 2008) argue that lack of absorption capacity in the recipient is a barrier to knowledge transfer. Davenport and Prusak (2000) go further by arguing “if knowledge is not absorbed, it has not been transferred” (p. 101). Many knowledge management researchers consider knowledge absorption as an integral part of knowledge transfer, but unfortunately they have not empirically examined the impact of gender and age on knowledge absorption. Hence, this study works on the following research question: ‘What is the impact of gender and age on knowledge absorption in the NGO beneficiaries in Bangladesh?’ For easy understanding and assimilation by the readers, this article is divided into the following sections: (i) Preliminary issues are highlighted in the introduction section; (ii) The literature review section addresses the relevant literature (iii) Section‐3 focuses on the research methodology (iv) Findings and Discussion section exhibits and discusses the results of the empirical experiment; (v) The last section of the article concludes the study and highlights approaches for future researchers.

2. Literature review Management communities around the world have recognised and valued knowledge management (Scarbrough et al., 2005). Knowledge Management is progressing into a new paradigm (Takeuchi, 2001). Its popularity has increased significantly, especially since 1995, and it has become the elementary theme of both management philosophy and management tools (Edvardsson, 2006), with multi‐dimensional and advanced approaches (Chae and Bloodgood, 2006). Knowledge management is comparatively new (Sch tt, 2003), promising (Jashapara, 2004; Prusak, 2001; Beckman, 1999) and is a popular segment in the dictionary of management (Nan, 2008). Organisations can realise the importance of managing knowledge now‐a‐days. It deeply focuses and relies on a strong culture of a cooperative, sharing and supportive social community, with a view to achieving organisational strategic requirements (Debowski, 2006). Knowledge Management ensures superior and excellent productivity (Firestone and McElroy, 2005). Several theorists and researchers (Gamble and Blackwell, 2001; Zuckerman and Buell, 1998; Jasimuddin et al., 2006) have mentioned elements of the Knowledge Management process. For example, Gamble and Blackwell (2001) find identifying, organizing, transferring and using to be some of these elements. Zuckerman and Buell (1998) identify collection, storage, sharing, and linking as part of the process. Jasimuddin et al. (2006) describe identifying, capturing, storing, retrieving, and transferring as the important elements of knowledge transfer. Heavin and Neville (2006) find capture, storage, dissemination, and creation are the crucial ingredients of the knowledge management process. So various elements of the knowledge management process have been proposed by the researchers (Holsapple and Jones, 2006). The phases may be fragmented, and divided into various sub‐phases. The major elements of the knowledge management process are: creation, storage, transfer and use. Among them knowledge transfer is receiving wide attention (Argote et al., 2000) as it is essential for the survival and prosperity (Wathne et al., 1996) of organisations. Learning and implementations of others’ experiences for social and organisational benefit necessitate the relevance of the concept of knowledge transfer. New knowledge may promote organisational learning and innovations in new methods and practices, which may also be absorbed into routines and culture (Darr and Kurtzberg, 2000). Out of the new knowledge all stakeholders of the organisations may be benefited. Knowledge transfer provides value (Hogberg and Edvinsson, 1998) and force (Hall, 2001) to the present knowledge stock of the organisations. The UN Economic and Social Council declares ‘NGO’ as the official name for the ‘Non‐Governmental th Organisations’ (UN Resolution 288 B (X) dated 27 February 1950). Before that the term was generic for voluntary associations, non‐profit associations, and diverse organisations including international non‐ government development, new social movement, peoples and membership, and grassroots support. The term was applied to identify various organisations and not befitting with the missions, ideology and objectives of the organisations (Fernando and Heston, 1997). Vakil (1997) defines NGOs as, “self governing, private, not profit organizations that are geared to improving the quality of life of disadvantaged people” (p. 2060). The NGOs’ contribution to the socio‐economic development of developing countries has been widely acknowledged. The contributions of NGOs in various aspects of human lives such as poverty alleviation,

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Sheikh Shamim Hasnain education (Ahmad, 1999), family planning, employment, relief operations in natural calamities (Rahman, 2000), and health (Gauri and Galef, 2005) and infrastructure development for the poor, have made this sector indispensable in modern society. In many areas, the NGO sector could supersede the public and private sectors. Stiles (2002) finds that, “NGOs, by virtue of their relatively independent character, their non‐profit making status, and their link to the poor communities that they have generally served well, offer donors a relatively safe and convenient means of avoiding both public and private sector and all their dangers” (p.836). So this sector is addressed as the third sector, while public and private are named as first and second sectors (Panda, 2007; Lewis, 2005 ), and it works as supplementary to the other two in any country. NGO activities may be broadly categorised as income generation programmes, provision of social services and social organizing (Buckland, 1998). NGO activities started in Bangladesh mainly after the liberation war of 1971 (Rahman, 2006; Lewis, 1997; Devine, 2006; Karim, 2001; Karim, 2008), although one or two NGOs had been operating as missionary organisations before this time. CARE, a US based NGO was pioneer in starting activities in 1949 in the then East Pakistan, now Bangladesh. NGO activity intensified in this area after the cyclone disaster of November 1970. A cyclone injured nation and the liberation war of 1971 paralysed the country’s socio‐economic conditions. Many local and foreign NGOs came forward to rescue the country from the privations of social and economic devastation. Supply of relief goods, distribution of medicine, healthcare, construction of shelters and rehabilitation were the main agenda of the NGOs at that time. The number of NGOs in 1980s accelerated in an experiential fashion with multidimensional activities such as education of rural people, health and medicine, poverty elevation, and family planning.

3. Research methodology This study carried out semi‐structured interviews for the NGO‐employees and questionnaire survey for the NGO‐beneficiaries simultaneously. Multi‐stage sampling as described by Saunders et al. (1997) was followed. The whole of Bangladesh is geographically divided into seven administrative divisions. From each division a single district from the selected district a single Thana; from Thana a union and from the selected union, five beneficiaries were randomly selected for the questionnaire survey (5X7=35). A drop and collect (e.g. in person) technique was applied as the respondents (the NGO beneficiaries) needed a detailed explanation about this study and questionnaire. Further, this technique (drop and collect) is supported by many researchers (Brown, 1987; Hair et al., 2007; Ibeh, Brock and Zhou, 2004). This technique is also fast, reliable, cheap and suitable to those who are having resource constraints (Brown, 1987).Out of listed 2445 NGOs (excluding the cancelled memberships), 14 NGOs (out of which (i) 7 large NGOs and (ii) 7 small NGOs) were purposefully selected for the interviews. In total, 54 employees consisting of their top, mid and lower levels based on salary grade (18 from each) were selected for the semi‐structured interviews. The field works of this research were carried out in Bangladesh. This research went for qualitative investigation (e.g. semi‐structured interviews) and quantitative study (e.g. survey questionnaires) simultaneously. Some scholars (Creswell, 1994; Creswell and Clark, 2007) lay emphasis on simultaneous and sequential triangulations. In the simultaneous triangulation, both qualitative and quantitative approaches are used equally and separately, and usually results are examined to cross validate the findings (Steckler et al., 1992; Creswell, 1994). The present study needed a detailed view of the topic under study and a solid validity. The researcher here studies the subject in the natural setting (Bangladeshi NGO scenario). The interviewees had the chance to explain their views and experiences on the subject matter, while the questionnaire respondents helped in finding the validity of the interview findings and vice versa. In research, it is the use of multiple but independent measures (Easterby‐ Smith et al., 2002) in a same phenomenon or programme (Patton, 1990) under study. More comprehensively, Denzin (1978) finds triangulation as “combination of methodologies in the study of same phenomenon” (p. 291). In fact, it is the art of employing one method to cross‐check the results of another (Jankowicz, 2005). Regarding the qualitative data analysis techniques, Jankowicz (2005) finds “the main technique associated with semi‐structured interviews is called content analysis” (p. 270). Now‐a‐days content analysis technique is popular with academics, commercial researchers and communication practitioners (Neuendrof, 2002). For this research, all interviews (recorded and interview notes) are transcribed. The research makes an endeavor to extract final themes which emerged from the study. This study borrowed the procedural guidelines to tabulate and present the content analysed data from Jankowicz (2005, p. 272‐73).The data categories are put as per the research issues of this study.

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Sheikh Shamim Hasnain Three copies of the coding sheet were prepared. To check the coding, two coders were requested. They were given the hard copy of the coding sheets. Firstly, this researcher coded, using the symbol tick (9) on a sheet, Coder A used the symbol star/cross (*/X) on a sheet and Coder B used circle (o) on a sheet. The coding sheets of Coders A and B were collected. Now the differences of Coder A from this researcher were transferred to the sheet of this researcher. Further, the differences of Coder B from this researcher were transferred to the sheet of this researcher. At this stage, both the coders’ percentage agreements with this researcher were calculated separately (e.g. Neuendrof, 2002) by adding up the number of cases that were coded in the same way by the two coders and dividing by the number of cases. [For example: Number of cases agreed=12. Total number of cases=16. So % agreed=12/16=75%]. For this study the coders had an excellent rate (e.g. above 90%) matching with this researcher. The minimum level of 80% is usually treated as normal (e.g. Riffe, Lacy and Fico, 1998). Here the rating is higher. So other researchers may also draw the similar conclusion.

4. Findings and discussion It is mentioned in the research methodology section that this study interviewed 54 NGO officials consisting of top, mid and lower levels based on salary grade (18 from each). Table 1 and Figure 1 exhibit the results of the interviews. Table‐1: The NGO employees’ opinions on the male and female knowledge absorption capacity (N=54)

No Comment

Above 55 Years

36‐55 Years

21‐35 Years

0‐20 Years

Above 55 Years

36‐55 Years

21‐35 Years

Numbe rs Percent ages (%)

0‐20 Years

Age Groups

Females

Total in favour of females

Males

Total in favour of males

7.40%

14.81%

1.85%

24.07%

16.67%

55.56%

72.22%

3.7%

7.40% NGO employees think that the 21‐35 age group of males is capable of absorbing more knowledge while 14.81% choose the 36‐55 years group and 1.85% are for the above 55 years group. 16.67% NGO employees believe that the 21‐35 age group of females is capable of absorbing more knowledge while 55.56% choose the 36‐55 years group and 0% the above 55 years group. Out of 54 interviewees, 13 (24.07%) consider that males’ absorption capacity is higher than that of females, while 39 (72.22%) interviewees opine that females’ absorption capacity is higher than males’ absorption capacity. In this regard an NGO official claims, “ Female clients absorb and implement the transferred knowledge better. Out of 100%, I think 85% females absorb and implement the transferred knowledge, while only 10% males are capable to do the same.” An NGO field worker finds, “Female clients are more active than those of the male clients. Female clients absorb and implement our transferred knowledge more. So female clients are getting the results of our knowledge. The success rate of female clients is higher than males. 70% female clients get the good results.” An Assistant Manager of an NGO observes, “If I talk about age, I find 36‐55 years old people try to receive more knowledge and also try to implement those. ….Among the males and females, I find females are more honest, sincere and trustworthy in absorbing and implementing the transferred knowledge. In maximum projects this

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Sheikh Shamim Hasnain thing was observed. The female clients are active and they want their social and economical developments.” A senior official of an NGO notes, “I have two training centres and I have huge experience on this issue [impact of sex on knowledge absorption]. In case of knowledge, in our country the female groups are sincere than those of the male groups. Our females have many potentialities than those of our males. We do not want to recognise it or we are ignorant about it. Let us take that we do not know it. I have twelve trades in Mollarkat [name of a place]. We have tailoring, sewing, block batik etc. At another centre we have nursing trades for the females. In that centre we have many trades for the females. It is seen that attendance rate in the female trades is above 90%. If you visit the male trades, you will find the attendance rate is 25%. Males do not want to attend the trades. In our country women are very sincere and can quickly absorb the lessons. Their sincerity and knowledge absorption capacity is almost double than that of the men. Women are very punctual. They listen to the instructions and the lessons very carefully. They also follow those. But the males do want not follow.”

Note: 1= % of interviewees in favour of males’ knowledge absorption capacity, 2=% of interviewees in favour of females’ knowledge absorption capacity, 3= % of no comments Figure 1: At a glance percentage of the interviewees’ opinions (the NGO employees) (N=54) About the age and knowledge absorption, an NGO‐field worker says, “Middle aged persons who are between 35‐55 years old they can absorb and implement knowledge properly. Females are sincere and they can absorption and implementation better than the males. They always want to return the instalments in time.” It is observed that no interviewee has supported age groups, 0‐20 years male or female. In this regard an interviewee reports, “….actually 0‐20 years clients are not matured enough to absorb knowledge. At the age of 20, you may expect some maturity from them. They tell us that they could receive our knowledge, but when you go to see their projects, it is very frustrating.” It is mentioned in the research methodology section that this study administered (5X7=) 35 questionnaires for the NGO beneficiaries covering the whole of Bangladesh. It followed multi‐stage sampling. Table 2 and Figure 2 exhibit the results of the questionnaire survey.

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Sheikh Shamim Hasnain Table 2: The beneficiaries’ opinions on the male and female knowledge absorption capacity (N=35)

0 0%

04 11.43 %

21 60%

0 0%

25 71.43 %

No Comment

09 25.71%

Above 55 Years

0 0%

36‐55 Years

07 35%

21‐35 Years

02 5.71%

0‐20 Years

0 0%

Above 55 Years

36‐55 Years

21‐35 Years

Numbers Percentag e (%)

0‐20 Years

Age Groups

Females

Total in favour of females

Males

Total in favour of males

01 2.86%

Note: 1= % of beneficiaries in favour of age group 0‐20 years, 2= % of beneficiaries in favour of age group 21‐ 35 years, 3= % of beneficiaries in favour of age group 36‐55 years, 4=% of beneficiaries in favour of age group above 55 years Figure 2: At a glance percentage of the interviewees’ opinions (the NGO employees) (N=35) The triangulation of the findings in respect of the opinions of NGO employees and the beneficiaries should exhibit a comprehensive picture (Table 3). A side‐by‐side study of the findings depicts that in both the cases, age group 36‐55 is highly considered by the NGO employees and the beneficiaries, while age group 0‐20 years was not at all supported by either NGO employees or the beneficiaries. Both NGO employees and their beneficiaries believe that female beneficiaries may absorb more knowledge than male beneficiaries. Table 3: Triangulation of results of opinions of NGO employees (N=54) and beneficiaries (N=35) on male and female knowledge absorption capacity Females

16.67%

55.56%

72.22 %

3.7 %

5.71%

35%

25.71%

11.43%

60%

71.43 %

2.8 6%

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Above 55 Years

24.07%

36‐55 Years

1.85%

21‐35 Years

14.81%

0‐20 Years

7.40%

Above 55 Years

36‐55 Years

NGO employees (%) Beneficiaries (%)

21‐35 Years

0‐20 Years

No Comment Total in favour of females

Males

Total in favour of males

Sheikh Shamim Hasnain Naderi, Abdullah and Aizan (2008) carried out empirical investigation on the differences between the intelligence level of male and female university students. They had a sample of 48 males and 105 females. They find that there is no significant difference between the intelligence level of the male and female students. Zaidi (2010) had similar results. Blinkhorn (2005) also finds that there is no IQ difference between the male and female university students. Geary, Saults, Liu and Hoard (2000) find “males show significantly higher mean scores on the arithmetical computations, arithmetical reasoning, and special cognition measures” (p. 337). In their study, Lynn and Kanazawa (2011) reveal that after the age of 16 years, males start to develop higher IQ than females. 7 and 11 year old girls have an IQ of 1 IQ point. At the age of 16, the boys get an IQ point of 1.8. But the girls do not. It may be noted that in maximum cases the literature reflects superior IQ and knowledge absorption capacity of the males over the females. However, the findings of this study are different from those studies. Here, it is empirically established that the female NGO beneficiaries have more knowledge absorption capacity than the males. The Bangladeshi NGO context is different as here the female NGO beneficiaries are more sincere than the males. Probably, the males do not like to receive and execute the knowledge of the NGOs like the women. Without empirical investigation, it would not be appropriate to comment that outside the NGOs, the women in Bangladesh possess more knowledge absorption capacity than men.

5. Conclusion and future research Many researchers have studied the relationship between the human information / knowledge retention capacity and demographic variables. However, there is hardly any Knowledge Management guru who has examined the impact of gender and age on the knowledge absorption capacity in the NGO context of Bangladesh. In their highly cited book on Knowledge Management, “Working Knowledge: How Organizations Manage What They Know”, Davenport and Prusak (2000) highlight the importance of knowledge absorption by the knowledge recipient. They continue by arguing that without absorption of knowledge by the knowledge recipients, knowledge transfer does not occur. However, unfortunately they could not also empirically investigate the issue. Similarly, the legendary book “Knowledge Creating Company” authored by Nonaka and Takeuchi (1995) ignored the issue. The NGO sector consists of three vital stakeholders, namely, the donors, the NGO itself (e.g. NGO employees) and the beneficiaries. The NGOs and their beneficiaries are the main actors in this sector and knowledge transfer mainly occurs between these two stakeholders. The beneficiaries’ successful absorption of knowledge indicates that the knowledge transfer was an effective one. If the transferred knowledge is not absorbed by the beneficiaries properly, it indicates that the entire NGO mission is a failure as the NGOs are created for the socio‐economic development of the beneficiaries, and there is no substitute for knowledge transfer for the socio‐economic development of the poor and the deprived. So it is imperative to know the impact of the demographical variables (especially, sex and age) on the knowledge absorption capacity of the beneficiaries. Bangladesh is selected as the context of this study as the highest number of NGOs is in operation here. Furthermore, the positive contribution of the NGOs to the socio‐ economic development of Bangladesh is highly documented. The study finds similar results from the interviews and the questionnaire survey. The triangulated result of the interviews and the questionnaire survey on the NGO beneficiaries shows that the age group of 36‐55 years belonging to females is capable of absorbing more knowledge than other groups (e.g. male groups: 0‐20 years, 21‐35 years, 36‐55 years, above 55 years; Female groups: 0‐20 years, 21‐35 years, above 55 years). Future researchers may also carry out similar study on (i) non‐listed NGOs in Bangladesh, (ii) other industries in Bangladesh, or (iii) any other country of the globe.

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Intellectual Capital in Developing Micro‐States: The Case of Caribbean SMEs Lennox Henry and David Watkins Southampton Solent University, Southampton, UK lennox.henry@solent.ac.uk Abstract: Most research into intellectual capital has focused on the developed world, with notable exceptions being Sri Lanka (Abeyesekera 2008), Malaysia (Goh and Lim, 2004; Tayles et al., 2007), and the hospitality industry in the Caribbean (Carrington 2009). Given the prevailing economic climate in many the major economies of the world there is a need to develop the potential of other markets and Micro‐states in the developing world are prime targets as they have historic ties with the developed world. Almost by definition, in Micro‐states the population of indigenous firms is dominated by SMEs and in many countries there is an extensively researched correlation between the strength of an SME sector and economic development, (although continuing debate about the causations). There is also much current interest in the links between intellectual capital and organisational performance. Out focus is thus to explore intellectual capital in Micro‐ states, to understand how it can be harnessed to improve the economy these areas and maximise any associated value in intra‐and extra regional networks. We report the findings of a quantitative study into the presence and understanding of intellectual capital within the Micro‐states of the English‐speaking Caribbean. A survey involving 1500 SMEs in the Caribbean with a response rate of 42% was conducted, the resulting data was analysed using IBM SPSS 20. The findings indicate both that exploitable intellectual capital is present in the SMEs of the Caribbean and that there is a link between intellectual capital and organisational performance. However, the development of methodologies and practices for the formal reporting and measurement of intellectual capital is at a rudimentary stage at best, and totally absent in most cases. There is therefore an excellent opportunity for knowledge regarding intellectual capital that has been developed in the accumulated world to be integrated within the organisational operations of these SMEs in the Caribbean; this can generate benefits at firm level with increased productivity, efficiency, profitability, and knowledge sharing and transfers. There are also benefits to be had at the state level where national economies can benefit from the productivity of the firms at a regional level with networking (relational capital), having the possibility of increased intra and extra regional activity. The possibilities of increased knowledge transfer, trade and international exposure through cooperation and collaboration could be substantial Keywords: intellectual capital, micro‐states, SMEs, CARICOM, Caribbean

1. Introduction The small size of Micro‐states makes them vulnerable both physically and in socio‐economic terms. Increasingly, they are confronted with potentially adverse environmental consequences through utilisation of their fragile natural resources for economic development. They are often faced with the typical dilemma associated with sustainable economic development ‐getting the balance right – with little room for manoeuvre. Primary production may be a major source of potential wealth for such nations, while pollution generated by exploitation of natural resources can result in irrecoverable losses in terms of tourist potential. Many studies of Micro‐states, especially those with a high proportion of islands, have focussed on the exploitation of natural resources, structural capital (Turvey 2007), the exploitation of human capital, issues of brain drain (Beine et al. 2008), and the often biased and one‐sided relationship with foreign entities. Micro‐ states in the developing world often have historic ties with the developed world, although such states now seek a more symbiotic relationship than in the past. Against this backdrop it is possible to see the potential for intellectual capital (IC) developed within these regions to add new perspectives on potential or emerging new commercial relationships, given that trading patterns in the modern era are increasingly knowledge driven and that this has permeated even traditional labour intensive areas such as agriculture and fishing. Most research into intellectual capital has focused on the developed world, with notable exceptions being Sri Lanka (Abeysekera 2008), Malaysia (Goh and Lim 2004; Tayles et al. 2007) and the hospitality industry in the Caribbean (Carrington 2009). Carrington argues that the extensively researched correlation between SMEs and economic development should be seen in the context of current research on linkages being drawn between intellectual capital and organisational performance. This suggests that it would be productive to explore the development and exploitation of intellectual capital in Micro‐states in order to understand how IC can be harnessed to improve the outcomes for these areas and maximise any associated value in intra‐and extra regional networking.

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2. Literature review ‘Intellectual capital’ is a term that is widely used among accountants and academics, and is now increasing being used by other business professionals (Beattie and Thomson 2010). Academic study of Intellectual Capital has been developing for many years. Mouritsen and Roslender (2009), Bontis (2001), Bezhani (2010), Stewart (1997), Sullivan (2000b) and Sveiby (1997) are among those who have described a range of uses for Intellectual Capital and the myriad of benefits that can be achieved from its effective exploitation. At the inception the focus of practitioners in the field of Intellectual Capital was ‘value creation’, with Stewart (1997), Sullivan (2000b) and Sveiby (1997) among those focusing on IC as creating value within the organisation. However when one critically reviews this approach one finds that the existing value of IC within the organisation was ignored and therefore omitted from the resulting models. Thus from the mid‐1990’s the focus shifted more towards ‘value extraction’ (Sullivan, 2000b; Lev and Zambon, 2003; Sullivan, 2000a). This led to an interest in appropriate reporting models, and ways to incorporate intellectual capital into these existing frameworks were explored. However no single method has been universally accepted and implemented, which has resulted in a fragmented approach to the use of Intellectual Capital models in reporting. The consequence of this is that organisation specific IC reports do not lend themselves readily to inter firm comparisons (Beattie and Thomson, 2007; Beattie and Sarah, 2010; De Pablos 2003 and Díez et al., 2010). But what, precisely, do these authors mean by Intellectual Capital? Stewart (1997) argued that Intellectual Capital is ‘collective brainpower’ that is difficult to identify and even more difficult to deploy effectively, but that the organisation which develops intellectual capital and exploits it effectively has the opportunity to gain a competitive advantage over other organisations that have failed in this regard, (Stewart 1997). It is in operationalising the concept of ‘collective brainpower’ in order to measure it that the real debate begins. Here, we will concur with Dumay (2009) who advocates a triadic descriptor of the components of IC as consisting of human capital (HC), structural capital (SC) and relational capital (RC). Human capital has long been identified as a critical strategic resource for new organisations. According to Schultz (1994) the term ‘human capital’ is a key element in improving an organisation’s assets and employees in order to increase productivity as well as sustain competitive advantage. Human capital encapsulates the training, education and other professional initiatives undertaken to increase the levels of knowledge, skills, abilities, values, and social value of an employee which will lead to the employee’s satisfaction and improved performance. This improvement will be transmitted to improved organisational performance, the employee will benefit from the process of learning and the organisation will have access to the skills and expertise relevant to their operation and the competitive environment in which it exists (Kianto et al., 2010; Steenkamp and Kashyap, 2010; Eisenhardt and Schoonhoven, 1990; Morris and Snell, 2011). There are many definitions of ‘structural capital’ and in some literature it is even referred to as ‘organisational capital’. Sullivan (2000b) states that structural capital is that which is left after employees go home for the night: processes, information systems, databases, patents and so on. This sees structural capital as organisational assets that can be owned and are entrenched in the structure of the organisation. An alternative view of structural capital restricts it to legal rights to ownership: technologies, inventions, data, publications, and processes that can be patented, copyrighted, or shielded by trade‐secret laws. This view is pre‐occupied with the legal status of ownership and the legal ramifications of infringement of the inherent rights of ownership of these assets. A broader view, taken by Benevene and Cortini (2010), Sáenz et al. (2009), Cleary (2009) and others sees structural capital as comprising strategy and culture, structures and systems, organisational routines and procedures – ‘assets’ that are often far more extensive and valuable than the codified ones. This definition focuses more on the organisational culture and structure as the assets that enable productivity and enhance the human capital. (Díez et al. 2010) suggest that structural capital comprises assets not directly related to the presence of employees ‐ including databases, customer lists, manuals, trademarks and organisational structures. This definition differs from the first in that its sees structural capital not as a separate entity but as a facilitator for human capital and thus the value of the structural capital is intrinsically linked to the productivity of the human capital (Perez 2003). The other component of Intellectual Capital that needs to be explored is ‘Relational Capital’. Relational capital is the knowledge embedded in the relationships with any stakeholder that influences the organisation’s life (do Rosário Cabrita and Vaz, 2005). ‘Customer capital’ and ‘external capital’ have been used synonymously with relational capital (Sveiby, 1997; Stewart, 1997; Sullivan, 2000b). However, these imply a restricted view of

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Lennox Henry and David Watkins relational capital. A modern view argues that relational capital should embrace all the relationships in which the organisation is involved, both external and internal. This obviously involves customers, suppliers and financiers among key external stakeholders, but important relationships can also exist as internal quasi‐ autonomous units. Thus the equation of relational capital as simply customer capital, or even as external capital, can disregard some significant elements of this component of Intellectual Capital which may be the dominant ones in some organisational designs. However, here our focus is on SMEs so the approach of Kianto et al. (2010), where relational capital refers to the ability of the organisation to interact in a positive manner with the external stakeholders, and in so doing maximise the wealth generation potential of the human and structural capital is appropriate. Of these, the relationship with the organisation’s intended customers is the most important element (Kianto et al., 2010).

3. What are micro‐states? A Micro‐state is a sovereign state having a very small population or very small land area. Micro‐states are distinct from micro nations, which are not recognized as sovereign states. There was a temptation to use Small Island developing states (SIDS) as the basis for this analysis as many of the countries in the Caribbean are indeed islands. However, the CARICOM criteria for describing the geography of the region under study focus in particular on language groups rather than physical features such as island versus mainland status. Here the focus is on English speaking states within CARICOM, and these include Guyana which is on the South American mainland and is therefore not an island. A balance between economy and environment underpins sustainable development. Turvey (2007) argues that economic development as a measure of human welfare is unsustainable in the presence of persistent deterioration in environmental and natural resource capital. Small islands and Micro‐states suffer a series of limitations as a result of their size and isolation; this is usually manifested in disadvantages in economic development. A shortage of natural resources and small domestic markets means that there is a great reliance on imports and that most of their production will have to be targeted on export markets. However, since this usually results in high transportation costs, and as a consequence higher selling prices, these territories are seen mainly as consumption markets (McElroy, 2002). It is therefore imperative that the governments of these territories are especially diligent in clearly identifying and exploiting any of the island’s characteristics that may represent a competitive advantage (Mehmet and Tahiroglu, 2002).

4. Intellectual capital in micro‐states Within the IC literature, Striukova et al. (2008) argue that the world’s economy continues to move away from the reliance on the tangible assets such as plant and machinery (the old economic capital) towards a one that is driven by the use of knowledge, technology, core competences and innovation. This view follows Cañibano et al. (2002) who cite examples relevan to development from numerous spheres of life ‐ from government services to previously labour intensive areas such as agriculture and fishing. Most research on IC has focused on companies and single organisations as their level of aggregation, but exceptions such as Bradley (1997a), Bradley (1997b), Bontis (2004), Daley (2001), and Lin and Edvinsson (2008) have argued that IC is also an import issue at the level of countries and regions in which they are situated. The importance of IC to Micro‐states is of even greater importance as these states are characterized, as we have argued above, by a shortage of tangible resources , many of which are sensitive to their exploitation. If such states could base their development on intangible assets they could conserve their natural resources. When IC is viewed from a national or regional perspective it can be defined as the territory’s ability to transform knowledge and intangible resources into wealth (Bradley, 1997a; Bradley, 1997b; Bontis, 2004; Daley, 2001; Lin and Edvinsson, 2008). This holds open the possibility of acting as a counterweight to the stress effects of rapid globalization, trade expansion, and technology diffusion which can lead to negative effects including pollution, economic disenfranchisement of the locals, and capital exodus (Turvey, 2007; Beine et al. 2008). The assessment of the IC of a nation requires the articulation of a comprehensive system of variables that helps uncover and manage that nation’s invisible wealth (Lin and Edvinsson, 2008). The measurement of intangible assets assists nations in analysing and benchmarking their competencies and capabilities, and such assessments can facilitate the adoption of policies and practices to promote holistic national development (Malhotra, 2003). Since most measurements of national IC analyse existing data at the input and output level

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Lennox Henry and David Watkins (Bounfour, 2003), the major problem lies in the lack of a comprehensive reference framework (Pomeda et al., 2002). It is therefore important to find a framework that is intrinsically liked to the economic performance of a nation in which IC can be explored, and here we build on the work of Lin and Edvinsson (2008) developed within a Nordic context and apply this in the context of Caribbean Micro‐states. Audretsch (2007) argues that SMEs are crucial to the economic development of countries and for sustainability and growth. As knowledge becomes a more important facet of production, knowledge spill‐overs and innovative new ideas have become more significant sources of economic growth. Entrepreneurship becomes more important as this serves as the key mechanism by which knowledge created in incumbent organisations becomes commercialised in new enterprises, thus contributing to economic growth, employment and the vitality of the overall economy. SMEs are generally the vehicle that facilitates these activities. SMEs provide two out of three of private sector jobs and contribute to more than half of the total value‐added created by businesses in the EU. They are responsible for wealth creation and economic growth, particularly through their key role in innovation (Wymenga et al., 2011). Cravo et al. (2010) further argues that SMEs are crucial to the economies of emerging economies; and most of the Caribbean economies can be broadly defined as emerging. Furthermore, within the Caribbean context, SME’s form the major economic activities as there are few larger multinational within this region. Many authors, including Henry (2013), have explored IC in SMEs. SMEs lend themselves as the ideal vehicle through which IC in Micro‐states can be investigated as they provide a link between economic activity and IC research that can be extrapolated to a national level

5. Methodology Bryman and Bell (2007) describe quantitative research as an approach that emphasises quantification in the collective collection and analysis of data. The primary positioning of quantitative research techniques are in the positivist epistemology and the ontological position of is one of objectivity. Within this framework, the approach is pragmatic (Johnson and Onwuegbuzie, 2004). A total of 245 items used in previous studies on IC was generated, with questions gathered from Bontis (1998), Sveiby (1997) and Stewart (1997). Some of the items were modified to reflect the Caribbean context of this study, and they were then grouped into the themes that had emerged during the literature review. The number of questions was reduced by removing duplicate questions that were soliciting the same or similar responses from the respondents. A 7‐point Likert scale was prepared. The literature on quantitative research methodology has shown that if the sampling procedure does not produce results that are representative of the population then there is no valid basis for generalisation of the results and finding beyond the members of that population from which responses were obtained (Thomas 2003; Bernard 2011; Creswell 2009). Telephone directories, Registrar of Companies, trade organisation databases, direct enumerations and specialist listing are potential sources of the sample frame that were identified. The telephone directories from the various countries in the Caribbean were combined with information from the government departments in the territory to provide the sampling frame for the research in the Caribbean. Scheaffer et al. (2011) and Tomaskovic‐Devey et al. (1994) support the use of the telephone directory as a sampling frame. They argue that this source yields a rich amount of data and because it is updated regularly the information is usually accurate. In some of the countries, like Trinidad and Tobago, Barbados and Jamaica, the information available from the government departments was of a high quality, rich nature providing information relating to the turnover, number of employees and other relevant information on the companies. In the other countries the information was more basic and it was necessary to undertake additional validation methods like speaking to nationals of the countries to ascertain the average size of the organisation. In addition, any respondents that did not fit the criteria for the study were eliminated post hoc prior to the full analysis. From the review of current literature and previous work conducted by the authors the following broad hypotheses were developed: H1 Intellectual Capital is present in SME’s in the Caribbean H2 Intellectual Capital impacts performance of SMEs in the Caribbean H3 National Culture impacts performance in SME’s in the Caribbean

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Lennox Henry and David Watkins Multiple Regression analysis was used to evaluate the relationship between the dependent and the independent variables. The stepwise regression technique was employed. In this technique selection of independent variables within the analysis is evaluated in a stepped approach, and at each step the variables already in the equation are evaluated for removal and those not in the equation are evaluated for entry. This process is repeated until there is no variable in the analysis that is eligible for entry or removal.

6. Findings and discussion The descriptive statistics in Table 1 summarise the independent variables that were included in the analysis. There were 518 valid cases that were included from the original response of 630 cases; 112 cases were excluded because they did not fit the criteria for the research, 43 cases declared more than 250 employees (therefore not SMEs) and 69 responses were incomplete. Table 1: Descriptive statistics

Table 2 shows the regression model summary corresponding to the analysis of the relationship between independent variables as identified in this research, and organisational performance as the dependent variable. When the independent variables were considered as predictors of intellectual capital it was shown that the SMEs exhibited the presence of intellectual capital in their operations, and that this has an impact on performance. It is important to point out that the Durbin‐Watson value of 2.013 shows that there is virtually no auto‐correlation among the variables. Table 2: Model summary

The ANOVA summary in Table 3 show that each of the 7 models in the multiple regression has a P value of less than 0.05, all being .000 and therefore the independent variables included in each of these models are significant in explaining the movements in the dependent variable. So, in an attempt to see the maximum number of variables in the model, model 7 was chosen. The adjusted R square is also greatest in model 7. This also contributed to its selection as the model of choice for this analysis

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Lennox Henry and David Watkins Table 3: ANOVA

Table 4: Coefficients

The analysis shows that of the measures of IC that were used as independent variables, Internal Knowledge sharing has the most significant effect on the performance of Caribbean SMEs with a β value of .188 and a P value of .000. It was found that Information Systems was the worst performing variable with a β value of .109 and a P value of .007, but this value was still significant because the P value is less than .05. When one revisits the hypotheses having completed the regression analysis it can be seen that, given the fact that the variables that were used to measure intellectual capital existed in the cases, and they were all significant in the model, there is no reason to reject the first hypothesis H1. As one might hope to find, Intellectual Capital is present in SMEs in the Caribbean. This confirms prior research by Carrington (2009) that has shown evidence that IC does exist within organisations within the Caribbean region. But there is no dedicated methodology for the management and reporting of intellectual capital in this region and sensemaking will have to be done to maximise the potential of intellectual capital as an enabling tool/strategy (Carrington 2009).

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Lennox Henry and David Watkins The other findings go beyond Carrington. The chosen regression model indicated that the measures of intellectual capital chosen are significant in determining performance in the SMEs, and that therefore the second hypothesis H2 is also accepted. The final hypothesis in this research is H3: that National culture impacts performance of SMEs in the Caribbean. National Culture was one of the independent variables used in the regression model and with a β value of .122 and a P value of .007 it was the second weakest of the contributors in the model. However, its contribution in the model was still significant with a P <0.05 and therefore there is no reason to reject this hypothesis. So the analysis has led to all three hypotheses being accepted.

7. Conclusions This research aimed to assess the impact of intellectual capital in developing Micro‐states with the Caribbean as the area of interest. SMEs were used as the vehicle for examining this phenomenon, within the target population. As illustrated in the literature review SMEs are seen as drivers of economic activity within nations. This has been widely researched and accepted within many developing countries; so there was no reason to assume any difference within developing Micro‐states. The findings of this research enable us to relate organisational performance to intellectual capital by examining the dimensions of the independent variables. Analysing the results has given some interesting insights, as well as raising new questions; for example, why are information systems not more significant in determining performance? And will higher levels of intellectual capital management within an organisation lead to greater productivity? The existing literature on National Intellectual Capital has usually taken a macro prospective and is biased towards developed economies; (Lin and Edvinsson 2008), with a few notable exceptions (Abeysekera 2008) (Goh and Lim 2004); (Tayles et al. 2007), and (Carrington 2009). As indicated by the results above, intellectual capital also exists within developing countries, but prior research has indicated that structural capital is usually deficient within these countries. However, human capital has the ability to compensate for any shortcomings that may exist because of the lack of resources and organisational assets. This was evident with internal knowledge sharing being the most significant predictor variable in the model. Globalisation has led to many of the brightest individuals (human capital) seeking opportunities outside of the Micro‐states. This has hindered, and continues to hinder, the development of Micro‐states and also skews the relationship with international parties and partners against the developing Micro‐states. National culture plays an important part in defining the intellectual capital of any organisation, with the structures within the organisation being driven by the socialisation of its members, and the prevailing attitudes, norms and values of those within the management structure being shaped by this. From the regression analysis presented, it was shown that National Culture affects performance of the organisation investigated and therefore it has an underlying effect on the understanding and utilisation of an Intellectual Capital management strategy within these organisations. Intellectual Capital, like any other knowledge asset, has the ability to transform the fortunes of these regions if they can learn to exploit it. The organisations that view the components of intellectual capital as important assets within the firm have shown the propensity for greater financial performance. One can conclude that because intellectual capital has been shown to affect the performance of organisations (SMEs) within the developing Micro‐states, and it was shown that SMEs are the drivers of economic activities within nations, then intellectual capital is present within the developing Micro‐states and the development, understanding, and exploitation of intellectual capital can provide developing Micro‐states with a basis for economic development and possible competitive advantage in a global marketplace Our focus in this paper has been on SMEs, in view of their relative importance to indigenous development within Micro‐states. However, one way in which our research has the potential to be extended is to include larger organisations (often MNC components) and governments within the Micro‐states to determine the desire to adopt Intellectual Capital Management as a strategy for development and to explore the possibilities of harmonising the reporting model at a national or even regional level.

References Abeysekera, I., 2008. Intellectual capital disclosure trends: Singapore and Sri Lanka. Journal of Intellectual Capital, 9(4), 723‐737 Audretsch, D.B., 2007. Entrepreneurship capital and economic growth. Oxford Review of Economic Policy, 23(1), 63‐78

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Lennox Henry and David Watkins Beattie, V. And S. Thomson, 2010. Intellectual capital reporting: academic utopia or corporate reality in a brave new world? Edinburgh: Institute of Chartered Accountants of Scotland (ICAS) Beattie, V. And S.J. Thomson, 2007. Lifting the lid on the use of content analysis to investigate intellectual capital disclosures. Accounting Forum . 31(2) 129‐163 Beattie, V. And S.J. Smith, 2010. Human capital, value creation and disclosure. Journal of Human Resource Costing & Accounting, 14(4), 262‐285 Beine, M., F. Docquier And H. Rapoport, 2008. Brain drain and human capital formation in developing countries: Winners and losers. The Economic Journal, 118(528), 631‐652 Bene vene, P. And M. Cortini, 2010. Interaction between structural capital and human capital in Italian NPOs: Leadership, organizational culture and human resource management. Journal of Intellectual Capital, 11(2), 123‐139 Bezha ni, I., 2010. Intellectual capital reporting at UK universities. Journal of Intellectual Capital, 11(2), 179 – 207 Bernard, H. R. 2011. Research methods in anthropology: Qualitative and quantitative approaches Lanham, MD: AltaMira Press Bontis, N., 2004. National intellectual capital index: a United Nations initiative for the Arab region. Journal of Intellectual Capital, 5(1), 13‐39 Bontis, N., 2001. Assessing knowledge assets: a review of the models used to measure intellectual capital. International Journal of Management Reviews, 3(1), 41‐60 Bradl ey, K., 1997a. Intellectual capital and the new wealth of nations. Business Strategy Review, 8(1), 53‐62 Bradley, K., 1997b. Intellectual capital and the new wealth of nations II. Business Strategy Review, 8(4), 33‐44 Brym an, A. And E. Bell, 2007. Business Research Methods Oxford: OUP Cañibano, L. et al., 2002. Guidelines for managing and reporting on intangibles: Intellectual Capital Report. Directrices para la Gestión y Difusión de Información sobre Intangibles (Informe de Capital Intelectual), Vodafone Foundation, Madrid Carrington, D.A., 2009. A study of intellectual capital in the hospitality industry in the Caribbean, Unpublished PhD Thesis, University of Hull Cleary, P., 2009. Exploring the relationship between management accounting and structural capital in a knowledge‐ intensive sector. Journal of Intellectual Capital, 10(1), 37‐52 Cravo , T.A., A. Gourlay And B. Becker, 2010. SMEs and regional economic growth in Brazil. Small Business Economics, 38(2), pages 217‐230 Cresw ell, J.W., 2009. Research design: Qualitative, Quantitative, and Mixed Methods Approaches Thousand Oaks, CA: Sage Daley, J., 2001. The intangible economy and Australia. Australian Journal of Management, 26(Special Issue), 3‐19 De Pa blos, P.O., 2003. Intellectual capital reporting in Spain: a comparative view. Journal of Intellectual Capital, 4(1), 61‐81 Díez, J.M. et al., 2010. Intellectual capital and value creation in Spanish firms. Journal of Intellectual Capital, 11(3), 348 ‐ 367 Do Rosário Cabrita, M. And J.L. Vaz, 2005. Intellectual Capital and Value Creation: Evidence from the Portuguese Banking Industry. Electronic Journal of Knowledge Management, 4(1), 11‐20 Dumay, J.C., 2009. Reflective discourse about intellectual capital: research and practice. Journal of Intellectual Capital, 10(4), 489‐503 Eisenhardt, K.M. And C.B. Schoonhoven, 1990. Organizational growth: Linking founding team, strategy, environment, and growth among US semiconductor ventures, 1978‐1988. Administrative Science Quarterly, 35(3), 504‐529 Goh, P.C. And K.P. Lim, 2004. Disclosing intellectual capital in company annual reports: evidence from Malaysia. Journal of Intellectual Capital, 5(3), 500‐510 Henry , L. 2013 Intellectual capital in a recession: evidence from UK SMEs. Journal of Intellectual Capital, 14(1), 84 ‐ 101 Johnson, R.B. And A.J. Onwuegbuzie, 2004. Mixed methods research: A research paradigm whose time has come. Educational researcher, 33(7), 14‐26 Kianto, A., P. Hurmelinna‐Laukkanen And P. Ritala, 2010. Intellectual capital in service‐ and product‐oriented companies. Journal of Intellectual Capital, 11(3), 305 ‐ 325 Lev, B. And S. Zambon, 2003. Intangibles and intellectual capital: an introduction to a special issue. European Accounting Review, 12(4), 597‐603 LIN, C .Y.Y. And L. EDVINSSON, 2008. National intellectual capital: comparison of the Nordic countries. Journal of Intellectual Capital, 9(4), 525‐545 Malh otra, Y. 2003. Managing and measuring knowledge assets in the public sector Working Paper, Syracuse University Mcelroy, J. L. 2002. The impact of tourism in small islands: a global comparison.In F. di Castri and V. Balaji (eds) Tourism, Biodiversity and Information. Leiden: Backhuys Publishers, , 151‐167 Mehmet, O. And M. Tahiroglu, 2002. Growth and equity in microstates: Does size matter in development? International Journal of Social Economics, 29(1/2), 152‐162 Morris, S.S. And S.A. Snell, 2011. Intellectual capital configurations and organizational capability: An empirical examination of human resource subunits in the multinational enterprise. Journal of International Business Studies, 42 (6): 805‐827 Mour itsen, J. And R. Roslender, 2009. Critical intellectual capital. Critical Perspectives on Accounting, 20(7), 801‐803 Perez, P., 2003. Structural capital candidate definitions [online] [viewed 02/15 2011]. Available from: http://www.knowledgeboard.com/item/466/23/5/3 Pomeda, J.R. et al., 2002. Towards an intellectual capital report of Madrid: new insights and developments. Transparent Enterprise.The Value of Intangibles, Madrid, November, 25‐26

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Towards a Model for Measuring University Sustainability Raine Isaksson1, Mikael Johnson2 and Rickard Garvare3 1 Gotland University, Humanities and social Sciences, Visby, Sweden 2 Karlstad University, Service research Center – CTF, Karlstad Sweden 3 Luleå University of Technology, Sweden Raine.isaksson@hgo.se Mikael.Johnson@kau.se

Rickard.Garvare@ltu.se Abstract: The multitude of challenges related to sustainable development require, not only a shift in mind‐set but also high competence in most sectors of employment. But how could we know if a university education is going to provide necessary competence in sustainable development? A model being developed to measure university sustainability is the Assessment Instrument of Sustainability in Higher Education (AISHE). Using the logic of self‐assessment and based on the Triple Bottom Line this model deals with operations, education, research, interaction with society and core values with a so called identity module. The model makes an operationalization of sustainable development and its structure should be usable for constructing a quick assessment system similar to those of many business excellence models. Finding out the level of university sustainability is clearly not very easy for presumptive students. Furthermore, we can assume that since being sustainable is politically correct there is a risk of “sustainability washing” of information provided. Current university ranking systems do not seem to correspond well with how universities are working with sustainable development. The research question is if the AISHE‐model could be converted into a credible quick assessment tool by relying on information provided by the university web‐site. For this to work the university needs to have a culture that promotes transparency. With the rapid development of information technology it could be expected that more and more countries will have the conditions for using web‐sites for providing the necessary information. Swedish university web‐sites are used for testing the model. Sweden has a high level of transparency and is therefore thought to form a suitable example. This paper deals with conceptual development of the assessment model. Further studies will be carried out to validate the model. Results indicate that a structured web‐site analysis can be used to quantify information that is organised according to chosen parts of the AISHE‐model. The first results indicate that Swedish universities still have a long way to go in becoming sustainable. Keywords: university sustainability, performance assessment, AISHE, model development

1. Introduction We are in the UN decade of Education for Sustainable Development (ESD) (2005‐2014). A Swedish University law from 2006, requiring that all levels of education include sustainable development, is a reflection of this. It has been recognised that education, including university level education, plays a key role in promoting global sustainable development. Brewer et al. (2008) state that: “An understanding of sustainability issues should be a key component of degree programmes. It is widely regarded as being a central attribute to professional practice and responsible global citizenship, …”. Striving for true sustainable development (Sibbel, 2009; Gray, 2009) triggers a multitude of challenges. It requires not only a considerable and widespread shift in mind‐set but also high competence in most sectors of employment (OECD, 2001). This necessitates extensive and excellent education in matters related to sustainable development. According to Holdsworth et al. (2008) a new way of educating students for sustainability is required that empowers them with capabilities and skills to seek out and examine their own frameworks for thinking. This involves changes to curriculum and teaching practice. With the decade approaching its end, it is of interest to know how the focus on ESD works. Here, we are particularly interested in university education. How could we know if university education of today provides adequate competence to tackle the global challenges of sustainable development? Finding out how universities work with sustainability and how they prepare students for the challenges is clearly not easy for presumptive students. Issues of sustainability are uncommon as a basis for university ranking, although there might be some correlations between existing ranking systems and measures of sustainability (Lukman et al. 2009). Since being sustainable is politically correct, there is a risk of “sustainability washing” of information provided (Maranto et al., 2009; Sharma and Starik, 2004). As implied by Roberts (2001) it is possible to find essentially narcissistic explanations to this occurrence. Universities may want and claim to provide their students with the required competence, but somehow the engagement gets diluted. To move away from this we need robust, yet simple assessment models that measure and value (Onisto, 1999) universities Triple Bottom Line (Elkington, 1998) performance. Shriberg (2002: 255‐257) reflects upon the attributes of an ideal

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Raine Isaksson, Mikael Johnson and Rickard Garvare cross‐institutional sustainability assessment tool and concludes that it should (1) Identify important issues, (2) Be calculable and comparable, (3) Move beyond eco‐efficiency, (4) Measure processes and motivations, and (5) Stress comprehensibility. A quick scanning of the literature reveals many different models for sustainability in higher education: State of the campus environment, Sustainability assessment questionnaire, Auditing instrument for sustainability in higher education, Environmental report and workbook, Greening campuses, Campus ecology, Environmental performance survey, Indicator snapshot/Guide, Grey pinstripes with green ties, EMS self‐assessment, Campus sustainability assessment review project, Sustainable pathways toolkit and others (for summaries see, for instance: Shriberg, 2004; Cole, 2003). Most certainly there are several more models. Most of them seem to start with the prerequisite that the university decides to begin disclosing its sustainability performance. During the literature review and database searches for this paper it has become obvious that there are many universities interested in reporting their sustainability performance. According to a previous research project on Swedish universities’ sustainability performance only a minority could be found work with sustainable development in any higher extent when assessing the performance based on a Triple Bottom Line approach (Isaksson and Johnson, 2010 and 2011). Global sustainable development will require some major changes in the near future. This implies that universities probably have to change their syllabi drastically to include issues relevant for this change. For the student interested in sustainable development it is important to know how universities work with sustainable development. The models for sustainability assessment listed above provide little support for external assessment as they all assume the universities to be the issuers of the reports. Also, many of the models do not have an easily detectable focus on the Triple Bottom Line, but often work with the much more narrow concept of campus greening. If we look at this from a perspective of a responsible and caring student, an issue when deciding to apply to a university is: can this education provide the competence needed to make substantial contributions in tackling the global challenges of sustainable development? We believe that an assessment tool for how universities work with sustainable development based on an external review is needed. The objective is to be able to assess to what extent universities provide society with the competence development needed to deal with challenges of sustainable development. The Auditing Instrument for Sustainability in Higher Education, AISHE (Roorda, 2001), has been pointed out as being a process‐oriented, flexible framework for institutional comparisons, that helps to prioritize and set goals through developmental stages. An identified weakness of this model is that it is relatively difficult to comprehend (Shriberg, 2004). Using the logic of self‐assessment and based on the Triple Bottom Line of economic prosperity, social equity and environmental protection the AISHE model deals with operations, education, research, interaction with society and the organisational core values. It makes an operationalization of the fundamentals of sustainable development based on the assumption that working with sustainable development is about how things are done – operations – and what is done in the core business – education, research and interaction with society – all part of the mission of Swedish universities. This cannot be done unless there is a clear management commitment and a culture for sustainable development – identity. These characteristics differentiate AISHE from many of the other models, which often deal mostly with campus greening or operations. The AISHE model is intended to be a methodology for self‐assessment and later also for external certification. However, self‐assessments based on consensus decisions are not only internal and subjective but also often quite time demanding exercises. In its present form, the AISHE model is not suitable for external assessments. However, using the operationalization with the five AISHE modules could lend itself as a base for external assessment. The assumption is that stages for the different criteria of the modules can be defined by means of a web analysis. For this to work, the university needs to have a culture that promotes transparency and it should also have a high level of digitalisation. With the rapid development of information technology it could be expected that more and more countries will have the conditions for using web‐sites in providing the necessary information. Sweden has a strong culture of transparency of both public and private organisations concerning a variety of information. Since Sweden also has the law requiring universities to work with sustainable development we have chosen Sweden and Swedish university web‐sites for testing the proposed model. The research question is if the AISHE‐model could be converted into a credible quick assessment tool by relying on information provided by the university web‐site.

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2. Theory background Assessing organizational development can be done using business excellence models such as the Baldrige Criteria for Performance Excellence (NIST, 2012). The methodology rests on self‐assessment of enablers and results and the performance is measured both based on output (results) and resources (enablers). The assessed performance is compared to an ideal performance described in the model. A similar logic is used in the Assessment Instrument for Sustainability in Higher Education (Roorda et al, 2009). This instrument is still in a testing stage but provides a helpful categorisation of different components of university sustainability, see Figure 1.

Figure 1: The evaluation structure from the draft AISHE 2 assessment instrument with five modules leading to an evaluation (Roorda et al., 2009) The AISHE2‐instrument suggests dividing the assessment of university sustainability in the five modules of operations, education, research, society and identity. The operations module is used to describe how the university campus and premises work in terms of sustainability. Then, there are the three main processes of carrying out education, doing research and interacting with society. The Identity module, which might be considered as the foundation of the assessment, contains elements of vision & policy, leadership, communication, expertise, coherence and transparency & accountability. The Identity module could be seen to define the core values (expressed by vision & policy) and also other types of abstract enablers or resources. An important part of the Management resource is how the policies and goals have been formulated. The vision & policy criterion from AISHE 2 states: “The organization has a vision on sustainable development and on corporate social responsibility in general, on aspects within the own fields of expertise and on the consequences of this for the organization policy. The vision is expressed in the policy. This policy translates the vision in concrete plans for action. Goals are formulated, and activities are designed aiming to realize these goals.” (Roorda et al, 2009). The model in Figure 1 could present us with a position for a university studied. The benchmark would be a full rating for the criteria presented.

3. Building a measurement model Each of the five AISHE‐modules in Figure 1 consists of six criteria, see Figure 2. Out of the five elements in Figure 2, we have chosen Identity, Operations, Education and Society to form a first assessment model. The identity module underpins it all and could be seen as a precondition for the other modules. Operations relate to how university premises are managed, something which might have an important symbolic value. In a sustainability context education should be related to society, which is therefore included. Even if research could be of importance the first priority for a new student is most probably education. We have therefore excluded the Research module in our study. In this paper we demonstrate translation of the criteria questions to be used for a web‐analysis using the Identity module. In further work we will do the same with criteria for the other chosen modules.

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Certification Reporting

AISHE 2.0 CHECK

PLAN

Operations

Education

Research

Society

Quality Assessment

Output Assessment

Impact Assessment

Humanity

Interdiscipl. Integration

Connecting

Ecology

Thematic Integration

Thematic Involvement

Economy

Awareness & Basics

Awareness & Learning

Physical Structure

Methodology

Goals

ACT

Identity CHECK

Transparency & Accountability Coherence

Expertise

ACT

Communication Leadership PLAN

Vision & Policy

Figure 2: Criteria in the AISHE2 proposal (Roorda et al., 2009) The chosen modules in Table 1 represent criteria, which we believe will be sufficient to form an adequate picture of the performance. These criteria should also be accessed readily in a web analysis. Table 1: Chosen AISHE 2 elements and criteria Identity

I‐1. Vision & Policy

I‐2. Leadership

I‐3. Communication

I‐4. Expertise

Operations

O‐1. Goals

O‐2. Physical structure

Education

E‐1. Goals

E‐2. Methodology

E‐3. Awareness & Basics

E‐6. Output assessment

Society

S‐1. Goals

S‐2. Methodology

S‐3. Awareness & Basics

S‐6. Impact assessment

Each criterion is quantified by integer values, so called stages, from 0 to 5. The criteria are cumulative and all previous ones must have been achieved to merit a higher rating. According to the AISHE2 proposal ratings are set by the use of consensus discussions with persons concerned and with the help of an external facilitator. However, the module criteria could also be used for a web analysis quantifying sustainability performance. We propose to test using 14 out of the 24 criteria for the chosen four

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Raine Isaksson, Mikael Johnson and Rickard Garvare modules. It could be argued that with almost 60% of the criteria covered for the chosen elements a first indication should be possible to achieve. The four chosen elements should provide sufficient information for an overview of the university sustainability work. Identity is a core element and operations have an important symbolic role. Research on sustainable development should also be seen in the education carried out. We therefore believe that, as a starting point, rating Identity, Operations, Education and Society should be suitable to make a quick assessment of university sustainability. Table 2: AISHE2 stages partly renamed. Stage 0

AISHE2 name Not started or not displayed

Proposed name Not interested

1 2

Activity oriented Process oriented

System oriented

Organisation oriented

4 5

Chain oriented Society oriented

Network oriented Society oriented

Comment Not demonstrating any detectable interest in sustainable development is considered equal to not doing anything Activities are parts of processes Processes are parts of the organisational system Organisations are part of the supply networks Supply networks are parts of society This is the highest system level – a system approach can be applied at all levels starting from processes

4. Quick assessment model for web analysis One important assumption is that a university that is interested in sustainable development and in carrying out Education on Sustainable Development (ESD) makes information of this available. The consequence is that if, for example, a sustainability policy cannot be found then the rating corresponds to no policy. In Table 3 the chosen four criteria of the Identity module are explained, based on descriptions of the AISHE2‐draft (our remarks). Table 3: The chosen four criteria of the Identity module with overall description and our remarks Identity

Description

Remarks

The organization has a vision on sustainable Management commitment is a precondition for development based on the Triple Bottom Line. The any major change. The commitment is I‐1. Vision & vision is expressed in the policy. This policy expressed in policy and objectives and then Policy translates the vision in concrete plans for action. followed up in yearly reports. Absence of policy Goals are formulated, and activities are designed indicates lacking management commitment. aiming to realize these goals. The management is not only formally responsible This might be checked by looking at what the for the integration of sustainable development in rector (university president) communicates. If the organization vision and activities. It also takes there is no mention of sustainable development leadership for it, i.e.: it shows personal involvement. then there probably is no sustainable I‐2. Leader‐ It inspires the staff, the students and possibly the development. ship other stakeholders. It listens actively to them, knows and uses their ideas and opinions, and asks feedback about its functioning. Thus, it uses its authority in a genuinely participatory way.

I‐3. Commu‐ nication

Communication about sustainable development in A university interested in sustainable relation to the organization takes place, both within development could be expected to the organization and with the outside world. The communicate this clearly. Even a university communication is used to strengthen the understanding that there could be some organization vision about sustainable development, positive branding in being seen to work with to develop new initiatives, and to inform and get sustainability communicates the little that is feedback from all sorts of stakeholders, e.g. staff, there. A university having no visible students, the professional field and other direct communication has arguably not recognized the stakeholders, and society in general. issue or considers it as irrelevant.

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Raine Isaksson, Mikael Johnson and Rickard Garvare Identity

Description

Remarks

I‐4. Expertise

The expertise available to the organization about sustainable development is kept up‐to‐date and is sufficient to enable to work actively on the integration and improvement of sustainable development in the vision and the activities of the organization. Partly, this expertise is available within the organization staff. Besides, an external network is functioning in order to utilize the expertise available in the outside world.

If there are no signs of anybody presenting themselves as interested and with some competence in the area, and there are no links to competence, then the university probably has little competence in sustainable development

In Table 4 the chosen four criteria of the identity module are presented. The text has been shortened and there are some additions in order to increase the accuracy of the web‐analysis. One example is that if the policy is not found within five clicks from starting then it is considered as not demonstrated. The test is to emulate interested students and we have assumed that there is a limit in the patience for looking after sustainability information.

5. Pilot testing A list of the chosen four criteria in the Identity module, see also Table 4, has been used to test four Swedish universities. Two benchmark universities have been selected together with two others not known for any particular direction towards sustainable development. This is in order to get a range for the assessment. Gothenburg University (GU) has been chosen due to their profile of having a sustainability focus. They were the second university in Sweden to become accredited for ISO 14001. Also they have for a few years been reporting according to the Global Reporting Initiative (GRI) guidelines. The GRI are used as one of the main instruments for companies to do their sustainability reports. The second benchmark chosen is University of Gävle (UOG) that has appointed a vice rector in sustainable development active in the Swedish network for sustainable universities. The University C is on the lower part of the ranking of Swedish universities (URANK that includes 29 universities) and has no acknowledged profile within sustainable development. University D is a Swedish prestige university and is among the highest ranked. The purpose of this pilot testing is to have a first understanding of if the methodology works and to get some indication of the level of sustainable development in Swedish universities. Table 4: Identity module with four criteria in six stages shortened and modified from original SD= Sustainable development

I‐1. Vision & Policy

Id.

0 ‐ Non existing or not demon‐ strated Policy not found using basic logic and within five clicks

1 ‐ Activity oriented The management has a vision on SD and CSR related to activities of the organization. There is an SD‐ policy. It could be integrated into an environmental policy. It is explained and relates to some common definitions such as Brundtland and The Triple Bottom Line (TBL).

4 ‐ Supply network oriented There is a There are The organization specific assessable goals is recognized by sustainability that can be its direct policy. It is identified based stakeholders as backed up by on the TBL, a key player for strategy and which are SD. This is plans and shows presented in backed up by how staff is separate credible external involved. documents. The statements. organization visions itself as a key player. Sustainability vision is found in mission statement. 2 ‐ Process oriented

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3 ‐ Organization oriented

5 ‐ Society oriented Within society at large, the organization is recognized as a leading key player for SD, acting proactively on a level of systemic change.

Raine Isaksson, Mikael Johnson and Rickard Garvare

I‐3. Communication

I‐2. Leadership

Id.

0 ‐ Non existing 4 ‐ Supply 1 ‐ Activity 2 ‐ Process 3 ‐ Organization 5 ‐ Society or not demon‐ network oriented oriented oriented oriented strated oriented No clear Occasionally, the Regular SD‐ The rector Based on a The commitment management communication systematically visible personal management, encouraging pays attention to from rector. and visibly commitment, staff and employees to and appreciates Dialogue with encourages the students work with SD ‐ activities by staff personnel found. active management, together Search within members participation and staff and stimulate and "On university concerning SD. shared students realize a pro‐ and SD leads to Occasional responsibility of together support active and no hits participation of staff and and expand excellent role of explaining how rector in issues students in relations with the organization the university promoting SD. development direct within society works with SD and stakeholders and and the improvement of with centres of educational and the organization expertise, with professional vision, strategy, the explicit aim field. policy, activities of strengthening and result the process of assessments. integration of SD into the organization. No communi‐ Efforts of cation to staff or individual students on SD members of the found. staff or of parts of the organization to enlarge the attention for sustainability take place. Staff and students informed on ad hoc basis.

The communica‐ tion about SD is based on a structured plan. The staff and the management are well‐ informed about each other’s opinions and aspirations concerning SD. Courses are clearly marked for sd‐content. Search on SD in Swedish and English results in minimum 80% relevant hits out of first 10. Annual report includes defined KPIs on SD showing trends.

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The SD‐ commitment is obvious from the first page. There is a structured communica‐tion plan for SD. Staff is well informed. Search on SD in Swedish and English results in minimum 80% relevant hits out of first 10. Annual report includes defined KPIs on SD showing trends.

The direct stakeholders are actively and visibly involved in communication about SD. Publications by staff members and/or students with a clear relation to sustainability appear regularly in scientific journals or public media.

A wide variety of societal actors is involved in the communication about sustainability. Publications by staff members and/or students are leading.

Raine Isaksson, Mikael Johnson and Rickard Garvare 0 ‐ Non existing or not demon‐ strated No expertise found.

1 ‐ Activity oriented Staff development in sustainability depends on individual initiatives.

I‐4. Expertise

Id.

2 ‐ Process oriented

3 ‐ Organization oriented

There is a staff There is a development systematic staff plan, which is development mainly short plan. For this term. SD‐ goal there is an information is external regularly network, which presented to is maintained. staff. There is an The SD council identifiable has a adequate structure for budget and can work with SD employ some council or people at least similar. part time to work with SD as well as finance internal projects.

4 ‐ Supply 5 ‐ Society network oriented oriented The regular The organization contacts with is or has an the external (inter)‐nationally network not recognized only contribute centre of to the expertise expertise within the concerning SD. organization, but also to the expertise of the network partners.

Table 5: Identity module scored using four criteria and an assessment scale 0‐5 University

University D

University C

UOG

Average

I‐1. Vision & Policy

1.3

I‐2. Leadership

0.8

I‐3. Communication

1.5

I‐4. Expertise

1.3

Average

0.0

0.5

2.8

1.5

1.2

6. Analysis, discussion and conclusions The average of 1.2 out of a maximum of 5 is low. Even though the sample is small it is still indicative. The average could in reality be even lower since the sample contains two benchmarks. On the other hand the choice of universities was not random and the lower scoring universities were chosen due to no known sustainability profile. The 0 scored by University D could be due to lack of transparency. Most Swedish universities provide access to guiding documents such as policies and to results in the form of yearly reports. This is not the case for University D. It could be that they do not qualify for the assumption of transparency. This would mean that they cannot be analysed using a web analysis. The result should therefore be treated with caution. Despite being considered a leader in sustainable development GU only scored 1.5/5. A main reason for this is that sustainable development is only defined in an environmental policy. The focus on environment as in comparison to the entire Triple Bottom Line can be detected in other documents that mostly give priority to environmental issues without any definite focus on the broader issue of sustainable development. The communication from the rector and the general information from the web site are not clearly conveying the message of importance of sustainable development. UOG receives the highest score. On their web‐site from the opening site there is information on sustainable development. Information on policies, goals and results are easy to find. The questions at different stages for the criteria still need to be specified and probably further modified form the original. It is found to be difficult to exactly define a stage without ambiguity since web sites are differently built and the access to information is partly a function of skills. There is some measurement variability. This variability can be reduced, by letting several researchers independently do the analysis. This is planned for in the continued research where the modules of operations, education and society will be studied. Also, it might be an advantage to expand the scale to include more steps, especially since many universities currently could be in the range of 0‐2 of the current scale of 0‐5. This should improve differentiation. The first indication is that the AISHE construct can be used for assessment of university sustainability. We will therefore continue the planned research and development of the assessment tool.

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Raine Isaksson, Mikael Johnson and Rickard Garvare In spite of a limited number of criteria and a small sample size the indication is that Swedish universities still have a long way to go in becoming sustainable, when using the AISHE based benchmark as reference. More than twenty years have elapsed since the Rio Conference of 1992 introducing Agenda 21. The Swedish law, requiring the introduction of sustainable development in universities, has been around for more than six years. The legislation and policy backup are there to support universities in their sustainability work. Still, it seems that not much has happened with the average Swedish university. The indication is that both the current performance in sustainable development and the speed of change are low. A question is whether this indication can be generalised or not. Sweden maintains a relatively high profile in promoting sustainable development and is among the best‐educated countries in the world. This might imply that universities of today generally are not preparing students for the forthcoming challenges of non‐sustainable practises. Mayor changes are needed, starting now. The consultancy company PWC reports in November 2012, that it is unlikely that global temperature can be kept within the “safe” +2C limit. The rate of carbon reduction has been much too low. According to PWC scenarios of +4 and +6 degrees C are now being assessed. This is only one of many urgent areas where new solutions and new thinking is needed. Those graduating today are those that should solve the problems. Currently it seems they have to do it with an education that is mostly business as usual.

References Brewer, G., Gajendran, T., Landorf, C. and Williams, T. (2008). Educating for urban sustainability: a transdisciplinary approach. Engineering Sustainability Vol. 161, Issue ES3, pp. 185‐193. Cole, L. (2003) Assessing sustainability on Canadian university campuses: Development of a campus sustainability assessment framework, Royal Roads University, Canada. Elkington, J. (1998). Cannibals with forks – The triple bottom line of the 21st Century Business, New Society Publisher, Canada. Gray, R. (2009). Is accounting for sustainability actually accounting for sustainability…and how would we know? An exploration of narratives of organisations and the planet. Accounting, Organizations and Society. Vol. 35, pp. 47‐62. Holdsworth, S., Wyborn, C., Bekessy, S. and Thomas, I. (2008). Professional development for education for sustainability: How advanced are Australian universities? International Journal of Sustainability in Higher Education, Vol. 9, Issue 2, pp. 131‐146. Johnson, M. and Isaksson, R. (2010). How to describe, define and work with sustainable development and how it relates to quality management – a study of Swedish Universities. Proceedings of the International Conference‐ quality and service sciences, 13th QMOD Conference, August 31‐September 1, Cottbus, Germany. Isaksson, R and Johnson, M. (2011). Sustainable Development in Universities – The power and role of visions and goals. Proceedings of the International Conference‐ quality and service sciences, 14th QMOD Conference, August 29‐31, San Sebastian, Spain. Lukman, R., Krajnc, D. and Glavic, P. (2009). University ranking using research, educational and environmental indicators. Journal of Cleaner Production, Vol. 18, pp. 619‐628. OECD (2001). Sustainable Development Critical Issues, OECD Publishing. NIST(2012). Business ‐ Nonprofit criteria. ,[online], http://www.nist.gov/baldrige/publications/upload/2011_2012_Business_Nonprofit_Criteria.pdf Onisto, L. (1999) The business of sustainability, Ecological Economics, Vol. 29, Issue 1, pp. 37‐43. Maranto, R., Hess, F. and Redding, R. (2009) The Politically Correct University: Problems, Scope and Reforms, Rowman & Littlefield. Roberts, J. (2001) Corporate Governance and the Ethics of Narcissus, Business Ethics Quarterly, Vol. 11 Issue 1, pp. 109‐127. Roorda, N. (2001): “AISHE – Assessment Instrument for Sustainability in Higher Education”, [online], https://www.box.net/s/dcl7z1r5jyqqqg8n0l84 Roorda, N., C. Rammel, S. Waara and U. Fra Paleo (2009): AISHE 2.0 Manual: Assessment Instrument for Sustainability in Higher Education Edition 2.0. Second draft, [online], https://www.box.net/s/0dglhugzyyzta4kkfb83 Sharma, S. and Starik, M. (2004) Stakeholder, The Environment And Society, Edward Elgar Publishing. Shriberg, M. (2002) Institutional assessment tools for sustainability I higher education: Strengths, weaknesses, and implications for practice and theory, International Journal of Sustainability in Higher Education, Vol. 3, issue 1, pp. 254‐270. Shriberg, M, (2004) Assessing sustainability: Criteria, tools and implications, In Corcoran, P.B. and Wals, A.E.J, (Eds,) Higher Education and the Challenge of Sustainability – Problematics, Promise, and Practice, Kluwer Academics Publishers, Hingham, USA. Sibbel, A. (2009) Pathways towards sustainability through higher education, International Journal of Sustainability in Higher Education, Vol. 10, No. 1, pp. 68‐82.

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Architecting the Dynamics of Innovation Ton Jörg¹ and Stephanie Akkaoui Hughes² ¹University of Utrecht, Utrecht, The Netherlands ²AKKA Architects, Amsterdam, The Netherlands agdjorg@gmail.com stephanie.hughes@akkaarchitects.com Abstract: The concept of innovation is hard to define and, consequently, difficult to put into practice. The actual complexity of innovation, we argue, is taken for granted. Our focus is on analyzing the very complexity of innovation, its dynamics and potential for practice. We take innovation as linked to creativity, by the processes of learning, thinking and knowing. Consequently, we analyze the complexity of innovation as a time‐related process. We take the ensemble of two partners and their interaction as a basic dynamic unit for innovation. Modelling this unit within the new framework of complexity shows innovation to be a self‐generative kind of process, depending on the conditions. That’s why we call these conditions “the conditions of possibility for innovation”. These conditions, which are closely linked to facilitating the quality of interaction and relationships between the partners in interaction within the ensemble, may be shown to unravel innovation as a nonlinear generative process with potential nonlinear effects over time. For practice this means that we need to take the notion of architecting interaction as foundational for innovation. We show innovation to be a complex process at both the individual and the collective level. Architecting interaction at both levels, both within and between ensembles, is to be taken as the key for architecting the dynamics of innovation, with the promise of nonlinear effects over time. We mean the promise of effects like bootstrapping each other within small communities of learners, with effects like the ‘butterfly effect’ and the ‘snowball phenomenon’. These effects are expected to bring the partners in interaction beyond their comfort zone into the Zone of Generativity. We argue here that, by architecting interaction and the conditions of possibility, we may encourage innovation as a generative process, thereby opening the space of the possible, with the promise of enabling these potential nonlinear effects. Henceforth, we may also speak about guiding innovation, guiding the very complex process of innovation. Keywords: dynamics of innovation, individual innovation, collective innovation, architecting interaction, generative learning, zone of generativity, nonlinear effects, conditions of possibility

1. Introduction The concept of innovation is hard to define and, consequently, difficult to put into practice. Although the word ‘innovation’ is widespread, not many groups and companies seem to genuinely understand the very notion of innovation and how to apply it. They do not seem to recognize innovation as a complex concept. The actual complexity of innovation is often misunderstood, considered too difficult for practice, and therefore unknowingly rejected. Our aim is to analyze innovation in more depth, to clarify its complexity and expose both its complex dynamics and potential for practice. In complexifying the concept of innovation, we may become able to show how to unleash the power of complexity in service of innovation: by opening new spaces of possibility.

2. The complexity of innovation In essence, innovating simply means doing something new that didn’t exist before, or doing something in a new way, a way that hasn’t been done before. Innovation is both in the process and in the resulting product. In practice, innovation and its complexity are mostly taken for granted. The general perception of innovation is one of misunderstanding its true nature. Numerous scholars, professionals and experts have attempted to dissect it, define it and explain it. Yet, innovation is still not fully understood, just because of its complexity. Although we talk about it rather often, we still aren’t sure of what it is, how it ‘works’, how to create it, how to deal with it, how to repeat it. So, the question remains: What is innovation? And how can we explain the real nature of its complexity? These are the key questions we want to deal with. Complexity is a rather unusual concept; we ‘simply’ cannot see it. For that reason we tend to take complexity for granted without realising how it may actually ‘work’ in practice. Once we realise how complexity may be ‘at work’ in practice, we may realise that complexity may actually be taken as self‐potentiating (Rescher, 1998, p. 28). This is not easy to understand. To understand innovation, however, we have to understand complexity as being part of the complexity of innovation. Only by understanding this complexity, we may start to know how innovation may really be ‘at work’ in the real. Understanding this complexity, then, may be of use for

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Ton Jörg and Stephanie Akkaoui Hughes facilitating innovation within companies and organisations. Understanding innovation means understanding innovation as a time‐related process. Understanding innovation means understanding innovation as a complex generative process. This notion of the generative implies creativity, which can be linked to concepts like generative processes of learning and knowing. Like Peter Senge, we may take this kind of generative learning as “learning that enhances our capacity to create” (Senge, 1990, p. 284). Our focus is on the conditions of possibility that encourage innovation as a complex generative process, thriving on interaction, with potential nonlinear effects over time. With this focus, then, we may become able to open up new spaces of possibility. Henceforth, complexity implies possibilities, hitherto unknown. Thinking in complexity implies opening the large field of the known unknown and the even larger field of the unknown unknown (Jörg, 2012).

3. Innovation in practice The advancement of our personal, social and business worlds cannot take place without innovation. Innovation of companies and organisations for example, is essential to their survival. So, we may state that “Innovation is therefore not a trend but a necessity” (Akkaoui Hughes, 2011). Innovation can be taken as both a personal and a social process for the sake of survival of the organisation one is working in. We urgently need new ways of thinking for that survival, ‘simply’ because “We cannot solve problems by using the same kind of thinking we used when we created them”, as Einstein once phrased it. So, we need to mature our thinking; that is, we need new thinking in complexity about innovation, to be taken as a ‘real’ complex concept.

4. What makes innovation so hard to grasp? Innovation is a rather confusing concept. Often what people look for are the big shiny sparks of innovation, the magical twist and the ‘abracadabra’. The news is innovation is not a magic trick. Innovation is simply the sum of a collection of small steps that form a non‐linear and sometimes chaotic process with emergent, potential nonlinear effects. The steps are small and relatively regular, there is no magic in them. The real genius is in their combination and in the knowledge of the direction, the purpose, the focus of innovation. Innovation is certainly a process, happening over time. We may question how does this process come about: not only in theory but also in practice. Innovation also depends on a key prerequisite ingredient: human interaction. Human interaction is in turn dependent on another key prerequisite ingredient: human context. Although we cannot force innovation or impose human interaction, we can surely design our environments and contexts. Human contexts need to gather all the possibilities for enabling and fostering the opportunities for interaction for the sake of innovation.

Innovation

(t)

ß1 (t)

A (t)

B (t) ß2 (t) R

(t)

Context Figure 1: A simple model of the link between human interaction and innovation

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Ton Jörg and Stephanie Akkaoui Hughes “We need to start designing contexts that are fertile for human interaction to emerge and blossom” (Akkaoui Hughes, 2011). That is, to increase the quality of human interaction and the complex emergent effects of such interaction. To ‘innovate’ then, the real question is: “How can we design contexts that trigger human interaction to create an experience of genuine innovation?” (ibid., 2011) This question is about “Architecting Interaction” (ibid.). This approach is foundational for architecting the dynamics of innovation. We may model these dynamic processes within a new framework of complexity, based on a new way of thinking: of thinking in complexity about a nonlinear complex reality (Jörg, 2011). In Figure 1 we present a simple model of the link between human interaction and innovation. It shows the role of the context, with diversity as a fertile resource. Human interaction takes place within the ensemble of two partners A and B, with their reciprocal relationship, as RAB and RBA. The two beta‐coefficients represent the influences as shaping forces on one another within the relationships RAB and RBA, all elements varying over time. With Valsiner (1998), we may take the unit of the ensemble as a complex, dynamic, cyclical‐helical unity (p. 251), with spiral developmental processes and their potential nonlinear effects over time. We believe that from a fertile context that gathers all the right ingredients, human interaction and its effects can emerge and blossom over time. Although it is not a linear calculated operation, certain contexts are indeed more fertile and more favourable for interaction than others. When human interaction takes place, and if the circumstances are favourable, innovation may emerge. The emergence of human interaction and innovation are deeply context‐dependent processes. As Akkaoui Hughes (2011) stresses: “Our responsibility in seeking innovation is to design the fertile context for it”. A context that minimizes the conditions of impossibilities and maximizes the conditions of possibility and opportunity for innovation, both as a generative process and in terms of generated effects, potential nonlinear over time.

5. What is innovation for? The very notion of innovation does not and cannot exist on its own, as a self‐standing notion. The process of innovation is in itself always related to a certain issue, topic or area ‘that we are innovating’. Innovation is a process called for to serve a specific challenge. When a company claims to be innovating, the key question is “innovating what?” The ‘what’ that innovation is applied to can be any issue, challenge or question at hand. Consequently the result of the innovation process can be any result you uncover. Although we can’t predict the result of innovation – if we do, then it wouldn’t be innovation. The process of innovation is quality‐driven and aims at enhancing the capacity to create value. At an individual level, innovation is about enhancing the capability to be creative. At a social level, innovation is about the potential of bootstrapping each other to become creative for the sake of survival through innovation. The outcome of innovating, starting from a certain challenge, should always bring quality and value.

6. The complexity of learning Although the term ‘learning’ is one of the most used terms in our discourse about innovation in the context of ‘learning organizations’, the fact remains that in the field of the social sciences, we actually do not know what learning really is (van Geert, 1994). Peter Senge (1990) himself introduced the term ‘generative learning’ in The Fifth Discipline as “learning that enhances our capacity to create” (p. 14). This is a rather unusual and attractive definition of learning, not very much known in psychology. He placed this generative learning as taking place within ‘learningful’ relationships’ (p. 284). His notion of generative learning seems rather intuitive and not based on a common theory of learning. Therefore Senge was unable to build a framework which could explain what kind of processes and effects were involved in the activity of learning. Although he had an open eye for dynamic complexity, he was not fully aware of the actual complexity involved and the potential complex effects of such generative learning. Generative learning, as we understand it, is a complex nonlinear process with potential nonlinear effects over time. To conceive of generative learning from a scientific perspective, we need a new way of thinking about learning, one that brings into play relationships (Jörg, 2011; cf. Fogel, 1993; see also Sidorkin, 2002). To conceive of this new way of thinking, we actually need to rethink the concept of interaction. This brings with it a paradigm shift, in terms of a complexity theory (Jörg, 2009, 2010, 2011; Fromberg, 2001).

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Ton Jörg and Stephanie Akkaoui Hughes Described as a ‘prophet of management’ by Peter Drucker et al. (1995), Mary Parker Follett (1924), in her book Creative Experience, touched on the notion of the nonlinear in her description of human interaction within the ensemble of two partners in interaction. What she did not describe is the actual generative kind of total effects on the partners involved. Recently Jörg (2009, 2010) introduced the notion of the nonlinear nature of generative learning, including its nonlinear effects over time. One of these effects is the ‘Matthew effect’, named after the bible by Matthew and introduced by the sociologist Robert Merton (1968): “the rich get richer and the poor get poorer”. We can extrapolate the positive aspects of this Matthew effect when applied to two partners in interaction and imagine how they can bootstrap each other in their interaction (see Bruner, 1996, p. 21). A different effect is the ‘Comenius effect’, named after the Czech pedagogue Comenius. We may also refer to effects like the known ‘butterfly effect’ and the ‘snowball phenomenon’ in education (Anderson et al., 2001). See below for a description of these effects. The existence of non‐linear effects, being in line with intuitive notions in the field, becomes very informing for examining the concept and process of learning. Going back to the notion of generative learning in the context of organizations, accepting non‐linear effects is foundational for adopting a ‘possibility‐oriented’ approach rather than an ‘ends‐oriented approach’ of learning in organizations. Of course, the practical challenge is how to organize this generative learning. We subscribe Senge’s view about the importance of ‘learningful’ relationships. We take the term ‘reciprocal learning’, with reciprocal influences operating as mutually shaping forces within the interactive reciprocal relationship, as foundational for generative learning (see Jörg, 2004, 2009).

7. The complexity of knowing Our understanding of learning as a generative process of learning implies the beginning of a new way of thinking about reality: of what is the real. We might conceive of it as thinking about a different reality. If reality, then, turns into a different, larger and richer reality, it may imply that we have to develop an altered account of this new reality (Kauffman, 2009). Of course, this means an opening to different fields of knowing: the field of the ‘known unknown’ and potentially the field of the ‘unknown unknown’ (Jörg, 2012). Complexity itself is very much part of this rather broad field of the unknown. Not taking complexity for granted means we may access and open these fields; they then become part of the ‘world of the possible’ (Kauffman, 1993, p. 375). In general, we are still very much ignorant about the complexity of reality. The very complexity of human interaction contributes a lot to the complexity of reality. The hitherto unrecognized fact is that complexity is self‐potentiating (Rescher, 1998, p. 28). Henceforth, we must become aware that we ‘simply’ do not know what we do not know: that is, the complexity of knowing about complexity (Jörg, 2012). You ‘simply’ need complexity of thinking and modelling to deal with ‘real’ complexity. This is true for the complexity of human interaction and for understanding how learning, thinking and knowing are to be linked to this complexity. To understand the complexity of interaction, we need to subscribe that human interaction is the process of mutually shaping forces, being ‘at work’ in this interaction, impinging on one another and having (total) effects on one another over time. In Jörg (2011), this interaction has been modelled within a causal framework. By going beyond the accepted causal framework, we were able to expand on the notion of causal interaction. The presented model of interaction within dynamic, ever‐evolving human relationships showed the possibility of nonlinear effects over time, fully in accordance with the formula shown in literature, of the builders of the causal framework of LISREL themselves (see Jöreskog & Sörbom, 1993). Based on this framework, we may better understand how human beings may really be able to bootstrap each other in small networks of ensembles of two partners in interaction or communities of learners.

8. The complex relationship of learning, thinking, knowing and innovating To understand innovation, how innovation can be ‘at work’ in reality, we need to understand how generative learning might actually be a condition for knowing. Both learning, thinking and knowing should be taken as verbs, and denote processes taking place within and between human beings over time. It is through understanding these interdependent and interconnected processes as complex self‐potentiating processes with nonlinear effects that we become more knowledgeable about the nature of the complexity involved in human interaction. These processes can be both intra‐generative and inter‐generative, meaning processes within and processes between humans in interaction (see Jörg, 2011). To unleash the power of complexity, we must understand how these generative processes and their effects can actually be facilitated; not only in

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Ton Jörg and Stephanie Akkaoui Hughes theory but also in practice. It is through such understanding that we may comprehend the conditions as conditions of possibility. These conditions are foundational for a possibility‐oriented approach. They are constitutive for an architecture of interaction, enabling the nonlinear effects over time. Henceforth, architecting interaction is conditional for architecting the dynamics of innovation. It is through architecting the context for human interaction that innovation becomes possible. Architecting interaction is then taken as conditional for a possibility‐oriented, generative approach. This way of new thinking is supported by Steve Job’s belief that “the best innovation is sometimes the company, the way you organize a company”. His belief was, of course, based on his own practical experiences. We may understand innovation within a company both as a personal, individual kind of innovation and as a kind of social innovation. The effects of innovation may be conceived as well at both levels (see f.i. the biography of Steve Jobs by Walter Isaacson). From the above links between learning, thinking and knowing and their relationship with innovation, we may understand their interdependent relationship as a real complex dynamic, relationship: see Figure 2 below. This relationship, now, can be understood as conditional and constitutive elements for “the way you organize a company”. Architecting the dynamics of innovation is about facilitating this relationship and, thereby, fostering the potential nonlinear processes of generative learning. Architecting the dynamics of innovation is about organizing reciprocal, potentially generative processes of learning within reciprocal ‘learningful’ relationships. In general, we may describe such kind of relationships between two entities as so‐called ‘ensembles’ or loops (Kauffman, 1993, pp. 426, 463). We do need an adequate ensemble theory, as Kauffman argues (p. 463). The ensemble with its interaction should now be taken as the corner stone for innovation: that is, for organizing a company to make innovation happen in the real.

innovation

thinking

learning

knowing

Figure 2: The complex tetrahedral relationship between learning, thinking, knowing and innovation

9. The conditions of innovation The quality of the innovation process depends on the quality of the interaction that leads to it. However, the quality of the interaction itself depends on the quality of the context it emerged in. Architecting interaction aims at creating the fertile context for human interaction to emerge. Human interaction can then become self‐ generative, turning general learning into generative learning which is foundational for an innovation process that will itself generate innovation as a result. As innovation seekers, we are merely facilitators. We should not and cannot control the results of innovation. By understanding the dynamics and the effects of interaction, we can merely guide and facilitate the process

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Ton Jörg and Stephanie Akkaoui Hughes of innovation as a nonlinear process. We may call this approach ‘guided innovation’. We can create the context, spark the trigger, guide the process and ‘simply’ trust the emerging quality of innovation.

10. The context for innovation As previously mentioned, our role as ‘innovation facilitators’ is to create the context for innovation. That context needs to gather a number of ingredients or factors, that we call the conditions of possibility. It is the balance of those conditions that will render the context fertile for the quality of human interaction to emerge, as a process of generative learning, thinking and knowing. It is worth mentioning and accepting that although we can design the context for interaction and innovation and make sure the conditions of possibility are all fulfilled, there remains in the emergence of human interaction and that of innovation the possibility of serendipity, as an element of the unknown unknown. “Our role as architects of interaction and innovation is to strive to create a context that minimizes the conditions of impossibility and optimizes the conditions of possibility” (Akkaoui Hughes, 2011). Innovation is rarely the result of a controlled linear process. Instead it often arises from a non‐linear process of cooperative and collaborative interaction, where independent individuals converge their attention and align their thinking towards addressing a common challenge. As Akkaoui Hughes (2011) explains: “when seeking innovation to address a certain challenge, we as facilitators are responsible for architecting interaction. Human interaction is the essential condition for innovation. When seeking innovation, we must therefore start by seeking human interaction. To create human interaction, we start by designing the context in which we plan to operate”. This context needs to fulfil a number of conditions of possibility, namely cooperation in a human context, diversity, available resources, and independence of the members, a common question that matters, a quality‐driven process, a communal search for value and trust in the emergence of a non‐linear process with potential nonlinear effects. In order to foster a healthy practice of cooperation, people need to be operating in a human context. A human context is one where every person is an active participant, that feels genuinely needed and appreciated, that feels responsible for the whole and can see the effect of his/her individual involvement in the group’s dynamic. It is only in a human sensitive context that people can truly come together, relate, communicate, brainstorm, consult and interact. In fact, cooperation is essentially the coming together of different and independent entities in a human context. First, it is important here to stress the importance of the diversity of the entities coming together; they have to be bringing different perspectives to the table. Second, it is best when every entity is a complete and independent individual, that is strong and defined in itself before entering a group dynamic. To keep the group aligned and achieve individual engagement, it is essential that the question at hand, the challenge which we are seeking to innovate matters, for every person involved, at a personal and a group dynamic, every person needs to genuinely care about the question and see its impact on his/her personal life and on the group/company/society’s life in order to feel ownership of the matter at hand. Another essential condition that will render human interaction possible is a common search for quality. The process of cooperation towards innovation needs to be one driven towards the creation of shared values, by quality in service of innovation. The conditions of possibility above need to all be fulfilled in order to strive for innovation through human interaction. When seeking innovation, our responsibility is not only limited to creating the best context for the best innovation, it also includes architecting interaction within the ensembles of entities involved in innovation.

11. The dynamics of ensembles An ensemble is a defined group of elements. The basic ensemble is formed by two elements. We will consider this two‐member ensemble to be our basic unit. Organizing cooperative ensembles denotes putting in place the right conditions for ensembles to interact; this however can be done at different levels. Organizing cooperation within ensembles starts at the basic level of one ensemble, containing two partners. At a second level, if we consider two ensembles containing two partners each, we are then organizing cooperation between ensembles. By taking the ensemble as the very basic building stone for organizing a company, we may start to think of the best conditions for innovation. Architecting interaction within ensembles seems to be

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Ton Jörg and Stephanie Akkaoui Hughes the very best condition for facilitating innovation within a company. We may expand on this notion of conditions by taking the ensemble of ensembles into account.

A ' B 3 4 Intra‐ Inter‐ Intra‐ generative generative generative dynamics dynamics dynamics

Figure 3: Kinds of generative dynamics in human interaction We Between ensembles through human interaction. These ensembles may develop as well a reciprocal relationship between their participants. Each ensemble may function as well as the environment for the other ensemble; that is, become a resource of information and inspiration for the (two) partners of the other ensemble. Architecting interaction between ensembles may lead as well to generative processes of learning and knowing: both within and between the ensembles. Of course, we need to take into account the possibility of both cooperation and competition in the dynamics of interaction. Both cooperation and competition should be possibility‐oriented, being in function of opening new spaces of possibility and opportunity. Only then we may open up the world of the possible (Kauffman, 1993), being very much part of the fields of the known unknown and unknown unknown (see Jörg, 2012). Organizing four partners in two ensembles and encouraging interaction among them seems to us the best example for new thinking and the best trigger for innovation. We may expand on this notion by taking into account two ensembles of four partners within two ensembles. So, we have two units of four partners interacting both between and among their ‘own’ ensembles in potential different ways. Their different backgrounds and different resources may trigger the generative processes of learning, thinking and knowing, being conditional as necessary conditions for best innovation. We may speak about intra‐generative and inter‐generative processes: see Figure 3. Both kind of processes may show the potential nonlinear effects, described above. The actual complexity involved in the interaction has consequences for the learning, thinking, knowing and the innovation process inside each of the partners in interaction: see Figure 4 below. It shows the very complex dynamics of innovation both inside and between the partners in their interaction. It shows innovation at both the individual and the collective level. innovation

innovation

thinking

learning knowing

thinking

learning

knowing

Figure 4: The complex dynamics of innovation enabled through human interaction

12. The possible effects of human interaction We will describe the different nonlinear effects of human interaction within ‘learningful’ relationships, as potential effects of human interaction over time. The Snowball Phenomenon, the Matthew effect, the Comenius effect and the so‐called ‘butterfly effect’ are the nonlinear effects to be described.

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The Snowball Phenomenon

The ‘Snowball Phenomenon’ describes an effect that takes place within a community of learners, that spreads to different members of the group of learners of a community (Anderson et al., 2001).

The Matthew effect

The ‘Matthew effect’ describes the symmetric effects of the rich getting richer and the poor getting poorer as a phenomenon that may take place in practice. In our modelling of human interaction within a causal framework, the Matthew effect implies increasing total causal effects on the partners, taken place over time through strengthening of the causal parameters of the model.

The Comenius effect

The ‘Comenius effect’ describes the effect of a teacher (member A) who learns most by teaching a student (member B): “the more you give, the more you get”. In our modelling of human interaction, it implies that the increase of influence on member B may generate an increase of total effect on member A over time (see Jörg, 2011).

The butterfly effect

The so‐called ‘butterfly effect’ describes the possibility that a small change may have increasing effects over time in a nonlinear way and lead to enormous consequences like those of tornado. Edward Lorenz (1964), a meteorologist, was the first person to describe this effect. The discovery itself of this effect was a clear case of serendipity. We may describe all of these effects as a kind of quality of the complexity involved in human interaction. We may speak therefore about the quality of interaction in terms of strong interaction and about the quality of relationships in terms of strength of relationship between two partners in interaction.

13. Architecting the dynamics of innovation Innovation is not one point in time, not an isolated instance, not a punctual solution. Innovation is a complex ever‐evolving process of learning, thinking and creating knowledge. The quality of interaction and quality of relationships can be conceived as being interdependent and facilitative for each other. In fact, according to Akkaoui Hughes (2011), “the quality of the relationships defined the quality of the interactions”. In turn, the quality of interaction determines the quality of relationships. They are interdependent. The very dynamics of their interdependence should be taken as the hitherto unknown motor for new, generative processes of learning, thinking and knowing, being conditional for the dynamics of innovation. The question, then, is: “how to facilitate both the dynamics of learning, thinking and knowing in order to encourage innovation?” The nonlinear effects, being potential consequences of the complex dynamics of interaction, may be described as being part of the very dynamics of innovation. They may be described as taking place within the space of possibilities, resulting from the conditions of possibility. Facilitating these conditions by way of organizing the context adequately may actually foster innovation best, by bringing the partners in interaction outside of their comfort zones, which is different for each partner in interaction (Kalantzis & Cope, 2008), developing these static zones into dynamic zones of generativity toward increased levels of potential knowing, thinking and learning (cf. Ball, 2012, pp. 289, 290).

14. The best innovation The best innovation thrives on human interaction; that is, on the potential power of complexity involved in the dynamics of interaction with their quality of interaction and the quality of relationships between the partners in this interaction. We may speak about the untapped potential of human interaction for innovation, in terms of both personal and social innovation, strengthening one another over time. This untapped potential is about the intra‐ and inter‐generative processes and their potential nonlinear effects described above. These processes may be understood as bootstrapping processes, in which human beings may bootstrap each other: both within their particular ensemble and within the ensemble of ensembles. We may speak as well about unleashing the power of complexity of innovation, in terms of the dynamics involved in individual and collective innovation. Consequently, we view innovation as both a personal and a social process within different units of ensembles.

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15. Conclusion Innovation is a complex concept, thriving on interaction of which the real complexity is still very much neglected or discarded. This complexity is very much in need to be explored. We take innovation and its complex nature as resulting from smart networks within complex configurations, operating as bootstrapping configurations, opening a new world of possibilities. Innovation may emerge unexpectedly if the conditions of possibility are present. They may be conceived as being organized in a way that unleashes the complex power of human interaction as a nonlinear process with potential nonlinear effects. Architecting the dynamics of innovation is about architecting such kind of interaction within complex, dynamic networks of ensembles. Interaction, as complexly generative interaction is self‐potentiating through self‐generative processes, operating as self‐generative, self‐reinforcing, and self‐perpetuating processes of learning, thinking and knowing and their potential nonlinear effects over time. Innovation, then, is about the complex generative, creative states of being one can be in as a human being, as a consequence of these processes. They bring the partners beyond their comfort zone, bringing them within the so‐called ‘Zone of Generativity’ (Ball, 2012), as being part of a complex dynamic state of generativity, resulting from generative processes. These states are complex, dynamic states of learning, thinking and knowing, at both the personal and social level of innovation. From these notions we may understand what the promise of architecting the complex dynamics of innovation might mean. More importantly, we may understand as well how innovation might actually be organized. This includes the context for innovation with its conditions of possibility, which may be the space one is operating in. Architecting the dynamics of innovation is about opening a new world of the possible with unexplored spaces of possibilities.

References Akkaoui Hughes, S. (2011) See http://www.linkedin.com/in/stephanieakkaoui Anderson, R.C., Nguyen‐Jahiel, K. McNurlen, B., Archodidou, A., Kim, S., Reznitskaya, A., Tillmanns, M. & Gilbert, L. (2001) “The Snowball Phenomenon: Spread of ways of Talking and Ways of Thinking Across Groups of Children”, Cognition and Instruction, Vol 19, pp 1‐46. Ball, A. F. (2012) “To know is not enough: Knowledge, power, and the Zone of Generativity”, Educational Researcher, Vol. 41, No. 8, pp. 283‐293. Bruner, J. (1996), The Culture of Education. Cambridge MA: Harvard University Press. Drucker, P. F., Kanter, R.M., and Graham, P. (Eds.) (1995), Mary Parker Follett. Prophet of Management, Boston, Harvard Business School Press. Follett, M. P. (1924), The Creative Experience. New York, Longmans, Green. Fogel, A. (1993), Developing through relationships, Chicago, Chicago University Press. Fromberg, D. P. 2001), The intuitive mind and early childhood education: Connections with chaos theory, script theory, and theory of mind, In: B. Torff & R. J. Sternberg (Eds.), Understanding and teaching the intuitive mind, Student and teacher learning (pp. 93‐113), Mahwah (NJ), Lawrence Erlbaum Inc. Jöreskog, K.G. & D. Sörbom (1993), LISREL8: A Guide to the Program and Applications. Chicago, SPSS. Jörg, T. (2004) “A Theory of Reciprocal Learning in Dyads”, Cognitive Systems, Vol. 6, No. 2/3, pp 159‐170. Jörg, T. (2009), “Thinking in complexity about learning and education – A programmatic view”. Complicity, 6 (1), Online available at http://www.complexityandeducation.ualberta.ca/COMPLICITY6/Complicity6_TOC.htm Jörg, T. (2010) “A Theory of Learning for the Creation and Management of Knowledge in Learning Communities and Organizations”, International Journal of Knowledge and Systems Science (IJKSS), Vol. 1, No. 1, pp. 27‐42. Jörg, T. (2011), New Thinking in Complexity for the Social Sciences and Humanities. A Generative Trans‐disciplinary Approach, New York, Springer Publishers. Jörg, T. (2012) ”The crisis and the complexity of knowing”, International Journal of Knowledge and Systems Science, Vol. 3, No. 3, pp. 1‐14. Kalantzis, M. & Cope, B. (1998), New learning. Elements of a science of education, Cambridge, Cambridge University Press. Kauffman, S. (1993), The origins of order. Self‐organization and selection in evolution, Oxford, Oxford University Press. Kauffman, S. A. (2009) Foreword. The open universe. In: R. Ulanowicz (2009), The third window. Natural life beyond Newton and Darwin (pp ix‐xvii), West Conshohocken, Templeton Foundation Press. Rescher, N. (1998), Complexity. A philosophical overview, New Brunswick, Transaction Publishers. Senge, P. (1990), The Fifth Discipline, New York, Doubleday. Sidorkin, A. M. (2002), Learning relations, New York, Peter Lang. Valsiner, J. (1998), The guided mind, Cambridge (MA), Harvard University Press. Van Geert, P. (1994), Dynamic systems of development. Change between complexity and chaos, New York, Harvester Wheatsheaf.

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The Identification of Polish Banks Intangibles’ Significance and Efficiency Monika Klimontowicz and Janina Harasim University of Economics, Katowice, Poland mklimontowicz@ue.katowice.pl jharasim@ue.katowice.pl Abstract: Knowledge and intellectual capital embodied in intangible assets are recently considered as banks’ primary source of competitive advantage and long‐term value. Retail bank executives are also aware of the sustainable growth and profitability hinge on their ability to attract and retain loyal customers. The turbulent changes in economic environment, increased competition in the Polish retail banking industry, internationalization and globalization, implementing new products, changes in consumer needs and technological progress caused Polish banks to revise their market strategies. Strategies oriented towards defining the target mix of future consumers has become a routine topic of discussion. At the same time better product, convenience and lower fees are not enough to make the consumers to be loyal. Putting consumer loyalty at the heart of growth requires new skills and competences of banks. They must learn to nurture the loyal core of their customer and develop skills for attracting the right new customers. A lot of bank’s management concepts have used knowledge, employees skills, social relationship, brand, know‐how and other intangibles to pursue that goal but it is necessary to find out which of them are the most effective ones. The purpose of this paper is to present which intangibles are used by Polish banks to develop their competitive advantage and performance. Banks managers’ opinions about the significance and efficiency of intangibles are confronted with the customers’ opinions. The results show to what extent banks’ opinions and practices correspond with customers’ needs and which intangibles are crucial for them. It is prepared combining descriptive theoretical and empirical analytical methods. The empirical study was conducted in the spring of 2012 and focused on retail banks and their customers. The paper is based on interviews with banks’ managers and banks’ customers. Keywords: intangibles’ significance and efficiency, competitive advantage

1. Introduction As a result of turbulent changes in economic environment banks in Poland shifted from product‐oriented strategy to customer‐oriented strategy which focuses on developing long‐term relationships with customers. Nowadays banks know that they will not achieve perfect positioning through the traditional 4Ps approach of product marketing (Knox, 2004). The primary purpose of marketing strategy is the development of a competitive advantage that can provide customers with superior value compared to competitive offers.The customer satisfaction has become the key to repeated buying, brand loyalty and spreading positive opinion of a bank. This paper presents the structure of Polish banks’ intangible assets and discusses their role in the process of gaining competitive advantage and developing positive relationships with customers. It is prepared combining descriptive theoretical and empirical analytical methods. The empirical study was conducted at the beginning of 2012. Presented results are a part of an extensive survey focused on the role of intangible assets in gaining competitive advantage on Polish banking market and have practical implications to banks’ managers in different fields in their competitive strategies. The data were collected by two methods – PAPI (personal and pen interviews) and CAWI (computer assisted web interviews). The survey’s target group consisted of banks’ managers and customers. The assets of banks which took part in the survey correspond to 48,4% of polish retail banking sector’s assets. 54,5% of them represent domestic capital and 45,5% foreign capital. Taking into account the number of employees and the number of branches that probe was also representative one (see picture 1 and 2). 679 customers responded to the questionnaire. All of them use at least one banking or financial product. 61,4% of them has been banks’ client for more than 5 years. They mostly use personal accounts (98,5% of respondents), debit cards (76,7%) and savings accounts (55,7%). The customers’ probe is representative for Polish society in relation to sex, incomes, permanent residence, opinion about their economic situation and

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Monika Klimontowicz and Janina Harasim their expectations concerning banking services. Taking into account sex, incomes and permanent residence its structure corresponds to statistical data characterizing Polish society.

Figure 1: Number of surveyed banks’ employees

Figure 2: Number of surveyed banks’ branches Opinion about their economic situation follows the normal distribution (Gaussian distribution) which is commonly encountered in practice and is used throughout statistics as a simple model for complex phenomena. Whereas the main expected benefits from cooperation with the bank correspond to the Kaynak and Harcar survey who divided banks’ customers into three target group (Kaynak and Harcar, 2004), namely:

security‐oriented,

interaction‐oriented, and

task‐oriented.

According to Kaynak and Harcar as a result of security‐orientation the bank and the bankers must look conservative and traditional. At the other end of the spectrum are task oriented customers who place their main emphasis on the banks’ professionalism. The effective procedures of banks’ operations become very important for that market segment. They appreciate speed, accuracy and efficiency of financial transactions. Finally, interaction‐oriented banks’ customers value the relationship between the bank and themselves very much. Generally, most of Polish banks’ customers declared that they are security‐oriented.

2. Results 2.1 The identification of banks’ intangibles structure Over the last decades situation of the banking sector in Poland has changed remarkably. Banks has realised that a firm possesses a sustainable competitive advantage when its value‐creating processes and position have not been able to be duplicated or imitated by other firms (Porter 1998). Nowadays the challenge for bank is not only to understand that the the real value of bank and the customers’ loyalty are based on intangible

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Monika Klimontowicz and Janina Harasim assets but also to specify components of its structure and find out which of them are the most significant and effective in making customers satisfy (Klimontowicz, 2011). The term Intangibles has many complex connotations and is often used synonymously with intellectual capital, intellectual property, intellectual assets and knowledge assets. It is defined as (Rogowski, 2006):

the sum of all the people of the company know what gives a competitive advantage in the market (T. Steward),

knowledge that can be converted into value (L. Evidson),

knowledge, experience, organizational technology, clients relationship and professional skills which give company a competitive advantage (L. Evidson, M. Malone),

the sum of hidden assets which are not seen in balance sheets consists of what employees have in their heads and what they leave in a company going home (G. Roos, J. Ross).

The variety of definitions makes the scientists and managers to classify, order and characterize the components of intangibles individually (see table 1). Table 1: The components of intangibles in selected concepts (Klimontowicz, 2011) Author Conrad Group Sveiby K.‐E. Evidson L., Malone S.

Year 1989 1996 1997

Individual Capital Human Capital Human Capital

Brooking A.

1997

Human Assets

Roos. G i Roos. J Steward T. A. Lev B.

1997

Human Capital

1998 2001

Human Capital Human Assets

The components of intangibles Structural Capital Internal Structural Capital External Structural Capital Structural Capital Organizational Capital Client Capital Capital of Capital of Processes Innovations Infrastructure Intellectual Property Market Assets Assets Assets Organizational Capital Client and Relationship Capital Structural Capital Organizational Assets

Client Capital Assets of Innovation

Analyzing the internal structure of intangibles it can be noticed that all concepts mention a human capital. It is defined in the same way and consists of employees’ experience, knowledge, qualifications and skills combined with their motivation and managers’ skills and abilities. At the contrary structural capital is defined and named in different way. Comparing those concepts it should be stressed that as far as intangibles’ components have been described precisely in the industry, they are rarely examined in banking sector. Table 2: The structure of bank’s intangible assets Intangibles Human Assets

Market Assets

Organizational Assets

Innovative Assets

Components knowledge and experience professionalism client oriented attitude and an ability of developing relations with clients level of employees’ creativity and innovativeness wish to cooperation quality of management image and reputation quality and effectiveness of bank’s marketing activity knowledge of clients’ needs and an ability to match offer with consumers’ needs and expectations and sales strategy ability of developing relations with clients knowledge of competitors and their offers traditional distribution channels modern distribution channels equipment and infrastructure working conditions safe and comfort way of transactions’ authorization TQM system level of service’s modernity R&D and innovativeness budget

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Monika Klimontowicz and Janina Harasim Intangibles

Components innovative products/services implementation innovative procedure implementation using the innovative technology in bank’s management new way of providing services

The structure of bank’s intangibles must reflect its specific character and take into account factors which create its long‐term value. From that perspective two the most important items are human assets and market assets. In banking sector achieving a market success is impossible without proper reputation and clients’ trust. In today’s changing environment the process of increasing bank’s value is undoubtedly connected with process optimization and service technology. That is why the structure of bank’s intellectual capital should also include organizational assets and innovative assets (Klimontowicz 2012) ‐ see table 2. The presented structure of banks’ intangible assets was the base for designing the questionnaire. Altogether, 51 statements in the questionnaire dedicated to banks’ managers and 36 statements in the questionnaire dedicated to customers were selected to examine the significance and efficiency of banks’ intangibles. All that statements were summed into five group of assets: human, market, organizational and innovative assets versus financial assets which represent tangible assets. Reliability analysis, measured with Cronbach’s alpha, showed adequate reliability levels for all of the scores (see table 3). Table 3: Scores reliability levels measured with Cronbach’s alpha. Scale Significance of intangible assets (according to banks’ managers). The ability of gaining competitive advantage using intangible assets(according to banks’ managers). The efficiency of using banks’ intangibles (according to banks’ managers). Significance of intangible assets (according to customers). The efficiency of using banks’ intangibles (according to customers).

Cronbach’s alpha 0,97 0,96 0,64 0,89 0,72

A seven‐point Likert scale from 0 to 6 was used. The significance of intangible was graded from 0 which meant that the factor is not important at all to 6 which meant the huge importance. When the banks’ managers had decided how important was a factor they were asked to decide if bank is able to use that factor efficiently in the process of gaining competitive advantage. They used the seven‐point scale from 0 which meant desperately low ability to 6 which meant excellent ability. When the customers had decided how important was a factor they were asked to decide what in their opinion was the grade for that factor in their banks. They also used seven‐point scale (from 0 which meant desperately low grade to 6 which meant excellent grade).

2.2 Significance of banks’ intangibles in gaining competitive advantage Research continually confirms the increasing role of intangible assets in the process of gaining competitive advantage and satisfying customers. That is no doubt that there is a significant correlation between satisfaction and repeated buying, brand loyalty and spreading a positive opinion of the product. In the banking sector Loveman (1998) found that higher customer satisfaction leads to increased cross‐selling at the branch level. According to Ittner and Larcker (1998) customer satisfaction is a leading indicator of revenue and growth. The consumer satisfaction category is based on the premise that the profit is made through the process of satisfying consumers’ needs. A lot of programs concerning consumer satisfaction have already been implemented in banks in Poland. They include activities which are to attract different kind of clients. One of the survey’s purposes was to examine if banks really know what factors are important to their customers and as the result of that if these programs and other banks’ market activity are based on right assumptions. Whereas the important role of intangibles is quite obvious for scientists and researchers Polish banks’ managers still choose the financial assets as the most important for bank’s competitiveness. 66,7% of them decided that they are the most important assets. The second place was granted to human assets (33,3% of responders). The third place went to organizational and innovative assets but they were pointed only by 11,1% of responders each. Generally, according to banks’ managers, the average significance of different assets was as follows:

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financial assets

‐

‐ 4,9

human assets

‐ 4,6

market assets

‐ 4,6

innovative assets

‐ 4,4

organizational assets ‐

4,3

The financial assets are definitely less important for customers. The average significance of these assets was 4,4. Similarly they pointed human, organizational and innovative assets as slightly more important ones. The average significance of these assets was 4,5. Surprisingly the less important significance was granted to market assets (average 4,0). After general evaluation respondents were asked to form their opinion of particular intangible assets. The human assets were defined as the collective sum of the attributes, life experience, knowledge, skills, potential, inventiveness, energy, and enthusiasm that staff choose to invest in their work. It’s important to remember, however, that individuals are only the asset insofar as they choose to invest their human capital into organization. With strong competition and the emergence of new business models banks’ employees need to be more responsive to change, more flexible and more resilient. According to banks’ managers and customers all human assets’ factors are important. Among them for banks’ managers the most important is a client oriented attitude and an ability of developing relations with clients. Whereas for customers the most important ones are knowledge and experience (see figure 3). Additionally banks’ managers were asked to decide how important are:

a level of employees’ innovativeness (mean score – 4,3),

a wish to cooperation (mean score – 4,4),

a quality of leadership and management (mean score – 4,8).

Figure 3: The significance of human assets The second group of intangible assets rated by responders were market assets. They are thought to be one of the most important factors which impact banks’ position on the financial market. Especially the image and reputation seem to be crucial for every bank activity because they are related with people attitudes, feelings and expectations (Jagelavicienie, Stravinskiene, Rutelione, 2006). A good brand makes the bank and its services recognizable and memorable on the market. The market‐based intangibles also relate to effective usage of banks’ databases that is aimed to attract and retain clients by better identifying their needs. It enables banks to prepare their customer‐oriented strategy, to develop better information about clients, to serve clients’ needs better and reap profits through increased cross‐selling and up‐selling (Klimontowicz, 2010). Among the market assets the most important for them are the ability to match bank’s offer with consumers’ needs and expectations and developing relations with clients. Surprisingly nowadays the

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Monika Klimontowicz and Janina Harasim traditional image and reputation factors as brand, logo, advertisement and promotion, an appearance of employees and bank’s departments, a quality of brochures, booklets and other materials, etc. are the least important for customers. Banks’ managers and customers’ opinions on the significance of image, reputation and banks’ marketing activity differ significantly (see figure 4).

Figure 4: The significance of market assets The last groups of intangible assets were organizational and innovative assets. They are connected with the new technical solutions. For a half of the millennium most of the banks have worked on the basis of physical distribution. Nearing the end of the first decade of the new millennium an electronic distribution has matured, works and is proven. Bankers have added automated teller machines, call centres, the Internet and the mobile phones as an extra layer on the foundation of the branch distribution system. (Skinner, 2008). Owing to new technology banks in Poland were able to improve the quality of their operations, improve settlement procedures and speed up the turnover of money. In the last decade, technical solutions, including the development of IT and the Internet, have become one of the key internal factors enabling banks to improve their management systems. In addition they contributed to the development of banking products and their distribution channels. A lot of customers are interested in Internet and telephone banking, as well as in mobile banking, which combines telephone banking with Internet banking. Modern distribution channels are definitely more important for them that traditional ones and they are the most important among all intangible factors. The second one is safe and comfort way of transactions’ authorization (mean score ‐5,0). Both factors are underestimated by banks’ managers. The results of the survey show that they pay too much attention to the factors referring to traditional branches which are less important for customers (see figure 5) Taking into account a web culture which creates a generation of people who live and work differently from previous generations the level of banks’ innovativeness will become more and more significant. Both, banks’ managers and customers, agreed that innovative assets are very important. Mean scores for almost all innovative factors are at the same level. Only the implementation of innovative procedures is underestimated by banks’ managers in the relation to customers’ scores (see figure 5). The survey results show some differences in opinion on the significance of particular banks’ assets in the process of gaining long‐term competitive advantage. Generally the largest difference concerns the role of financial assets. They are more important for banks’ managers than customers. Taking into account elements of intangibles for customers the most important factors are:

electronic distribution channels (mean score – 5,1),

safe and comfort way of transactions’ authorizations (mean score – 5,0).

Banks’ managers declared that the most significant are:

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an employees’ client‐oriented attitude and an ability of developing relations with client (mean score – 5,0),

an ability to match offer with clients’ needs (mean score – 5,0).

Figure 5: The significance of organizational assets

Figure 5: The significance of innovative assets As far as these factors are also important for customers, the factors of huge importance for customers are underestimated by managers. At the other hand they used to overestimate factors connected with banks’ image, reputation and marketing activity.

2.3 Efficiency of banks’ intangibles usage The next part of research focused on banks’ ability to use intangible assets in the process of gaining competitive advantage. Banks’ managers were asked to decide if, in their opinion, bank uses intangibles effectively. Generally they thought their banks to be rather effective in using human, organizational and market assets. The grade for innovative assets was in the middle of the scale what meant neither effective nor ineffective. The customers were asked if they are satisfied with bank’s activity in the field of personnel, organization, innovativeness and marketing. They pointed that they were rather satisfied, especially in the

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Monika Klimontowicz and Janina Harasim field of human and innovation assets, but their grades were a little bit lower than managers’ grades with the exception of innovative assets (see table 4). Table 4: General efficiency of banks’ intangible usage Intangibles Human assets Organizational assets Innovative assets Market assets

Banks’ managers 4,1 4,2 3,5 4,3

Customers 3,9 3,8 3,9 3,6

According to banks’ managers they are very good in the field of using human assets. Their employees are thought to be very professional, well‐educated, experienced, customer‐oriented and able to develop relations with customers. Unfortunately customers are not as satisfied as they should be taking into account the importance of these factors (see figure 6).

Figure 6: The efficiency of human assets’ usage in the process of creating competitiveness Analyzing the data concerning market assets it is easy to notice that the ability to match offer with consumers’ needs and expectations received the lowest score among all market factors graded by customers. At the contrary according to managers banks’ ability to use that assets effectively is thought to be very good. Another factors are graded at the same level by both group of respondents (see figure 7) The organizational and innovative factors consist of traditional and modern factors. The traditional ones as branch network, equipment and infrastructure received lower scores when they were graded by customers. At the other hand the modern ones as electronic distribution channels, a quality systems and all innovative factors as level of service’s modernity, innovative product/services implementation, innovative procedures’ implementation and new way of providing services were graded higher by them. A safe and comfort way of transactions’ authorization was graded at the same level by both groups of respondents (see figure 8 and 9). Comparing the significance of intangibles’ factors with the managers and customers’ opinion on banks’ ability to use them in the process of creating competitiveness it can be observed that the consumers are generally satisfied with the factors which are important for them. Their lower satisfaction is connected with the less important factors.

3. Conclusions The sum of all the changes which have appeared in market environment over the last decades makes the banking sector to be highly competitive. In today’s changing environment the increase of bank’s value is undoubtedly connected with developing intangible assets. A knowledge embodied in intangibles has become crucial for that activity and in large extent influences banks’ competitiveness and growth. The structure of

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Monika Klimontowicz and Janina Harasim bank’s intangibles should reflect its specific character and take into account factors which create its long‐term value.

Figure 7: The efficiency of market assets’ usage in the process of creating competitiveness

Figure 8: The efficiency of organizational assets’ usage in the process of creating competitiveness In the response to turbulent market environment commercial banks have shown a renewed interest in relationship marketing. They try to focus on developing long‐term relations leading to customers’ loyalty. Unfortunately preparing their competitive activity banks’ managers have made the assumption that they know customers needs and expectation. The results of the research show that they quite often concentrate on factors which are not the most important ones for their consumers. Creating the banks’ long‐ term value requires thinking that assumption over and realising that their significance should be graded from the customer perspective. The difference between the banks’ managers and customers’ opinion on the importance of some intangible factors should make banks to rethink their future policy. Fortunately, despite the fact that their significance is underestimated by banks’ managers, the modern distribution channels, the level of services’ modernity,

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Monika Klimontowicz and Janina Harasim innovative products/services and procedures implementation and new ways of providing services is generally graded well by customers. But it is probably the result of rather low clients’ expectations. The higher expectation the largest difference between the grade of significance and grade of the particular factors. It is especially important for banks’ ability to match offer with consumers’ needs and expectation and developing relations

Figure 9: The efficiency of innovative assets’ usage in the process of creating competitiveness.

References Ittner, C. and Larcker, D. F. (1998) “Are non‐financial measures leading indicators of financial performance? An analysis of customer satisfaction”, Journal of Accounting Research, No. 2, pp.138–144. Jagelavicienie, A. and Stravinskiene, J. and Rutelione, A. (2006) “Image Factor which Determine Choice of the Bank”, Engineering Economics, No 4, pp. 87‐89. Kaynak, E. and Harcar, T. D. (2004) “American consumers’ attitudes towards commercial banks. A comparison of local and national bank customers by use of geodemographic segmentation”, International Journal of Bank Marketing, Vol. 23, No. 1, pp. 73‐89. Klimontowicz, M. (2010) “How intangibles affect the Polish consumers’ decisions on the banking market”, Paper read at 2nd European Conference on Intellectual Capital, ISCTE Lisbon University Institute, Lisbon, Portugal Klimontowicz, M. (2011) “The concept of intellectual capital in a bank”, Paper read at European Financial System 2011 Conference of Masaryk University, Brno, Czech Republic, June th Klimontowicz M. (2012) “Banks’ Intangibles in Developing Relationships with Young Customers” Paper read at 13 European Conference on Knowledge Management, ECKM 2012, Universidad Politecnica de Cartagena, Cartagena, Spain Knox, S. (2004) “Positioning and branding your organization”, Journal of Product & Brand Management, Vol.13, No. 5, pp. 105‐115. Korenik, D. (2006) Innowacyjne usługi bankowe, Wydawnictwo Naukowe PWN, pp. 15‐31. Loveman, G. W. (1998) “Employee satisfaction, customer loyalty and financial performance: An empirical examination of the service profit chain in retail banking”, Journal of Service Research, No. 1, pp. 18–31. Porter, M. E. (1998) On Competition., Harvard Business Review, pp.40‐42. Rogowski, W. (2006) Kapitał intelektualny jako generator nowych czynników konkurencyjnych, [w:] Kasiewicz S., Rogowski W., Kicińska M., Kapitał intelektualny. Spojrzenie z perspektywy interesariuszy, Oficyna Ekonomiczna, Kraków Skinner, C. (2008) Technology: Shaping Tomorrow ‐ Fundamentally Flawed Thinking ‐ It is High Time That Retail Banks Stopped Thinking of Electronic Channels as Extensions of the Branch and Started Thinking About the Electronic Structure as the Foundation upon which All the C, The Banker, FT Business, http://www.highbeam.com

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A Structural Model for Social Capital in Banks based on Quality of Work Life Anahita Madankar and Fattah Nazem Department of Education, Roudehen Branch, Islamic Azad University, Roudehen, Iran anahitamadankar@ymail.com nazem@riau.ac.ir

Abstract: The purpose of the present study was to provide a structural model for social capital in banks based on quality of work life. The population of the research included all the employees of Tejarat Banks located in Tehran, the capital city of Iran. Hence, 600 employees were selected using stratified and cluster random sampling. The instruments used were two questionnaires: Abili and Abilis’ (2011) 24-item questionnaire measuring social capital and its three key cognitive, relational, and structural dimensions with Cronbach Alpha of 0.98 and Walton’s (1973) 29-item quality of work life questionnaire with eight underlying constructs of safe and healthy working condition, future opportunity for continued growth and security, constitutionalism in the work organization, the social relevance of work life, work and the total life space, social integration in the work organization, immediate opportunity to use and develop human capacities, and adequate and fair compensation with Cronbach Alpha of 0.93. The results of path analysis using LISREL software indicated that dimensions of the quality of work life had a direct effect on social capital with the indices of 0.87. Furthermore, the model showed that out of the factors of the quality of work life the factor of constitutionalism had the highest direct effect on the cognitive construct of the social capital. Overall, the proposed model showed full fit. Keywords: social capital, quality of work life,structural model, banks

1. Introduction and purpose of the study Social capital is considered a crucial and vital ingredient in the development of economic institutions (Granovetter, 1985; Requena, 1991; De Graaf & Flap, 1988). A vast variety of social processes, patterns and practices determine the social capital of a social unit, including social support, integration, social cohesion (Lin et al., 2001), team work, density of exchanges with colleagues (Oh et al., 2004), reduced probability of opportunism, cost of monitoring (Putnam,1993), encouraging cooperative behavior, facilitating the development of new forms of association and innovative organization (Fukuyama,1995; Putnam,1993) resolving disputes (Varshney, 2000), taking advantage of new opportunities (Isham,1999), and featuring the structure, not of the individual actors within the social structure; an ecologic characteristic.(Lochner et al.,1999). The concepts of social capital seem to have been classified in to three different groups: 

Cognitive dimension: The cognitive dimension of social capital refers to attributes like a mutual belief or shared paradigm that promotes a common understanding of collective goals and the proper ways of acting in the social environment (Tsai & Ghoshal, 1998). The social capital's cognitive dimension may enable knowledge sharing in the sense that stories, shared language, customs and traditions can bridge the tacit-explicit division as well as division in terms of, for example, old-timers-newcomers (Hinds & Pfeffer, 2003). The cognitive dimension refers to those resources that provide shared representations, interpretations, and systems of meaning among parties. This includes shared language and codes as well as shared narratives, which increase the mutual understanding among individuals and help members communicate more effectively. (Cabrera & Cabrera, 2005).



Structural dimension: The structural dimension of social capital focuses mainly on the density of networks and on bridging structural holes (Burt, 1992; Wasserman & Faust, 1994). Structural social capital facilitates information sharing, and collective action and decision making through established roles, social networks and other social structures supplemented by rules, procedures and precedents. (Uphoff, 2001).



Relational dimension: McDonald (2000) has tried to include a motivational element into the design of expertise recommender systems. He augmented an expert recommendation system with social networks. Therefore, the recommender system would suggest first those experts who had the closest social ties with the person asking.

Quality of Work Life (QWL) is one of the most major issues in every organization, including job security, better reward systems, higher pay, opportunity for growth, participative groups, increased organizational

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Anahita Madankar and Fattah Nazem productivity, and a good indicator to boosts its image in attracting and retaining employees (Beauregard, 2007), Quality of Work Life can be defined with regard to the employees’ satisfaction, work related behaviors (Bagtasos, 2011), the attempt to develop more satisfying work conditions through the collaborative efforts of management and employees (Luthans, 2005), job security, stability and continuance of that job (Probst, 2003). Attracting and retaining the employees (Akdere, 2006), and improving one’s work to satisfy the personal needs. The techniques and approaches to improve the quality of work life are as followings: job enrichment, self-managed teams, and labor-management committees (Davis & Cherns, 1975), enhancing the performance of the employees and organizations, reducing absenteeism, minor accidents, grievances, and quitting (Havlovic,1991), job satisfaction and performance (Mosharraf & Islam,1999), shedding light on task content and physical features of the job (Kalra and Ghosh 1984; Kahn ,1981), which can be regarded as supports to fulfill the technical and social requirements of the job in our organizations (Adhikari & Gautam, 2010). One conceptualization of quality of work life, based on the need-hierarchy theory of Maslow, regards quality of work life as employee satisfaction of seven sets of human developmental needs: which are health and safety needs, economic and family needs, social needs, esteem needs, actualization needs, knowledge needs, and esthetic needs (Marta et al., 2011). Walton (1973) isolated eight variables related to the quality of work life: 

Adequate and Fair Compensation: The major and initial impetus for employment is earning a living. How well that aim is achieved fundamentally affects the quality of working. More than any other criteria, adequacy of compensation is a relative concept and there simply is no consensus on the objective or subjective standards for judging the adequacy of compensation. Fairness in compensation, on the other hand, has various operational meanings. Job evaluation specifies the relationships between pay and factors such as training required, job responsibility, and noxiousness of working conditions.



Safe and Healthy Working Conditions: It is widely accepted in our society that workers should not be exposed to physical conditions or hourly arrangements that are unduly hazardous or detrimental to their health. Legislation, union action, and employer concern have resulted in continually rising standards of satisfactory working conditions.



Immediate Opportunity to Use and Develop Human Capacities: The industrial revolution and a simplistic extension of its underlying logic have taken much of the meaning out of work. Work has tended to be fractionated, deskilled, and tightly controlled. The planning of work has been separated from its implementation. These tendencies have progressed in varying degrees from one job to the next; therefore, jobs differ in how much they enable employees to use and develop their skills and knowledge.



Opportunity for Continued Growth and Security: Here the focus shifts from the job to career opportunities. Although the opportunity for self-improvement through education and hard work has been considered an American birth right, the typical industrial job can now be completely learned within a few weeks or a few years, after which the blue-collar worker has reached nearly the peak of his earnings and can look forward to only minor improvements.



Social Integration in the Work Organization: The preceding categories relate to the work’s immediate and long-range opportunities of expressing and developing individual abilities. Since work and career are typically pursued within the framework of social organizations, the nature of personal relationships becomes another important dimension of the quality of working life. Whether the worker has a satisfying identity and experiences self-esteem will be influenced by the attributes in the climate of his work place such as freedom from prejudice, egalitarianism, mobility, supportive primary groups, community, and interpersonal openness.



Constitutionalism in the Work Organization: The labor unions have brought constitutionalism to the work place to protect employees from arbitrary or capricious actions by employers. In unorganized employment, there are wide levels of variation in the extent to which the organizational culture respects personal privacy, tolerates dissent, adheres to high standards of equity in distributing organizational rewards, and provides for due process in work-related matters. The following aspects of constitutionalism are key elements in providing higher quality to working life: Privacy, free speech, equity, due process.



Work and Total Life Space: The relationship of work to the total life space is best expressed by the concept of balance. The balanced role of work is defined by work schedules, career demands, and travel

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Anahita Madankar and Fattah Nazem requirements that do not take up leisure and family time on a regular basis. Likewise, balance refers to advancement and promotion that do not require repeated geographical moves. 

Social Relevance of Work Life: The socially beneficial roles of employing and the socially injurious effects of its activities have increasingly become salient issues for employees. Organizations which are seen to be acting in a socially irresponsible manner will cause increasing numbers of employees to depreciate the value of their work and careers, which in turn affects worker self-esteem. (Walton, 1973, 12-16).

Quality of Work Life was conceptualized in terms of need satisfaction stemming from an interaction of workers' needs of survival, social needs, ego needs, self-actualization needs and those organizational resources relevant for meeting them (Efraty & Sirgy, 2004). Satisfaction shows that it corresponds to a psychological state resulting from the difference between the situation in which a person finds himself or herself and the situation in which that person wishes to be (Locke, 1976; Quilty et al., 2003). Quantity and quality of leisure time created by the job (Kirkman, 1981), and the recognition of the quality of work life will lead to productivity and higher job performance, (Heskett, Sasser, & Schlesinger, 1997), commitment between the needs and development of the individual, the goal and development of the organization (Ivancevich, 2005). Since today’s life demands are quite stressful, quality of work life is important as it contributes to the environment as well as family structure by offering ways to fulfill individuals responsibilities (Bagtasos, 2011). According to Eurostat (2000), knowledge society is characterized by the relevant growing of intangible assets and social activities; due to this factor, social capital is one of the forms of capital of the World Bank classification that is acquiring the greatest level of importance. Lesser and Cothrel (2001) note that, social activities have an eminent role in the knowledge-based economy. There are a set of critical resources that enable the creation of essential competences out of which, social activities increase the capacities for the creation, sharing and management of knowledge generating sustainable competitive advantages (Bueno, 2002). Lazerson (1995) remarks that social capital solves conflicts, improves consensus with surrounding organizations, enhances the understanding with public administration, supports the development of business strategy, mitigates the imperfections of information in the market, and reduces transaction costs. In today’s complex, competitive world, increasing the social capital is necessary to guarantee organizational survival and banks competitive advantage. The research purpose is to construct a structural model of social capital in the banking business of Iran based on quality of work life.

2. Research questions 

What is the structural model of social capital based on quality of work life in Banks?



Which variable does have the highest effectiveness on social capital?



How predictive is the quality of work life in terms of promoting social capital?



How much is the goodness of fit in this study?

3. Method of the study The research methods which were used in this study are: library research to access the theoretical framework and the related literature; and the survey method to collect, classify, describe, and analyze the data. The population of the research included all the employees working in Tejarat Banks of Tehran city in Iran. In order to estimate the volume of the sample, using stratified and cluster random sampling.

formula was used. Therefore, 600 employees were selected

The research instruments were two questionnaires: Abili and Abilis’ (2011) 24-item questionnaire measuring social capital and its three key cognitive, relational, and structural dimensions with Cronbach Alpha of 0.98 and Walton’s (1973) 29-item quality of work life questionnaire with eight underlying constructs of safe and healthy working condition,, Future opportunity for continued growth and security, constitutionalism in the work organization, the social relevance of work life, work and the total life space, social integration in the work organization, immediate opportunity to use and develop human capacities, and adequate and fair compensation with Cronbach Alpha of 0.93. The results of the study were calculated through path analysis using LISREL software.

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4. Findings of the study The data collected from the administration of the instruments were analyzed. These data included the different indexes of central tendency, variability and the distribution of staff’s groups, the staff members’ scores obtained from social capital and quality of work life and their related components. The distribution of the staff members’ scores in the given variables had tendency toward normality.

Figure 1: Path analysis model for components of quality of work life and social capital As shown in Figure 1, the Lambda rate of external latent variable of quality of work life components was 0.82 for adequate and fair compensation,0.89 for safe and healthy working conditions,0.91 for future opportunity for continued growth and security,0.92 for constitutionalism in the work organization,0.87 for the social relevance of work life,0.88 for work and the total life space,0.91 for social Integration in the work organization, and 0.89 for immediate opportunity to use and develop human capacities. it’s worth mentioning that their accumulation form the quality of work life variable with the effectiveness rate of 0.87. It means that 87% of the variation in the dependant variable of employees' social capital is explained by a collection of these indices. The variable of constitutionalism in the work organization indicates the highest amount of internal consistency in the external latent variable. The Lambda rate of internal latent variable of social capital components was 0.98 for cognitive,0.97 for relational , and 0.97 for structural. Their accumulation form the social capital variable. The cognitive and relational of variable indicates the highest amount of internal consistency in the internal latent variable. Since the model’s goodness of fit index is 0.94, it can be stated that it has an acceptable fit. The calculated index indicates the direct effect of quality of work life components on social capital. Moreover, the model shows that the highest direct effect is related to constitutionalism in the work organization. The following table presents the indices related to the model’s fit. Table 1: Model’s fit indices Index Lewis-Tucker (Non-normed fit index) Bentler-Bonett’s (Normed fit index) Hoelter Root Mean Square Error (RMSE) GFI

Rate 0.91 0.91 0.73 0.033 0.94

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Interpretation High fit (more than 0.90) High fit (more than 0.90) High fit (more than 0.70) High fit (equal to or less than 0.05) High fit (more than 0.90)

Anahita Madankar and Fattah Nazem The five goodness of fit indices presented model’s fit and empirical data. Therefore, desirability adaptation is provided for the designed model and empirical data and can approve it as an appropriate model for the social capital.

5. Discussion and conclusions The results of path analysis method revealed that dimensions of employees' quality of work life impact on social capital. The results of the present study is in line with the results of the studies by Chitakornkijsil (2010), Taherian (2011), Farahani (2009), James (2008), Alinezhad (2008), Liukkonen et al (2004), Requena (2003), and Lowe and Schellenberg (2001). Chitakornkijsil (2010), studied various aspects of quality of work life issues, such as fair treatment for employees, working time, collective bargaining, the roles of employees, and social responsibilities of the organization. He concluded that mass consumption, productivity, socio-cultural and economic factors affects the quality of work life. Taherian et al (2011) carried out a study on the effect of social capital and quality of work life, and results demonstrated that out of the eight components of the quality of work life the factor of constitutionalism had the highest direct effect on the cognitive construct of the social capital. Farahani's (2009) study on the role of social capital in the working environment and productivity concluded that social capital is considered an important source of productivity in business organizations. If employees feel to be receiving high level of organizational support and that their employers treat them well, they are likely to behave reasonably. A similar study by Alinezhad (2008) reported that social capital is positively correlated to the quality of work life in teaching research center located in Shiraz. Social capital was also found of having predictive value in predicting the quality of work life. Liukkonen et al. (2004) investigated social capital as a workplace characteristic that can potentially affect employees’ health. They used the indicator of trust, both in job security and in co-worker support to determine the extent to which social capital exists. The researchers determined that a high level of social capital existed for people who had a high level of coworker support. Requena (2003) published an article entitled “social capital, satisfaction and quality of life in the workplace in Spain”. In this study, social capital has been defined as the set of cooperative relationships between social actors that facilitate collective action. The results of the analysis indicate that the models applied are significant, which confirms the examined propositions. Higher levels of social capital imply greater levels of satisfaction and quality of life at work. Social capital is a better predictor of the quality of life at work and job satisfaction than the characteristics of the worker, the company or organization, and the work environment. Lowe and Schellenber (2001) have outlined a model for Canada that causally links social capital with the level of satisfaction and well-being generated by a work position. They examined how trust, commitment and social relations affect workers' personal and subjective achievements. In addition, it was found that both at individual and organizational levels social capital predicts greater work achievements. Regular assessment of the quality of work life can potentially provide organizations with important information about the welfare of their employees such as job satisfaction, work-family balance, job security and job stress. (Sulaksha & Harisha, 2012). Quality of Work Life is a comprehensive and expanded program that increases member satisfaction, reinforces their learning with the environment, and helps them to manage change. Member dissatisfaction of quality of work life is a problem that harms all employees–without considering rank and situation. The aim of many organizations is increasing members’ satisfaction in all levels. However, this is a complex problem, because determining the related factors to quality of work life is difficult (Seraji, 2006). Social capital exists in the relationships between and among persons and extends the more that the position one occupies in the social network constitutes a valuable resource (Friedman and Krackhardt, 1997). The importance of social capital lies in that it brings together several important sociological concepts such as social support, integration and social cohesion. Social capital also relates to norms and values. Another quality is it's easy operationalization in economic activities and organizations (Lin et al., 2001). According to many analysts, such as Portes (1998) and Putnam (2000), it helps people resolve collective problems more

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Anahita Madankar and Fattah Nazem easily, facilitates development, heightens awareness of our globally connected fates, fosters the flow of useful information and helps people cope with improve their health, find jobs and maintain businesses.the structure of individuals' contact networks - the pattern of interconnection among the various people with whom each person is tied (Raider and Burt, 1996, p. 187). also found that social capital is positively related to promotions and career satisfaction( Seibert et al.,2001). In the present study, social capital was discussed as an important factor in the information age. The results of the present research supported the direct effect of the quality of work life upon the social capital, Moreover, since the model showed that out of the factors of the quality of work life the factor of constitutionalism had the highest direct effect on the cognitive construct of the social capital it is suggested that: 

Employees should be given the opportunity to freely express opinions.



The organization should employ modern methods for voicing and dealing with complaints.



Employees have a clear understanding of organizational goals.



Staff should receive emotional support from their superiors.



The staff should aim to reach acceptable agreements.

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A Similarity and compatibility of personal values and goals should exist.



There should be a desire to achieve organizational goals.



Shared values and goals should be determined and shared among the employees.

In conclusion, the newly-proposed results in this research can be effectively employed to enhance the social capital in similar organizations.

Acknowledgements The authors want to extend a heart-felt thank you to the members of Tejarat bank Managers for their commitment and efficient research assistance. They are truly appreciated as their partnership was vital to carrying out this research.

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The Effect of Intellectual Assets and Intellectual Liabilities Disclosure on Financial Performance: An Empirical Analysis of Publicly Listed Companies in the United Arab Emirates George Majdalany1 and Jeffrey Henderson2 1 Faculty of Accounting and Finance, UGSM‐Monarch Business School Switzerland, Zug, Switzerland 2 Faculty of Business Ethics, UGSM‐Monarch Business School Switzerland, Zug, Switzerland george.asaad.majdalany@ugsm‐monarch.ch dr.henderson@ugsm‐monarch.ch Abstract: Financial reporting is an important, crucial task for achieving and sustaining a well‐organized, farsighted business. Furthermore, investor demand for relevant information and improved quality and timeliness of financial information is increasing in the face of deteriorating usefulness of traditionally reported earnings, cash flows, and equity values. Thus, many accounting industry practitioners, analysts, and researchers now see Intellectual Capital (IC) as a driver for a firm’s long term business competitiveness. However, most researchers have overlooked the negative side of IC which is referred to as Intellectual Liabilities (IL). Therefore, the objective of the present study is to explore through empirical analysis how Intellectual Assets (IA) and IL components, as independent variables, influence Firm Financial Performance (FFP) as a dependent variable. The present study uses content analysis of 2010 and 2011 annual reports for all publicly listed companies in the United Arab Emirates (UAE). Multivariate regression analysis is employed to answer the research question: What are the characteristics of a new conceptual model that assists in explaining the relationships between IC disclosure and FFP for companies listed on the UAE stock exchanges? The findings indicate a statistically positive relationship between Human Capital (HC), Relational Capital (RC), Structural Capital (SC), Human Liabilities (HL), Relational Liabilities (RL), and Structural Liabilities (RL) on one hand, and Return on Equity (ROE) on the other hand. However, this study has some limitations which include the restrictions inherent in the content analysis method, in addition to the external validity to other jurisdictions due to the sample being chosen from the UAE only. In terms of practical implications, the findings of this study provide an insight to firm managers on the impact of increased transparency and disclosure on FFP. Keywords: intellectual capital, intellectual assets, intellectual liabilities, disclosure, financial performance

1. Introduction Financial reporting is an important and crucial task for efficient decision making and for achieving and sustaining a well‐organized, farsighted business (Lev and Zarowin, 1999; Oyelere and Kuruppu, 2010). According to Lev and Zarowin (1999), investor demand for relevant information and improved quality and timeliness of financial information is increasing in the face of deteriorating usefulness of traditionally reported earnings, cash flows, and equity values. A ‘new economy', characteristically driven by information and knowledge, is emerging quickly in the twenty‐ first century (Joshi and Ubha, 2009). This ‘new economy’ differs drastically from the ‘old economy’, prompting many researchers to adopt the nomenclature “Knowledge Economy” when referring to this phenomenon (Joshi and Ubha, 2009). Therefore, driven by innovation, changes in firms operations and economic conditions, IC is not considered to be adequately reflected by the current financial reporting system (Gerpott et al., 2008). Consequently, and as IC is believed to bridge the gaps in traditional financial reporting, it is understood that the burgeoning prominence of IC in accounting practice and research is the major driver of the knowledge economy (Marr, 2004; Chen et al., 2004). The significance of IC as a leading value driver in the contemporary knowledge economy cannot be disputed (Marr, 2004). Thus, many accounting industry practitioners, analysts, and researchers now see IC as a major determinant of enterprise value (Stam, 2009). However, to date, the majority of contemporary research ignores IL, equating IC with IA (Stam, 2009); hence, the contribution of the current study in exploring the impact of IA and IL disclosure on FFP. In addition to the scarcity of IC research in the UAE, most research efforts have concentrated on the assets side and ignored the liabilities side of IC. Thus, the significance of the present research is to examine the field

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George Majdalany and Jeffrey Henderson of IC research in publicly listed companies in the UAE, to examine the field of IL research, and to generate a conceptual model that assists in understanding the relationship between ICD (IA and IL) and FFP. Using content analysis and multivariate regression analysis, the present study examines the association between ICD (HC, RC, SC, HL, RL, and SL) and FFP using the 2010 and 2011 annual reports of all publicly listed companies in the UAE. The remainder of this article is structured as: literature review and hypotheses development, research design and methodology, data presentation, statistical analysis and results, and finally, conclusions, limitations, and implications for future research.

2. Literature review A major theme of strategic management focus is organizational performance (Galbreath and Galvin, 2008). This focus has passed through a series of transformations from industrial to knowledge‐based. This focus has evolved from an industry‐specific into firm‐specific focus, both of which are notable dominating paradigms in the strategic management field (Galbreath and Galvin, 2008). With the industry‐specific theory evolving into firm‐specific, the factors of organizational performance, which view the firm as a bundle of tangible and intangible resources, have become the priority of managers (Galbreath and Galvin, 2008). With the understanding that the value, scarcity, uniqueness, and sustainability of these resources prompt a competitive advantage for the firm, researchers seem to have a common view that these resources are the major drivers of the differentials in FFP (Galbreath, 2005; Galbreath and Galvin, 2008). Therefore, this understanding is represented by the Resource Based View (RBV). With more scholars researching the RBV further, they have increasingly suggested that an organization’s intangible resources are the actual drivers of its competitive advantage because of their peculiarity, high scale returns, and difficult barriers to duplication (Carlucci et al., 2004, Tseng and Goo, 2005). The integration of the RBV and the Competence Based View (CBV) paved way for the Knowledge Based View (KBV), which finally led to development of the Intellectual Based View (IBV) (Carlucci et al., 2004, Tseng and Goo, 2005). The CBV proposes that competitive advantage can be attained through the competent management of the unique resources, as highlighted by the RBV (Conner and Prahalad, 1996). The KBV regards these resources as the sources of strategic competitive advantage (Nonanka and Takeuchi, 1995). The KBV also states that competitive advantage is dependent on the organization’s ability to handle knowledge in terms of growth, management, measurement, and control (Rubino, 2004). Finally, the KBV evolved into the IBV, which presents competitive advantage and FFP as a function of the organizational movement of knowledge stocks (Carlucci et al., 2004, Tseng and Goo, 2005). Consequently, the knowledge economy is characterized by sources of economic value relying on the managers’ ability to properly manage, measure, and disclose the firm’s IC (Guthrie et al., 2004).

2.1 Intellectual liabilities There is a large body of literature regarding ICD as an asset to the firm. However, it seems that most studies have ignored IL (Stam, 2009). The major gap in understanding of IC is the complete misunderstanding of net intellectual worth, which in reality equals IA minus IL (Stam, 2009). The reasons why emphasis has not been placed on the existence of IL in previous studies on IC include:

Absence of regulatory framework for ICD (Abeysekera, 2003); and

Poor understanding, inadequate identification, inefficient management, and inconsistent disclosure of key IC components (Brennan, 2001).

According to Harvey and Lusch (1999), Caddy (2000), and Abeysekera (2003), revealing a firm’s true hidden values includes IC measurement practice that should account for both IA and IL. Considering that the current understanding of IC has practically failed to incorporate IL, exploring a more holistic definition of IC is the next logical step (Stam, 2009). According to Stam (2009), the difference between the book value of a company’s assets and liabilities is the company’s book value of equity. Similarly, the company’s market value can be regarded as the difference between the market’s value of the company’s assets and liabilities (Stam, 2009). Therefore, it follows logically that the difference between the company’s market value of equity and book value must be significant (Stam, 2009). Researchers describe this difference between the company’s market value of equity and book value of

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George Majdalany and Jeffrey Henderson equity as its unrecorded IA (Cañibano et al., 2000, Abeysekera, 2003). Following this line of reasoning, the difference between the company’s market and book value consists of IA and IL (Harvey and Lusch, 1999; Caddy, 2000; Stam, 2009). To balance IC reporting, Stam (2009) proposes that it is necessary to redefine the concept of IC itself. According to Stam (2009), if the equation “IC = IA – IL”, as proposed by Harvey and Lusch (1999) and Caddy (2000) is true, the same equation will be correct for a variety of IC categories. In other words, the same equation can be extrapolated into each of the different categories of IC. Therefore, Stam (2009) maintains that it will be inaccurate to take the summation of all HA as a firm’s HC; he recommends that the HC should be derived by deducting the summation of all HL from the summation of all HA. The same idea is applied in determining the SC and RC. These definitions are an extension of the framework suggested by Bontis (2002). Therefore, Stam’s (2009) definition for IC components is summarized as: IC = HA + RC + SC – HA – RL – SL. HL are referred to as causes of deterioration arising from the personnel within the firm, the employees, employees’ tacit knowledge, employees’ skills, employees’ experience, and employees’ attitude (Harvey and Lusch, 1999; Stam, 2009). RL are defined as causes of deterioration arising from relationships between the firm and its customers, suppliers, or other external stakeholders (Stam, 2009). SL are defined as causes of deterioration arising from the non‐human resources within the firm (Stam, 2009). In other words, they refer to those value creation sources that persist, after the employees have quit the firm (Stam, 2009). An important synopsis of SL provided by Stam (2009) is as follows: "The liability of newness, the liability of smallness, group think, top management homogeneity, long management tenure, and past performance." According to Caddy (2000) and Garcia‐Parra et al. (2009), and despite the fact that several studies have attempted to measure the disclosure of IC items, there are no known studies that have attempted to measure disclosure of IL or the impact of disclosure on FFP.

2.2 Intellectual capital and financial performance Several studies have identified positive and negative effects of ICD on FFP (Dammak et al., 2010; Van der Wielen, 2010). In terms of positive effects, ICD is largely justified based on the value creation notion of the knowledge economy (Wyatt, 2008; Yaghoubi et al., 2010; Curado et al., 2011). This implies that it is advisable for a firm to disclose its IC for the following reasons (Yaghoubi et al., 2010):

Better appeal to investors;

Reduction of cost of borrowing; and

Winning or sustaining customers’ confidence and key human resources

On the negative side, it can be reasoned that disclosure in itself is a costly, lengthy process (Wyatt, 2008; Van der Wielen, 2010), which can subject the firm to a financial outlay drawback (Van der Wielen, 2010). The outlay may be monetary forms or in the form of opportunity cost of the loss of time to arrange, review, mechanize, and publish such information (Van der Wielen, 2010). However, on the positive side, the claim above is debatable based on the fact that if IC is measureable, then it is manageable (Roos and Roos, 1997; Andriessen, 2004). Furthering this argument, if IC can be managed, and if it can be improved, then competitive advantage is possible; ultimately, competitive advantage will convert into financial superiority (Wyatt, 2008). Several authors are adamant in their belief that ICD has a positive effect on FFP (Wyatt, 2008; Dammak et al., 2010; Falikhatun et al., 2010). Often, this performance is defined by profitability, which is a reflection of the capability of the invested capital to earn some measure of profit (Van der Wielen, 2010). Applying the RBV, Chen et al. (2005) have argued that if IC is a valued resource for a firm’s competitive advantages, it will add to the financial performance of that firm. This belief is also shared by other studies (Youndt et al., 2004), which state that IC intensive firms are more competitive than other firms and therefore, tend to be more successful. However, and despite the fact that some studies (Harvey and Lusch, 1999; Garcia‐Parra et al., 2009; Stam, 2009) recognize IL, very little work has been done, and what has been done was mostly conceptual, rather than empirical; hence, the need for further conceptualization of IL into IC, and to examine empirically IL disclosure and impact on FFP.

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2.3 Hypotheses development In light of the conducted literature review, a specifically defined research question has been developed as: “What are the characteristics of a new conceptual model that assists in explaining the relationships between ICD and FFP for companies listed on the UAE stock exchanges?” In response to the main research question, the following hypotheses were developed: Table 1: Null hypotheses

H10

There is no statistically significant relationship between ICD and Firm Financial Performance for publicly listed companies in the UAE.

H20

There is no statistically significant relationship between HC Disclosure and Firm Financial Performance for publicly listed companies in the UAE

H30

There is no statistically significant relationship between HL Disclosure and Firm Financial Performance for publicly listed companies in the UAE

H40

There is no statistically significant relationship between SC Disclosure and Firm Financial Performance for publicly listed companies in the UAE

H50

There is no statistically significant relationship between SL Disclosure and Firm Financial Performance for publicly listed companies in the UAE

H60

There is no statistically significant relationship between RC Disclosure and Firm Financial Performance for publicly listed companies in the UAE

H70

There is no statistically significant relationship between RL Disclosure and Firm Financial Performance for publicly listed companies in the UAE

Source: Authors

3. Research design and methodology A quantitative research approach, using statistical analysis of content analysis findings, is applied to examine the relationships between ICD and FFP for publicly listed companies in the UAE for the fiscal years 2010 and 2011.

3.1 Target population and sampling method The stock markets of the UAE consist of two exchanges: Dubai Financial Market (DFM) and Abu Dhabi Securities Exchange (ADX). As of January 2012, the total number of listed companies in the UAE stock market was 124; 66 in ADX and 58 in DFM; the companies listed on the DFM are mostly Emirati companies in addition to some secondary listings from the neighboring Gulf countries (DFM, 2012; ADX, 2012). As displayed in Table 2, the UAE ranks third in the Gulf Cooperation Council (GCC) region in terms of number of publicly listed companies; hence, the relative importance of the stock markets in the UAE. Table 2: Listed firms in the GCC Stock Market

Number of Listed Firms

Kuwait

195

Kingdom of Saudi Arabia

161

United Arab Emirates

124

Oman

123

Qatar

Bahrain

Total GCC

431

Source: Bloomberg, 2012

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George Majdalany and Jeffrey Henderson In addition, and as shown in Table 3, the UAE accounts for the second largest marketplace in the Middle East in terms of market capitalization. Table 3: Arab market capitalization in2012 Country

p Market Capitalization in Billions of USD

Kingdom of Saudi Arabia

397.0

United Arab Emirates

125.0

Qatar

117.0

Kuwait

102.7

Others

67.4

Morocco

61.5

Egypt

61.2

Jordan

27.0

Syria

1.4

Total Arab Market Capitalization

294.2

Source: IMF, 2012

To ensure the external validity and the generalizability to the population of interest, Hair et al. (2009) opines that the researcher needs to adequately respond to the question of whether the survey results would have remained the same if a response rate of 100% had been attained. In the current study, the researchers have responded to this concern by studying 100% of the sample. Therefore, the present research is superior to the majority of existing ICD studies in the fact that it has used 100% of the population for content analysis. The choice of 100% sample population closes a gap in the literature, which is the small sample of firms being studied relevant to the population of publicly listed companies (Abeysekera, 2007).

3.2 Annual reports and content analysis Although it can be argued that all forms of external communication of an organization should be monitored if a researcher wants to capture all IC reporting, the difficulty is that it is impossible to determine with certainty that all communications are taken into consideration (Gray et al., 1995). Therefore, annual reports were chosen for three reasons (Lang and Lundholm, 1993). First, they are considered an important source of company information by internal and external users (Guthrie and Petty, 2000; Abeysekera, 2007). Second, the level of disclosure in annual reports has a positive correlation with the volume of corporate information communicated to the market (Guthrie and Petty, 2000). Third, annual reports are produced on a regular basis, usually yearly, and as such, they provide an opportunity for meaningful comparisons and analysis (Niemark, 1995). To ensure full representation of all IC indicators, IC terms were grouped into six categories, with a total of 498 terms, based on the frameworks of Harvey and Lush (1999), Guthrie and Petty (2000), Bozzolan et al., (2003), Guthrie et al. (2004), Abeysekera and Guthrie (2005), Stam (2009), and Yi and Davey (2010) as represented in Table 4: Table 4: Intellectual capital categories Category

Number of Terms

Human Capital

158

Relational Capital

133

Structural Capital

146

Human Liabilities

Relational Liabilities

Structural Liabilities

Total

498

Source: Harvey and Lush (1999); Guthrie and Petty (2000); Bozzolan et al., (2003); Guthrie et al. (2004); Abeysek era and Guthrie (2005); Stam (2009); Yi and Davey (2010)

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George Majdalany and Jeffrey Henderson Content analysis is applied for the classification of annual reports contents according to IC terms and the frequency at which they appear; hence, the use of semantical content analysis method. This technique is in line with the methods defined and applied by Abeysekera (2003). To ensure objectivity, which is usually associated with the allocation of different weights to various IC categories, a 0 ‐ 1 coding scheme was used, following the set of coding rules. Nevertheless, other coding schemes exist, such as the 4‐point coding scheme as proposed by Guthrie and Petty (2000), the 5‐point by Beaulieu et al. (2002), and the 3‐point by Bozzolan et al. (2003). However, the 0 ‐ 1 coding scheme because applying a coding scheme with more points results in high subjectivity (Williams, 2001).

3.3 Independent and dependent variables In IC studies, performance variables are time specific, representing information or a set of details that may interest the users of accounting information (Wallace et al., 1995). Consequently, researchers often apply variations of profit margins, Return on Equity (ROE), Return on Assets (ROA), current ratio, or VAIC™ as measures of performance (Zéghal and Maaloul, 2010). To carry out the necessary analysis in this study, the dependent variable ROE was used as proxy for FFP. ROE is defined as the ratio of Net Income divided by average Total Equity (Total Assets minus Total Liabilities). The use of ROE as a proxy for performance in this study is justified based on the fact that ROE reflects the resource utilization efficiency of the firm as an indicator of profitability and overall performance. In addition, investors and potential investors apply this ratio to assess a firm‘s leadership and efficiency in converting every unit of their funds into value. Therefore, ROE in accounting is generally accepted as a valid and veritable measure of the overall performance of the firm (Core et al. 1999) as it provides the relevant details about the value added to the firm that causes better performance (Zéghal and Maaloul, 2010). Table 5 includes the dependent and independent variables, their definitions, and the proxies used in measurement. Table 5: Independent and dependent variables Independent Variables Human Capital

Code HC

Definition Tacit knowledge embedded in the minds of the employees

Relational Capital

Knowledge embedded in the relationships established with the outside environment

Structural Capital

Organizational routines of the business

Human Liabilities

Sources of deterioration arising from human resources with the organization

Relational Liabilities

Causes of deterioration arising from relationships with external stakeholders

Structural Liabilities

Causes of deterioration from internal non-human resources

Dependent Variable Return on Equity

Code ROE

Definition Net Income divided by average Total Equity (Total Assets minus Total Liabilities)

Source: Authors

3.4 Data collection The present study uses the 2010 annual reports of the 124 firms listed on the UAE stocks market as its source of information principally because of the following:

None of the companies have published the 2012 annual report because year 2012 is still running; and

Annual reports for 2011 are required to check the impact of the disclosure of Intellectual of Capital in 2010 on the financial performance in 2011. Obviously, there exists a time lag between ICD and FFP effect. This is evidenced by several studies that have pointed to an inevitable time lag between increased transparency and disclosure on one hand, and performance on the other; this time lag is generally perceived to be one year (Aksu and Kosedag, 2005)

The 2010 annual reports of 124 publicly listed companies were downloaded in PDF format from the website of each company. The downloaded annual reports were converted to MS Word 2007 format using “ABBYY FineReader 10 Professional Edition”, which is an optical‐character‐recognition (OCR) software. The MS 2007 Word format of each company’s annual report was uploaded to the content analysis software (QDAMINER 4 and WORDSTAT 6), coded, and then electronically codified to extract the disclosure of IA and IL (independent variables) according to the predefined categories. The 2011 annual reports of 124 publicly listed companies were analyzed to extract the ROE for each company. To ensure the validity and reliability of content analysis,

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George Majdalany and Jeffrey Henderson the researchers mitigated the threats of sample size, established information, coding information process, and data recording and interpretation errors.

4. Results and discussion This section includes the statistical analysis of the study, including descriptive analysis and regression output.

4.1 Descriptive analysis Table 6 shows the descriptive statistics of independent and dependent variables: Table 6: Descriptive statistics Variable

N Minimum Maximum

Mean

Std. Deviation

ROE

124

(22.22)

32.24

5.50

124

9.00

46.00

27.69

9.86 7.32

124

2.00

18.00

9.41

3.43

124

7.00

32.00

18.06

5.83

124

16.00

6.79

3.08

124

1.00

8.00

5.12

1.32

124

1.00

9.00

3.63

1.66

Source: Authors

4.2 Regression output The present study follows the four phases of data examination as suggested by Hair et al. (2009), which are graphical examination of variables, missing data analysis, identification of outliers, and assessment of the data to meet the statistical assumptions specific to multivariable regression analysis, including Linearity, Normality, Homoscedasticity, Multicollinearity, and Uncorrelated Error Terms. Multivariate regression technique, through IBM SPSS Statistics 20, is used, which involves linear regression analysis. In this case, the model for regression is specified thus: ROE = β0 + β1 HC + β2 RC + β3 SC + β4 HL + β5 RL + β6 SL + ε 4.2.1 Coefficient of determination (R2) The adjusted R2 in this model is 0.825. The interpretation of R2 is that 82.50% of the variation in ROE is justified by the variation in the independent variables, while 17.50% of the variation is explained by other factors not included in this model, as shown in Table 7: Table 7: Coefficient of determination (R2)) Model

( R

R Square

.913a a. Predictors: (Constant), SL, SC, RL, RC, HL, HC 1

)

Adjusted R Square .833

.825

Dependent Variable: ROE

Source: Authors

4.2.2 F‐Test The regression model shows highly significant results, where the F value (97.576) shows a statistically significant relationship (p = 0.000) between the dependent and independent variables at the 95% confidence level (α = 5%), as displayed in Table 8: Table 8: F‐Test Model

Sum of Squares

Regression 1

Mean Square 6

1,659.453

1,989.79

117

17.007

11,946.51

123

Residual Total

9,956.72

F 97.576

Sig. .000b

Dependent Variable: ROE b. Predictors: (Constant), SL, SC, RL, RC, HL, HC

Source: Authors

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George Majdalany and Jeffrey Henderson 4.2.3 Regression tests After performing all the tests to conform to regression requirements, regression analysis is performed to test all the hypotheses developed in this study. The results are presented in Table 9. Table 9: Regression results Model

Unstandardized Coefficients B

(Constant) HC

Std. Error

Standardized Coefficients

Sig.

Beta

Collinearity Statistics Tolerance

VIF

(37.531)

2.168

(17.310)

0.000

0.646

0.102

0.480

6.327

0.000

0.247

4.043

0.732

0.119

0.255

6.151

0.000

0.828

1.207

0.388

0.134

0.229

2.887

0.005

0.225

4.438

0.584

0.137

0.183

4.257

0.000

0.774

1.291

0.995

0.306

0.134

3.252

0.001

0.843

1.186

0.601

0.250

0.101

2.407

0.018

0.803

1.245

Source: Authors

The intercept β0 has a value of ‐37.531 and a t‐value of ‐17.310, which is significant (p = 0.000) at the 95% confidence level (α = 5%). Table 9 reveals the significant regression coefficients, namely HC (β1) at p = 0.000, RC (β2) at p = 0.000, SC (β3) at p = 0.005, HL (β4) at p = 0.000, RL (β5) at p = 0.001, SL (β5) at p = 0.018. The unstandardized coefficients indicate that for one unit increase in the independent variable, ROE will increase or decrease by that amount. Therefore, for one unit increase in HC disclosure, ROE will increase by 0.732%. From this information, we produce the regression equation as follows: ROE = ‐37.531 + 0.646 HC + 0.732 RC + 0.388 SC + 0.584 HL + 0.995 RL + 0.601 SL + ε

4.3 Hypotheses verification Table 8 also shows a statistically significant relationship between ICD and ROE at α = 5%. Thus, the null hypothesis H10 is not accepted, and H1 is accepted. This result supports the findings of Healy and Palepu (1993), Reese and Weisbach (2002), and Klein et al. (2005), who found statistically significant positive relationship between ICD and firm performance. This also contradicts the findings of Wyatt (2008) and Van der Wielen (2010) who claim that ICD has a negative impact on FFP. Table 9 also shows that HC has a significant t‐value at α = 5%. Thus, the null hypothesis H20 is not accepted, and H2 is accepted. This result supports the findings of Lin and Lin (2006) and Xu et al. (2007) who found statistically significant positive relationship between HC disclosure and FFP. Table 9 also shows that HL has a significant t‐value at α = 5%. Thus, the null hypothesis H30 is not accepted, and H3 is accepted. Since to the best knowledge of the researchers, no previous studies have attempted to study the impact of HL disclosure on FFP, the findings could not be compared to other studies. Table 9 also shows that RC has a significant t‐value at α = 5%. Thus, the null hypothesis H40 is not accepted, and H4 is accepted. This result supports the findings of Ittner and Larcker (1998) and Anderson et al. (2004) which found statistically significant positive relationship between RC disclosure and FFP. Table 9 also shows that RL has a significant t‐value at α = 5%. Thus, the null hypothesis H50 is not accepted, and H5 is accepted. Since to the best knowledge of the researchers, no previous studies have attempted to study the impact of HL disclosure on FFP, the findings could not be compared to other studies. Table 9 also shows that SC has a significant t‐value at α = 5%. Thus, the null hypothesis H60 is not accepted, and H6 is accepted. This result supports the findings of Ittner and Larcker (1998) and Anderson et al. (2004) which found statistically significant positive relationship between RC disclosure and FFP. Table 9 also shows that SC has a significant t‐value at α = 5%. Thus, the null hypothesis H70 is not accepted, and H7 is accepted. Since to the best knowledge of the researchers, no previous studies have attempted to study the impact of HL disclosure on FFP, the findings could not be compared to other studies.

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4.4 Implications for research and practice The present study has both theoretical and practical implications. From a theoretical perspective, this study serves as a reference for further research on ICD and FFP in the UAE. To the best knowledge of the researchers, it is believed that detailed research focused on testing the interactive effects of IC elements on FFP in the publicly listed companies in the UAE was not conducted before. Moreover, by incorporating IL in the framework, this study brings a more refined, theoretically and empirically based conceptualization of IC than those provided so far, aiding in the development of a more robust theory of ICD and its correlation with FFP. The findings support some earlier studies and contradict others, which opens opportunities for academic debates and further research on the subject area. Regarding the practical implications, the findings of this study are expected to provide information for regulatory agencies to develop guidelines in order to increase ICD in the UAE. Furthermore, the present study provides valuable input on the mitigation of agency costs arising from the possibility that managers may not act in the best interest of shareholders. Knowing that IC resources are key drivers of the firm’s value creation process, disclosure of these resources helps investors to better monitor management. The investors’ business comprehension increases with disclosure, lowering the investor risk perception and thus increasing firm value. Mechanisms allowing investors to increase their ability in firm monitoring, as disclosures, increase firm performance and firm value (Healy and Palepu, 1993; Reese and Weisbach, 2002).

5. Conclusions, limitations, and future research The purpose of this study is to analyze the impact of 2010 ICD on 2011 FFP in publicly listed companies in the UAE. The independent variables used as proxies for ICD are HC, RC, SC, HL, HL, and SL. ROE is used as a proxy to measure performance. ICD is measured by a disclosure index supported by word count of metrics using a combination of IC terms commonly used in earlier studies. Results of the analysis show that all independent variables have a statistically positive effect on ROE. Therefore, in the absence of a regulatory framework for ICD UAE, it is shown the ICD, whether IA or IL, positively affects the level of ROE in publicly listed firms. As with other empirical studies, the present study has some limitations. Apart from the limitations of the study, the present research also provides the opportunity for future research. The limitations and the opportunity for further research associated with this study are as follows:

Since the present research studies only the UAE market, this questions the ability to generalize the findings into other jurisdictions; and

There are limitations inherent in the use of the content analysis method; analyzing the annual reports based on the specified list of intellectual related terms may not provide the whole picture; therefore, future research should triangulate the findings of content analysis with interviews with key stakeholders in publicly listed firms.

Despite its limitations, the present study significantly contributes to the literature of IC in several ways. First, it confirms as well as contradicts findings of earlier studies. Second, and to the best knowledge of the researchers, this is the first study in the UAE that examines the impact of ICD (IA and IL) on FFP. The findings offer new insights into these relationships in an institutional context that greatly differs from those of the countries considered in the previous literature on voluntary disclosure. Finally, the choice of a large sample, representing 100% of the population of publicly listed companies UAE, contributed in overcoming the limitations of earlier studies that used a small sample relative to the population.

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Intellectual Capital Development in Business Schools: The Role of “Soft Skills” in Italian Business Schools Maurizio Massaro1, Roland Bardy2, Maria Teresa Lepeley3 and Francesca Dal Mas4 1 DIES, Udine University, Italy 2 DLI, Florida Gulf Coast University, USA 3 GIQE ‐ Global Institute for Quality Education, USA 4 Mas Consulting, Italy maurizio.massaro@uniud.it rbardy@t‐online.de mtlepeley@globalqualityeducation.org francesca@mas‐consulting.net Abstract: Soft skills are recognized as a key issue within the Intellectual Capital (IC) framework for leading companies in a global economy. An increasing number of authors and specialists in IC are emphasizing the specific role of people’ soft competences as a condition to consolidate new and well established business initiatives. At the same time soft competences are recognized as a key element for fostering creativity and innovation. Unfortunately in spite of the increasing number of articles in the field, still there is not a unique definition for “Soft Skills” (SK). At the same time there is no empirical evidence that analyses the state of art of Soft Skills in business schools (BS) education. This paper intends to fill the gap reviewing literature on soft competences in order to identify a possible definition. Several papers have been studied recognizing some concepts. Using a content analysis approach the manifestos of a group of Italian school have been analyzed to recognize the importance that Italian BSs assign to each context. The results of this paper could be used to evaluate the impact and relevance of “Soft Skills” in Italian Business Schools and expedite the need to introduce changes in Knowledge creation strategies a proposal to consolidate teaching of “Soft Skill” programs in Business Schools in Italy. The data could be useful to further studies comparing different attitudes on “Soft Skills” in different countries and cultures. Keywords: intellectual capital, soft skills, business education, content analysis, business schools

1. Introduction and research questions Literature recognizes Intellectual Capital as a central element to foster business companies and strengthen all institutions’ performance and productivity (Brooking, 1999; J. Roos et al., 1998; Saint‐Onge, 1996). A review of the literature on Intellectual Capital (IC) defines Human Capital as the summation of people’s technical knowledge integrated with competences, attitudes, beliefs and behaviors necessary to add value to all business organizations worldwide (Bontis, 2005; Choo and Bontin, 2002; Guerrero, 2003; Jackson and Chapman, 2012; Kong, 2008; Stewart, 1997). An increasing number of authors and specialists in IC emphasize the specific role of people’ soft skills as a condition to consolidate new and advance established business initiatives (S.K. Clinebell and J.M. Clinebell, 2008; Ford and Bowen, 2008; A.Y. Kolb and D.A. Kolb, 2005). Soft competences contribute to develop and advance participatory leadership, effective communications, team building, Win–Win negotiation strategies, and entrepreneurial abilities (Bardy and Massaro, 2012; Lepeley and Albornoz, 2012). All this prompts human interest in knowledge exploration leading to creativity and innovation (Handzic and Chaimungkalanont, 2004; Massaro M., 2012; Politis, 2004; Ruiz et al., 2012). It is also clear today that innovation is a key factor in the global economy where potential and successful trade with new markets in emerging economies requires business leaders and production managers to have outstanding people skills, as well as extensive knowledge of other cultures and inequivocal ability to apply it effectively in new international contexts. To the large extent the skills required to compete and succeed in the global environment today increasingly depend on a broad base knowledge of business functions embedded emotional intelligence, mental and social abilities and organizational, societal and cultural proficiencies(S.K. Clinebell and J.M. Clinebell, 2008; Ford and Bowen, 2008; A.Y. Kolb and D.A. Kolb, 2005). So far, there is not a unique definition for “Soft Skills” (Lepeley and Albornoz, 2012). Robotham and Jubb, recognized that soft competences are related to personal qualities that lay behind people rational behavior (Robotham and Jubb, 1996). These items have been viewed as conceptually different from hard competences. Indeed, as the authors suggested, both soft and hard competences are descriptions of regularities in individual behavior. Moreover, there is a very limited number of empirical studies that investigate the importance of SK

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Maurizio Massaro et al. only few formal programs on the subject, including in traditional business schools. This paper intends to fill this gap from two dimensions: it reviews several articles in the field of “Soft Skills” in order to draw a theoretical framework intended to strengthen their importance in business literature. These concepts are discussed and analyzed using a semantic approach. Secondly, after developing the model, a content‐analysis will be provided to show what we found in Italian Business School Manifestos on the subject of “Soft Skills”. The Italian Minister of Education listed 99 Business Schools in Italy which are stratified by the Italian Research Center “Cencis” in Mega, Big, Medium and Small. According to our review at the time of our analyses only 47 BSs have a well detailed online manifesto with specific sections on the aims of the courses. Their curricula on business management and accounting will be examined through the content‐analysis method. A statistic approach will be developed in order to recognize concepts and identifying the importance of each context within the sample.

2. Theoretical back ground In order to understand the concept of soft skills several articles have been analyzed. According to Goodman and Beenen (Goodman and Beenen, 2008) learning outcomes derive from knowledge, skills, and abilities acquired during the learning process. The authors recognized as the role of management schools is to prepare people to achieve successful business careers preparing people for new work careers. Blasco recognizes that in a global environment, internationalization of business involves several cultural dimensions, which confront business schools with new challenges (Blasco, 2009). The author identifies the following major challenges: decisions about which cultural approaches need to be taught and how international business courses can be effectively integrated with other teachings. Moreover, Clinebel and Clinebel have recognized the relevance of teachers’ selections balancing executive and full time professors who are able to recognize the need to equate hard and context specific competences with soft competences (S.K. Clinebell and J.M. Clinebell, 2008). Andrews and Higson assesse that BSs confront an urgent need to develop people competence to provide graduates with the skills and abilities required today by employers. This need is critical in a period of global crisis and job scarcity. Unfortunately, since the business literature has not reached consensus yet on a common definition for Soft Skills. Thus, we have made the intent to highlight borders of the concept reviewing some paper on the topic. Table 1 below shows shows definitions of “Soft Skills” by different analyzed authors. Table 1: Literature and soft competences AUTORI (Andrews and Higson, 2008)

(Blasco, 2009) (Ford and Bowen, 2008) (Goodman and Beenen, 2008) (S.K. Clinebell and J.M. Clinebell, 2008) (Holtbrugge and Mohr, 2010)

Concepts used for defining soft competences Professionalism; Reliability; The ability to cope with uncertainty; The ability to work under pressure; The ability to plan and think strategically; The capability to communicate and interact with others, either in teams or through networking; Good written and verbal communication skills; Information and Communication Technology skills; Creativity and self‐confidence; Good self‐management and time‐management skills; A willingness to learn and accept responsibility. Culture. Global environment issues, Ethic issues, Planning, Strategy and strategic planning, Decision making and problem solving, Change, Employee Motivation. Learning to learn, problem solving, collaboration, multicultural and global competences , information valuation ability. Character and complexity are results of the knowledge acquisition process.

Leadership, Relationship and communication, Team working, Information management.

Using the definition proposed we tried to single out skills that shape up soft competences. A semantic approach is used to aggregate similar concepts. Moreover, using a couple of commonly used sources and dictionaries we identified skills with a number of words that fit the concept. We later searched synonymous for these concepts. Table 2 reports a description of the concepts recognized, word and synonymous used.

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Maurizio Massaro et al. Table 2: Soft skills items, words derivation and synonymous Soft Skills items

Words Concept

Synonymous

Ideas creation ability

Problem Solving Creativity and self‐confidence

Coordination ability

Collaborative skills

Ability to communicate and interact with others Interpersonal relations and communications Work in groups and Team

‐ Imagination, ingenuity, inventiveness, creativity Collaboration, cooperation, help, contribution Communication, dissemination, influence

Multicultural ability

Global expertise / multicultural General culture Networking Global context character Complexity

Planning ability

Ability to cope with uncertainty

Plan Strategy and strategic planning Change

Learning ability Professionalism

Learning to learn Willingness to learn and accept responsibility. Professionalism Reliability Stress management Time management

Leadership

Employee motivation Leadership and influence

Information management ability

ICT competences Decision‐making Information management

Ethics

Communication ability

Written and verbal communication skills

Relationship, connection, correlation Team environment, staff, units Skills, languages, interaction Knowledge, learning, scholarship Social network Workplace, environment Personality, inclination, mind, mind Multiplicity, variety, variability, multiplicity Indeterminacy, vagueness, indecision, ambiguity Organization, planning, design Skills, tactics, cunning Change, change, renewal, innovation Acquire, understand, learn, experience Aspiration, awareness, maturity, will Seriousness, commitment, competence Trust, secure Stress, endeavors, urgency Independence, run, self‐management Boost, encouragement Management, command, direction Computer, web, internet Choice, resolution, conclusions ‐ Morality, integrity, ethics, customs Communication, relationships

3. Methodology After defining the concept of Soft Skills we used a content‐analysis technique to verify how Italian Business Schools foster these competences in their curricula. Data was collected from Business Schools Manifestos in the web sites of Italian Universities. Content‐analysis is a “research technique for the objective, systematic, and quantitative description of the manifest content of a communication” (Berelson, 1952, p.18). This method investigates the content of communication either text, images, audio file and so on. The advantage of this research method is “its high level of objectivity and external validity” (Ceci and Iubatti, 2012, p.570). Because of the diffusion of specific software literature recognizes how this approach has been increasingly used since the 1980s (Ceci and Iubatti, 2012; Gebauer et al., 2008; Salvatore et al., 2012). Moreover, Sonpar and Golden‐

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Maurizio Massaro et al. Biddle (2008) enhanced the value of content analysis as an instrument that facilitates theory elaboration (Sonpar and Golden‐Biddle, 2007). The content analysis was developed using as a source data information published in the Web sites of Italian Universities. According to the Italian Minister of Education there are 99 Business Schools recognized in Italy. The Italian research center “Censis” classified Universities on the base of their dimension dividing them into Mega Universities (Universities with more than 40,000 students), Big Universities (between 20,000 and 40,000 students), Medium Universities (between 10,000 and 20,000 students) and Small Universities (with less than 10,000 students). Our search identified only 47 Universities with a well detailed manifesto that contains specific sections on aims of the courses. The analysis is focused on management and accounting curricula and excluding on economics. We analyzed ratings assigned to each BS by the research center “Censis” in order to verify that our sample was normally distributed and dimension didn’t have an impact on BS quality. Table 3 offers a synthesis of descriptive statistics of the sample. Table 3: Italian BS of the sample and their quality Descriptive statistics

Numb of BS

Rating

Min

Max

SDev

Mega

83.48182

72.8

102.5

8.345875

Big

84.42857

75.3

94.5

5.902858

Medium

87.33846

74.5

98.5

6.800924

Small

82.08333

77.3

5.16543

To verify that each group was well distributed and there was control of the size variance among Business Schools we carried on an ANOVA analysis. Our findings do not justify considering differences in performance among business school in the sample in terms of quality rating assigned by the Italian research center “Censis”. Table 4: Italian BS of the sample ANOVA analysis Anova

Among groups

148.891

49.630

1.080

0.368

Within groups

1837.944

45.948

Totale

1986.835

43*

* 4 universities did not have ranking since considered too young.

4. Results To develop our study we designed a two steps analysis. A first step was to test validity of convergence between concepts recognized in the semantic analysis. A Cronbach Alpha analysis was developed and results are reported in table 5. It is interesting to highlight that some concepts could not be tested because insufficient number of words could be recognized in relation to the underlying concept. The results of our semantic analysis are confirmed by the Cronbach Alpha analysis with the exception of Learning ability and leadership information. Indeed, all measured Cronbach Alpha are over 0.50 which is recognized as the level of acceptability. Thus, words and items used for defining single soft skills are statistically relevant. A second step has been defined in order to verify the importance recognized by Italian BSs to soft skills. While some concepts are well described in the program descriptions in the BS Websites, others are less clear. For instance ethic, communication ability, leadership and idea creation use significantly less number of words when compared with to multicultural ability, planning ability and coordination ability. Results are presented in table 5. Table 5: Soft competences in Italian Business Schools Variable Ideas creation ability Coordination ability Multicultural ability Planning ability Learning ability

Average

Min

Max

St Dev

Cronbach Alpha

2.68 25.17 37.68 24.19 21.09

0 0 0 0 0

10 102 139 90 68

2.87 23.10 32.69 22.13 18.03

0.82 Not Valuable 0.83 0.61 0.24

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Maurizio Massaro et al. Variable Professionalism Leadership Information management ability Ethics Communication ability

Average 17.94 1.00

Min 0 0

Max 65 12

St Dev 18.88 2.16

Cronbach Alpha 0.63 0.44

12.32 1.70

0 0

71 20

11.82 3.59

0.7 Not Valuable

5.49

8.08

Not Valuable

Interesting enough analyzing the impact of “soft skills” among SBs, we found that there is a different approach with previous assessment mechanisms. While mega Universities have a lower number of soft skills published in their curricula as an aim of their curricula, large universities pay the greater attention with to “Soft Skills” showing an average of 188 words compared with 74 words published in Mega BSs curricula, as shown in table 6. The ANOVA test reported in table 7 seems to confirm our hypothesis. Table 6: Soft skills in mega, big, average and small Italian Business Schools: Descriptive statistics BSs

Average

Min

Max

St Dev

Average

182.2308

389

142.4963

Small

117.7143

233

101.7165

Big

188

417

123.8251

Mega

167

54.55273

Table 7: Soft skills in mega, big, average and small Italian Business Schools ANOVA Anova Among groups Within groups Totale

SQ 107415.1999 565489.7363 672904.9362

df 3 43 46

MS 35.805.1 13.150.9

F 2.7

p 0.05

5. Conclusion Our research follows up results of a survey of chief executive officers of international companies in Chile, since 1995 the most competitive economy among developing countries, and their shared concern as the main employers of business school graduates, that effective management of “soft skills” is necessary for graduates to increase employment opportunities and are required to improve business performance and outcome in the global economy of the 21st century (Lepeley and Albornoz, 2012). Furthermore, the executives observe that business schools are not training graduates effectively in “Soft Skills” to make them productive and successful in todays’ business markets. They also assess that for companies, it is much more difficult and expensive to train employees in “Soft Skills” than in the hard core business skills, traditionally taught in business schools (Lepeley and Albornoz, 2012) Our study follows the assumption that a primary concern of institutions of higher education is the content of the educational programs and degrees they offer. Hence we infer that if business education curriculum follows the traditional pattern this uncovers a critical need for change. Because it is critical to assess business curricula and re‐design programs in synch with changing demands of the labor markets business education must serve to benefit graduates. Business programs assessment and change are especially certain in view of a strong criticism of MBA programs. An increasingly common criticism is that managerial competences require “Soft Skills”. Consequently we sought important to examine MBA programs and their curricula. Although our analysis is limited to Italy, it has considerable potential to be replicated in other countries because the methodology used can be easily applied to business education elsewhere. The method we used has strengths. It is empirically driven in competency comparisons, it is based in a large representative sample of business schools, and an objective selection of course data. But there are limitation.

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Maurizio Massaro et al. First, the course content data used in the analyses contains only required coursework publicly advertised in business program Websites. And it may be possible that business schools offer additional opportunities to train graduates in “soft skills” which are not necessarily advertised in websites. Second, a content analysis like this, cannot measure how the course content is ultimately delivered by the instructor. Third, some business education programs include international assignments where students may receive training in “soft skill” at a foreign university. Fourth, an assessment of business education is pending to objectively balance hard core business skills (finance, economics, strategy, management, marketing, production) with the emerging need for “soft skills” that labor markets are demanding (Bardy and Massaro, 2012; Lepeley and Albornoz, 2012). Or what non‐technical competencies business corporations demand. Ultimately we think that our findings may serve as groundwork for research in other countries and a base for necessary international comparisons. Future research that utilize policy‐capturing techniques could allow researchers to better understand the decision‐making process employed in the design and delivery of business programs, and further explore the needs and demands of various stakeholder groups, such as companies, small business, other organizations, students, faculty, and overl all business school’ Deans, to objectively evaluate what is relevant, or irrelevant today, for business education in the context of the global economy and the Knowledge Society of the 21st century.

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Maurizio Massaro et al. Lepeley, M.‐T. and Albornoz, C.A. (2012), “Advancing People Skills for 21st Century Business Education in Chile,” inAlon,I.,Jones,V. and McIntyre,J. (Eds.),Innovation in Business Education in Emerging Countries, New York, Palgrave Macmillan, p. forthcoming. Massaro M., B.R., Pitts M. (2012), “Supporting creativity through knowledge integration during the creative processes. A management control system perspective,” The Electronic Journal of Knowledge Management, Vol. 10 No. 3, pp. 254– 263. Politis, J.D. (2004), “Transformational and Transactional Leadership Predictors of the ‘ Stimulant ’ Determinants to Creativity in Organisational Work Environments,” Electronic Journal of Knowledge Management, Vol. 2 No. 2, pp. 23– 34. Robotham, D. and Jubb, R. (1996), “Competences : measuring the unmeasurable,” Management Development Review, Vol. 9 No. 5, pp. 25–29. Roos, J., Roos, G. and Edvinsson, L. (1998), Intellectual Capital – Navigating the New Business Landscape, Basingstoke, McMillan, p. 143. Ruiz, D., Jain, D. and Grayson, K. (2012), “Subproblem Decomposition: An Exploratory Research Method for Effective Incremental New Product Development,” Journal of Product Innovation Management, Vol. 29 No. 3, pp. 385–404. doi:10.1111/j.1540‐5885.2012.00913.x Saint‐Onge, H. (1996), “Tacit knowledge the key to the strategic alignment of intellectual capital,” Strategy & LeadershIp, Vol. 24 No. 2, pp. 10–16. doi:10.1108/eb054547 Salvatore, S., Gennaro, A., Auletta, A.F., Tonti, M. and Nitti, M. (2012), “Automated method of content analysis: A device for psychotherapy process research.,” Psychotherapy research journal of the Society for Psychotherapy Research, Vol. 22 No. 3, pp. 37–41. doi:10.1080/10503307.2011.647930 Sonpar, K. and Golden‐Biddle, K. (2007), “Using Content Analysis to Elaborate Adolescent Theories of Organization,” Organizational Research Methods, Vol. 11 No. 4, pp. 795–814. doi:10.1177/1094428106297804 Stewart, T.A. (1997), Intellectual Capital: The New Wealth of Organizations, Performance Improvement, Doubleday, Vol. 37, p. xxi, 278 p. doi:10.1002/pfi.4140370713

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Intellectual Capital Management: From Theoretical Model to a Practice Model Florinda Matos CAPP – ISCSP ‐ Technical University of Lisbon, Portugal and ESTG – Polytechnic Institute of Leiria, Portugal florinda.matos@icaa.pt Abstract: Intellectual capital has become a key element of the knowledge economy. Their management is a factor that influences the competitive advantage of companies. The main objective of this paper is to present a methodology (ICM ‐ Intellectual Capital Model) that allows the audit of intellectual capital management in small and medium enterprises (SMEs). From the conducted research, it can be concluded that the model is technically robust and determines that the management of intellectual capital is likely to be audited and certified in order to control the quality and dynamism of the knowledge generated and allowing the partner organizations (customers, suppliers and enders) to estimate the innovation capacity and verify the conformity of their management parameters, compared with a reference standard. Indeed, the results of surveys also show that the proposed model forms the basis of a credible accreditation system for intellectual capital management in the majority of Portuguese SMEs. This paper also contributes to enhance the discussion around the value of organizations intangible assets and therefore to change the current concepts of economic development. Keywords: intellectual capital management, audit, ICM

1. Introduction The competition between companies not only results in the production process as seen in the industrial age. At the moment, the skills, creativity, values, culture, motivation, among others, are differentiating factors. Indeed there has been a high consensus on the importance of intangible assets as a source of economic competitiveness of enterprises. However, it has not been possible to establish credible metrics, accepted unanimously, to measure these assets. Understanding the intellectual capital of an organization, manage it and turn it into a competitive advantage for enterprises requires new talents. The OECD (2010, p.1) states that investment in intangible assets and multifactor productivity increases have been responsible for over two thirds of the growth in labour productivity in many countries. According to this organization, the World Bank estimates that the predominant form of wealth for most countries is intangible capital. The European Commission has also given much importance to the issue of intangible assets, including those related to more innovation policies. It is recognized that SMEs make up 99% of the productive capacity of the European Union, which are the main source of innovation. However, with the current financial crisis, the financing of these companies has been very difficult (European Commission, 2008). Thus, we can say that a report of the management of intellectual capital presented with credible metrics, recognized and accepted by the various stakeholders, may be a safety tool and at the same time can be used as an important marketing tool. The following sections of this paper present ICM methodology as a valid tool for auditing the intellectual capital management.

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2. Literature review 2.1 Methodologies for measuring intellectual capital Several authors have postulated the importance of measuring intellectual capital in a strategic perspective. Thus, there have been many models and methodologies, which represent different approaches to the measurement of intellectual capital. However intellectual capital has gained prominence only after the works of Sveiby (1997), in Sweden. The author gave a new vision of intellectual capital considering the intangible assets as the main strategic issue that should be put to the organizations. Since then, several authors proposed models and methodologies for assessing the intellectual capital of organizations. The further development of these models was found with authors such as Edvinsson and Malone (Edvinson and Malone, 1997). Edvinson and Malone (1997), proposed a model, “Skandia Navigator”, which divides intellectual capital into two categories: human capital and structural capital. Thus, according to this vision, intellectual capital is the sum of structural capital and human capital, this being the basic capacity for the creation of high quality value. Sveiby, (1997), developed a measurement methodology, “The Intangible Asset Monitor”, by dividing the intangible assets into three groups: individual competence, internal structure and external structure. This methodology is based on quantitative and qualitative indicators to assess the intellectual capital. The "Intangible Asset Monitor" is used by several companies around the world that offer an overview of intellectual capital. The “Skandia IC Report" is the result of that assessment. Sveiby (1997) recommends replacing the traditional accounting methodology with a new methodology that contains a knowledge perspective. For the author, this methodology is very important to complete the financial information and evaluates the company´s efficiency and stability. Among the most relevant methodologies are also the Balanced Scorecard” (Kaplan and Norton, 1992, 1996, 1996a) the “IC Accounting System” (Mouritsen et al., 2001), the “Value Explorer” (Andriessen and Tissen, 2000), and the “Intellectual Capital Benchmarking System” (Viedma, 2001). These different approaches are based on the measurement of organizations intangible assets. Andriessen (2004, 2004a) by applying the "theory of multidimensional value measurement" to the nations, gives a new vision to strategic intellectual capital management. To understand the diversity of attempts to measure intellectual capital a listing of some models that have been presented is presented below (Table 1), which were classified according to the classification of Williams (2000). Table 1: Chronological classification of methods and methodologies for measuring intellectual capital, according to the Williams classification (2000) Williams Classification 1

Model

Author

MCM

The Invisible Balance Sheet

Sveiby (1990)

SC DIC DIC DIC

Balanced Scorecard Citation ‐ Weighted Patents Technology Broker Citation‐Weighted Patents

Kaplan & Norton (1992) Dow Chemical (1996) Brooking (1996) Bontis (1996)

Market Capitalization Methods (MCM)

Scorecard Methods (SC) Direct Intellectual Capital Methods (DIC) Return on Assets Methods (ROA)

267

Florinda Matos Williams Classification 1 DIC MCM ROA MCM SC

Model

Author

SC SC DIC DIC

Human Resource Costing & Accounting Tobin´s Q Economic Value Added (EVA™) Calculated Intangible Value IC‐Index™ Value Added Intellectual Coefficient (VAIC™) Skandia Navigator™ Intangible Assets Monitor Accounting for the Future (AFTF) HRSstatement

DIC

Inclusive Valuation Methodology (IVM)

ROA SC MCM SC ROA SC SC SC SC DIC DIC DIC SC DIC SC SC SC SC SC SC DIC / MCM SC DIC SC SC SC

Calculated Intangible Value Intelect Model Investor Assigned Market Value (IAMV™) Holistic Accounts Knowledge Capital Earnings Modelo Nova Intangible Value Framework Value Creation Index (VCI) IC Rating The Value Explorer Total Value Creation, TVC™ Intellectual Asset Valuation Intellectual Capital Rating Inclusive Valuation Methodology Based on EFQM Model Knowledge Audit Cycle Intangible Assets Statement Heng Model Meritum Guidelines Value Chain Scoreboard™ FiMIAM Public Sector IC The 4‐Leaf Model Danish Guidelines IC‐dVAL™ Chen, Zhu e Xie Model

IAbM

SC SC SC SC SC DIC SC DIC DIC SC

SICAP ‐ EU Project Intellectus National Intellectual Capital Index Topplinjen/ Business IQ Intellectual Capital Value Creation The Plexus Model Intellectus Model Dynamic Monetary Model EVVICAE™ Regional Intellectual Capital Index (RICI) Intellectual Capital Statements for Europe(InCaS) ICU Report

Johansson (1996) Tobin (1997) Stern Stewart & Co (1997) Stewart (1997) Roos et al. (1997) Pulic (1997) Edvinsson & Malone (1997) Sveiby (1997) Nash H. (1998) Ahonen (1998) McPherson (1998) Luthy (1998) Euroforum (1998) Standfield (1998) Rambøll Group (1999) Lev (1999) Camisón, Palácios et al.(1999) Allee (2000) Baum et al. (2000) Edvinsson (2000) Andriessen & Tissen (2000) Anderson & McLean (2000) Sullivan (2000) Joia (2000) M´Pherson & Pike (2001) Caba & Sierra (2001) Schiuma & Marr (2001) Garcia (2001) Heng (2001) Meritum (2001) Lev (2001) Rodov & Leliaert (2002) Bossi (2003) Leliaert, Candries et al. (2003) Mouritzen, Bukh et al. (2003) Bonfour (2003) Chen, Zhu & Xie (2004) Japanese Ministry of Economy, Trade and Industry (2004) Bueno (2004) IADE (2003) Bontis (2004) Sandvik (2004) Boedker, Guthrie et al. (2005) Litschka, Markom et al. (2006) Sanchez‐Canizares et al. (2007) Milost (2007) McMcCutcheon (2008) Schiuma, Lerro et al.(2008)

ROA

SC SC

Source: Authors' ‐ Adapted from Sveiby (2010)

268

InCaS Consortium (2006) Sanchez (2009)

Florinda Matos These models and methodologies will not be developed because this is not the objective of this paper. On the other hand, it is assumed that the readers of this paper will easily be able to access the different approaches in these models. The model presented in this paper is the “Intellectual Capital Model” (ICM) (Matos and Lopes, 2009). The choice of this Model is linked to the need for being able to identify, in an integrated and consistent way, the complexity of the factors in the framework of organizational knowledge. Compared with other models, ICM seems to be more adapted to evaluate the intellectual capital management. The ICM ‐ Intellectual Capital Model ‐ consists of 4 Quadrants specified by their parameters (Matos and Lopes, 2009).

Figure 1: ICM ‐ Intellectual Capital Model (Matos and Lopes, 2009) The Quadrant Individual Capital, Team Capital and Processes Capital are related to the company's internal environment, the Quadrant Clients Capital is related to the external environment. Individual Capital In ICM, is called Individual Capital Tacit Knowledge / Human Capital Quadrant. It is the knowledge inherent to the individual himself, and containing the real source of value, talents and the skills to generate innovation. Here, one has included the theoretical and practical knowledge of the individuals and the capacities of different types, such as artistic, sporting or technical. Individual capital is interpreted as personal skills, social skills, qualifications, experience and formal education or skills that each individual is willing to put at the service of the company, in view of ongoing customer orientation. When individuals combine these skills with the ability to realize the company's business this is very valuable individual capital. This individual capital can be increased when the company invests in recruitment and selection, training / qualification, in talent management and personal development. Whenever the company dismiss any employee, or where there are factors of internal or external that affect the performance of individuals (e.g.: motivation, compensation, the downgrading of skills), the individual capital may decrease.

269

Florinda Matos Companies that want to create knowledge must invest in training and skills, but not all the know‐how is acquired through formal channels. Much of this knowledge comes through the work that is developed at the company's result and, particularly, the interaction of the teams, especially teams that adopt innovation and development and also the interaction of individuals with the customer. Team Capital The Team Capital is the Human Capital / Explicit Knowledge Quadrant. The team shares the explicit knowledge. In this area, knowledge applies to the individual in the form of facts, concepts or tools. The team capital results from the way skills of individuals are combined, creating an affiliation group. Team capital assumes the existence of a type of group that shares common goals and differentiates itself from others by their level of performance in a given task. The teamwork is just an intangible asset but that results in the ability to perform tasks with efficiency and effectiveness, while generates satisfaction of team members. Teams are generating value for the organization and they are an essential source of competitive advantage. Teams operationalize the sharing of tacit knowledge from individuals and convert it into explicit knowledge or formalized in the form of specifications, process descriptions, rules, regulations, among others. When this tacit knowledge of individuals is shared with the collective, it earns a higher value and is able to pass the structural capital. We highlight the role of training / qualification, such as determining the possibility of transformation of tacit knowledge into explicit knowledge, since this parameter functions as inducer and facilitator of a team culture with a focus on total quality client service. The alignment between the different leaders of the company is a determining factor in the continuing development of teams, including the creative teams. Communication in teams is essential, because it is what allows the interactions between individuals. New technologies and networks are also essential in these processes of knowledge transfer. Processes Capital When the explicit knowledge of the teams is associated with the structural capital it emerges formalization and development of organizational memory, which supports, in turn, improving processes with a focus on total quality. The processes capital corresponds to knowledge that is not of individuals but of the organization and it is recognized in the structural capital. This Quadrant represents the ensemble of shared knowledge, summed up by experts (scientific community), recognized as the most advanced form of knowledge. This type of knowledge covers, among other dimensions, the organizational routines or the organizational memory. Organizational memory represents the register of an organization, represented by a set of documents and artefacts. Its goal is to expand and amplify knowledge through its acquisition, organization, dissemination, usage and refinement. Organizational memory can be a way of registering tacit knowledge, making it explicit, so that through business processes it becomes part of the patrimony of the company, to be shared and recreated. The structural capital result from the way of the know‐how belonging to people is embedded in the company, producing organization, providing answers to customer needs.

270

Florinda Matos Also, the ability to attract and retain skilled people is the structural capital, where they decided the processes of recruitment and selection, career development, reward systems, motivation, challenging tasks, internal organization, among others. Organizations have their own history which is documented through computerized files or files on paper resulting from routines that are being assimilated and in standardized in procedures manuals. Access to this information is facilitated through information management, held with the support of information technologies and communication. We can say that the capital process includes all powers to the customer orientation and all internal routines. The management of the intellectual capital of a company is thus a very important task which is to create processes that facilitate the creation of more structural capital. When companies invest in innovation and development, they make people's skills incorporated into structural capital. An example of this are the companies that use CRM systems (Customer Relationship Management) that incorporate the knowledge of individuals and transform into better skills in relationship management market. The product quality, process or service depends therefore on how the structural capital is developed and incorporated in the organization at the level of processes. This information, though difficult to describe the reports of the companies, are very important for lenders (investors, shareholders and creditors), for assessing sustainability in the long term. Clients Capital The Clients Capital is the result of the interaction Structural Capital / Tacit Knowledge. This typology represents the organizational knowledge in its practical form and is already incorporated into the tacit experiences formalized in the team. This knowledge, although hidden, becomes accessible through interaction, and it is the main characteristic of the performance of highly specialized teams. The customer capital arises when individuals are able to create solutions (products or services) to meet the needs or solve customer problems. The customer relationships that allow the formation of this capital, stable over time, requires a continuous work to establish long lasting relationships. Market research and analysis of customer satisfaction are some of the actions that can measure the image that customers have of the company. The systematization of the results of these studies, standards and procedures, is an example of structural capital transformation into clients’ capital. The customers are difficult to retain, whereby knowledge of the company must be invested in processes that facilitate the fixation of these clients. The correct use of networks and new technologies is crucial in interacting with customers and therefore to build a stable clients capital. Clients focus, assumes that there is a continued investment in innovation and development in order to meet needs previously scheduled. The clients’ capital thus includes all the knowledge that the company has in the market, including indicators to know the size of the target market and market potential, clients’ preferences, the purchasing decision factors and reputation or image of company in the market. The clients’ capital can be valued by upgrading skills of individuals and teams.

271

Florinda Matos The analysis of the movements of clients’ capital should enable to predict in which direction they move the company's financial forecasts. NTIC and Network In the presented Model the Network and NTIC are essential in the relationship between the 4 Quadrants. Thus, the companies that put the NTIC at the service of human resources have a great advantage, because they can reduce the administrative difficulties in solving simple problems, increase the quality of services and promote continuous improvement and personal growth. The approach to the concept of Network is not a new concept. The network, as a social concept, is the genesis of the social constructs of individuals. More recent is the approach to the concept of network system as a factor in the acquisition of knowledge and innovative action. In conclusion, the NTIC are crucial to have effective Networks. In the ICM, the relational capital is the result of several interactions that take place within the organization and that allows to transform tacit knowledge into explicit knowledge. This knowledge is put to the service of customers and all stakeholders, allows organizations to achieve high performances.

3. Empirical research 3.1 Methodology This research aims to further validate previous studies performed by the authors (Matos and Lopes, 2009, 2010, 2011). We intend to identify the indicators to evaluate each parameter of Intellectual Capital Model (ICM) and to examine the psychometric properties (reliability and validity) of ICM parameters. According Carmines and Zeller (1979), reliability concerns confidence in the constancy of the results of several applications of the same test. To study the reliability of the questionnaire we have adopted the method of internal consistency by determining the Cronbach's alpha (Cronbach, 1951) coefficient that, according to Nunnally (1978), provides a good estimate of reliability. The value of this coefficient can vary between 0 and 1. For Nunnally (1978) an instrument is classified as having acceptable reliability when Cronbach's alpha is at least 0.70. Construct validity will be determined by factor analysis (CFA). The extraction method used was the Principal Component Analysis (PCA).

3.2 Sample The initial sample consists of 1107 Portuguese SMEs considered the "PME Leaders' (SME leader), the best SMEs based in Portugal, in 2010. This classification is awarded annually by IAPMEI ‐ Portuguese Agency for SMEs and Innovation. The “PME Líder” companies are proposed by one of seven major banks in Portugal and must obey certain criteria. These companies are, from various sectors, as through the quality of their results and high competitive standards, having good financial ratios and profitability above the national average. These companies are actively contributing to the dynamics of development and employment in Portugal, being responsible for 37 000 direct jobs. Its turnover in 2010 exceeds 4.5 billion euros. From this total sample we obtained a total of 112 responses, corresponding to the same number of subjects of analysis, which respects the rules of a minimum of 10 observations per item, proposed by Biddle, Markland, Gilbourne, Chatzisarantis and Sparkes (2001).

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3.3 Instrument specificity As a tool for validating these properties we used a questionnaire that has been developed by the authors (see Matos and Lopes, 2009, 2010, 2011). To facilitate analysis, we divide the global instrument in various groups of questions (a total of 140 questions) according to each of the ICM Quadrants. Networks and New technologies were also considered. The questionnaire consists of statements about the applicability of each parameter to the business context of each SME, according to a Likert scale of 5 points (1 = never applies, 2 = applies somewhat, 3 = moderately applies 4 = applies very much and 5 = applies completely). This version follows the adaptation of the questionnaire proposed by Matos and Lopes (2011). We conducted a questionnaire pre‐test in a convenience sample consisting of 10 SMEs in the same sector of the main sample.

3.4 Statistical procedures Initially, we carried out most of the descriptive statistics concerning the variables assessed using Likert scales. Subsequently, we proceeded to determine the internal consistency using Pearson's correlation coefficient. Reliability was determined using Cronbach's alpha coefficient. The significance level adopted to reject the null hypotheses is p <.05, which corresponds to a probability of wrong rejection of 5%. Since the sample size was over 30 (Pestana and Gajeiro, 2008), we used the Central Limit Theorem to determine the arithmetic average of the indicators of each parameter. We conclude with a Principal Component Analysis (PCA) for each parameter. For statistical data, we used the computer software SPSS ‐ Statistical Package for Social Sciences, version 18.0.

4. Results 4.1 Reliability The average of each indicator is in the interval [1.31, 3.49], the standard deviation is in the range [‐0.541, 1.995], the variance is in the range [0.306, 3.978], the minimum is 1 and the maximum 9. A visual examination of the shape of the distribution showed that almost all the variables had a slight bias towards higher scores but they did not look very different from the normal distribution curve. Calculating the matrix of correlations between items were excluded indicators with values lower than 0.30. We used the criteria proposed by Cohen (1988): values between 0.10 and 0.29 can be considered small, values between 0.30 and 0.49 can be considered as average, and values between 0.50 and 1 can be interpreted as large. So we decided to eliminate all indicators with a correlation less than 0.30. Initially we applied the Cronbach's alpha in all issues of the questionnaire and we exclude issues that had Cronbach's alpha values below 0.6. With these procedures, we eliminated 43 indicators and the study of the instrument dimensionality continued based on the remaining 97 indicators. We conducted twenty two Principal Component Analysis to explore the setting of the questions that allow you to check each parameter. The final Cronbach's alpha reliability coefficients parameters are averaged 0.7 and 0.9.

273

Florinda Matos Based on these analyses, we reduced the number of items constructs representing each parameter. We had attention to the soundness of the contents of each item, obtaining an instrument small enough. With the Principal Component Analysis, we verified the existence of latent variables that explain the total variance of the indicators of each parameter. Conducted a Principal Components Analysis, the first component explains a very large percentage of the variance of these indicators together.

4.2 Validity Given the above results that provide empirical evidence of the proper selection of indicators for each parameter, we find that it is legitimate to add indicators in each group and calculating the respective mean, since all quantities are expressed in equal ranges. There are thus obtained, averages of groups of variables that are continuous variables, which makes sense to apply normality tests, especially since, in many cases, the number of parts is high, approaching the conditions of applicability of the Central Limit Theorem of Lindeberg. On the set of 22 variables obtained, now makes sense to try to fit a model theory. Our initial hypothesis is that underlying these 22 variables there are, at most, four factors, corresponding to the 4 quadrants of the ICM. We intend to exploit the adjustment factor loadings in the ICM and to verify if it was possible to find a reduced number of factors representatives of the entirety of the parameters. The study on the number of factors to retain was made based on a set of indicators, corresponding to the average of 22 parameters of ICM, observance of the Kaiser criterion, according to which if chosen factors whose variance is greater than 1 (initial eigenvalues> 1). We used a principal component factor analysis and analysed the loads of factors. The results point to the extraction of 6 factors explains about 70% of the total variance (see table 2). If we consider only 4 factors, the variance explained by this model, would correspond to about 56% of the total variance ‐ which, if not a full corroboration of the theory, is important evidence in support ‐ see scree plot that corroborates this analysis. Table 2: Total variance explained Component D

Initial Eigenvalues

Rotation Sums of Squared Loadings

Total

% of Variance

Cumulative %

Total

% of Variance

Cumulative %

7,547

34,304

4,132

18,782

2,188

9,947

44,251

3,244

14,743

33,526

1,842

8,371

52,622

2,679

12,175

45,701

1,516

6,890

59,512

2,174

9,884

55,585

1,196

5,437

64,949

1,570

7,137

62,722

1,007

4,578

69,527

1,497

6,805

69,527

,899

4,085

73,612

,798

3,625

77,238

,782

3,555

80,793

,638

2,898

83,691

,588

2,674

86,364

,549

2,494

88,858

,459

2,086

90,944

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Florinda Matos Component

Initial Eigenvalues

Rotation Sums of Squared Loadings

Total

% of Variance

Cumulative %

Total

% of Variance

Cumulative %

,373

1,696

92,640

,328

1,490

94,130

,299

1,359

95,489

,269

1,223

96,712

,208

,943

97,656

,201

,914

98,570

,177

,803

99,373

,138

,627

100,000

‐1,149E‐16

‐5,224E‐16

100,000

Extraction Method: Principal Component Analysis.

Source: Authors'

Source: Authors' Figure 2: Scree plot of factor analysis Table 3: Matrix components

Component 1

QCEP3

,823

‐,010

‐,115

‐,178

‐,006

,022

QCIP3

,773

,141

‐,076

‐,057

‐,059

,155

PRT

,725

,040

‐,023

,117

,127

,064

QCEP1

,676

,268

,181

,092

‐,151

,227

QCCP1

,674

‐,185

,290

‐,394

,177

‐,230

QCEP2

,674

,263

‐,197

‐,037

‐,068

,036

QCPP5

,647

‐,190

‐,017

‐,042

,404

,139

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Florinda Matos

Component 1

QCPP1

,634

,336

,165

,199

‐,173

‐,142

QCIP4

,618

,197

‐,339

‐,051

‐,245

‐,083

PNT

,596

‐,510

‐,136

,507

‐,080

,067

QCPP4

,596

‐,510

‐,136

,507

‐,080

,067

QCIP1

,576

,492

‐,402

,026

,083

‐,194

QCCP4

,571

‐,415

‐,036

‐,394

,026

‐,226

QCPP8

,569

‐,369

‐,055

‐,403

,104

,002

QCPP3

,548

‐,291

,463

‐,254

‐,216

‐,143

QCIP2

,529

,465

‐,489

‐,113

,080

‐,080

QCPP7

,500

,349

,356

,198

,359

‐,057

QCEP4

,486

‐,137

,142

,453

‐,242

‐,340

QCCP2

,428

,150

,668

,102

,026

‐,085

QCPP2

,211

,253

,307

‐,259

‐,612

,434

QCCP3

,081

,311

,382

,155

,447

,263

QCPP6

,429

‐,329

‐,183

‐,047

,131

,564

Extraction Method: Principal Component Analysis. a. 6 components extracted.

Source: Authors'

5. Conclusion From the analyzed literature we can conclude that there are no perfect methods of measuring intellectual capital, i.e., none of the methods can answer all needs. Different authors try to choose the method according to the context and the goals they wish to achieve. As a result of this research, we created an instrument (questionnaire format), composed of a set of indicators to audit intellectual capital management. The psychometric properties (reliability and validity) of the instrument were validated. The internal consistency of the indicators was verified based on correlations and Cronbach alpha, considering that the internal consistency should not increase when the indicator is eliminated. The factor structure was examined using the extraction method of Principal Components. The analyses provide abundant empirical evidence that underlying the data collected with the retained indicators (97 indicators and 22 indicators mean) there is a factor structure 4‐6 common factors ‐ which is compatible with the theory of the 4 quadrants of the ICM. After this analysis we examined the internal consistency of the proposed instrument and refined by eliminating indicators that contributed to decrease the theoretical soundness and content, we can see that the 22 parameters of Intellectual Capital Model (ICM), and determine if the indicators show adequate (a total of 97) to assess each of these parameters. The instrument has the following presentation: I – Individual Capital Quadrant Training / Qualification and Talent Management – 3 indicators

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Florinda Matos Valuation of Know ‐ How and Innovation – 4 indicators Investment in Innovation and Development (ID) – 7 indicators Existence of a Policy for Talent Retention – 4 indicators II – Team Capital Quadrant Training / Qualification – 7 indicators Team Work – 3 indicators Innovation in Teams – 3 indicators Leadership in Teams – 3 indicators III – Processes Capital Quadrant Processes Systematization – 2 indicators Registration of Organizational Knowledge – 3 indicators Existence of Certification, Environmental and Social Policies – 9 indicators Partnerships – 4 indicators Investment in Innovation and Development (ID) – 3 indicators The Brands Creation and Management – 4 indicators Complaints System – 4 indicators The Existence of Awards – 3 indicators IV – Clients Capital Quadrant Market Audits – 8 indicators Management of the Clients' Satisfaction – 3 indicators Complaints Clients System – 2 indicators New Markets – 10 indicators Networks ‐ 3 indicators New Technologies of Information and Communication – 5 indicators Finally the survey results allow us to conclude that ICM parameters are robust approach to auditing the intellectual capital management in SMEs, as they meet the criteria for its psychometric properties. Due to the extension of the research, that was supported these conclusions, the instrument cannot be presented here and it will be presented in a publication being prepared for this purpose.

References Andriessen, D., Tissen R. (2000) Weightless Wealth. Financial Times. Prentice Hall. Andriessen, D. (2004) Making Sense of Intellectual Capital. Designing a Method for the Valuation of Intangibles. Elsevier Butterworth‐Heineman, Burlington (MA) and Oxford (UK). Andriessen, D. (2004a) “IC valuation and measurement: classifying the state of the art” Journal of Intellectual Capital, Vol. 5, No. 2, 230‐242. Biddle, S., Markland, D., Gilbourne, D., Chatzisarantis, N., & Sparkes, A. (2001) “Research methods in sport and exercise psychology: Quantitative and qualitative issues.“ Journal of Sports Sciences, 19, 777‐809. Carmines E.G., Zeller R.A. (1979) Reliability and validity assessment. Newbury Park: Sage Publications. Cohen, Jacob. (1988), Statistical power analysis for the behavioural sciences. Hillsdale, NJ, Erlbaum. Cronbach, L. J (1951) “Coefficient alpha and the internal structure of tests.” Psychometrika, 16, 297−334. Edvinsson, L. and Malone, M.S. (1997) Intellectual Capital, Harper Collins Publishers Inc., New York.. European Commission (2008). Putting Small Business First – Europe is Good for SMEs and SMEs are Good For Europe (2008 Edition). Available: http://ec.europa.eu/enterprise/entrepreneurship/sba_en.htm [12 May 2012]. Kaplan RS and Norton DP (1992) ”The Balanced Scorecard” Harvard Business Review, Jan‐Feb 71‐79. Kaplan RS and Norton DP (1996) “Using the Balanced Scorecard as a Strategic Management System" Harvard Business Review. Jan‐Feb. Kaplan RS and Norton DP (1996a) The Balanced Scorecard, Harvard Business School Press, Boston Massachusetts. Mouritsen, Jan, Larsen, H.T., Bukh, P.N. and Johansen, M.R. (2001) ”Reading an Intellectual Capital Statement Paper “The 4th.Intangibles conference” en Stern School of Business, New York University. Matos F.; Lopes A. (2009) “Intellectual Capital Management – SMEs Accreditation Methodology” Paper read at European Conference on Intellectual Capital 09, INHolland University of Applied Sciences, Haarlem, The Netherlands, 28‐29 April.

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Florinda Matos Matos F.; Lopes A., (2010) “Why Intellectual Capital Management Accreditation Is a Tool for Organizational Development?” Electronic Journal of Knowledge Management Vol. 8 Issue 2: 235 – 244. Matos F.; Lopes A. (2011) Intellectual Capital Management: Case Study Portugal versus Romania Paper read at 3th European Conference on Intellectual Capital 09, University of Nicosia, Nicosia, Cyprus, 18‐19 April 2011. Nunnally, J. C. (1978), Psychometric Theory, (2nd ed.), McGraw‐Hill. New York. Pestana, Maria H.; Gageiro, João N. (2008); “Análise de Dados para Ciências Sociais: A complementaridade do SPSS”; 2ª edição; Edições Sílabo: Lisboa. OECD (2010). New sources of growth: intangible assets (pp. 1‐2). Available: http://www.oecd.org/newsearch/0,3766,en_2649_201185_1_1_1_1_1,00.html?q=New+sources+of+growth%3A+int angible+assets%E2%80%9D.&sa=Search&cx=012432601748511391518%3Axzeadub0b0a&cof=FORID%3A11&ie=UTF‐ 8 [12 May 2012]. Sveiby, K. E. (2010). Methods for Measuring Intangible Assets. Available: http://www.sveiby.com/articles/IntangibleMethods.htm [05 March 2012]. Sveiby, K. E. (1997) The New Organizational Wealth. Managing & Measuring Knowledge‐Based Assets, Berrett‐Koehler Publishers, San Francisco. Williams M. (2000). Is a company’s intellectual capital performance and intellectual capital disclosure practices related? Evidence from publicly listed companies from the FTSE 100. Paper presented at McMasters Intellectual Capital Conference. January 2001: Toronto. Viedma (2001) “ICBS Intellectual Capital Benchmarking System” Journal of Intellectual Capital, Vol 2 No 2, 148‐164.

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What is Intellectual Capital Management Accreditation? Florinda Matos1, Albino Lopes2 and Nuno Matos 3 1 ICAA – Intellectual Capital Accreditation Association, Portugal and CAPP – ISCSP ‐ Technical University of Lisbon, Portugal 2 CAPP – ISCSP ‐ Technical University of Lisbon, Portugal 3 ICAA – Intellectual Capital Accreditation Association, Portugal florinda.matos@icaa.pt alopes@iscsp.utl.pt nuno.matos@icaa.pt Abstract: In the last three decades, the world economy has undergone profound economic changes that were reflected in how economic factors affect productivity. So we exchanged the traditional economy based on resources, land, capital and labour, into an economy based on intangible assets with financial accounting difficult and high impact on productive performance of organizations. These assets are recognized as assets of knowledge and now have a strategic value, therefore reliable methods are needed to validate how these assets are being managed. Empirical studies conducted in Small and medium‐sized enterprises (SMEs), led to the development of a methodology for the accreditation of intellectual ® ® capital management ‐ ICMA methodology. The ICMA methodology is a process where a collegial body recognizes that a particular company has the capacity to manage their intellectual capital. Effectively, the accreditation of the intellectual capital management would be a public declaration where a particular company meets a set of accreditation criteria, pre‐ established by the accrediting body. The accreditation will be seen as a quality and recognition stamp and as an indicator of the company’s ability regarding intellectual capital management. The Propose of this paper is to present this methodology as well as the benefits of its use by the business community. In terms of practical implication, we can say that the creation of a recognized intellectual capital management accreditation methodology could be an important tool to ensure the innovation capacity of SMEs and therefore a guarantee for sustainability for the various partners (customers, suppliers, shareholders, government and employees). The following sections of this paper are detailed description of this accreditation methodology. Keywords: Intellectual capital, ICMA®, accreditation, SMEs

1. Introduction The European Commission has given much importance to the issue of intangible assets, including those related to more innovation policies. As is known, Small and Medium‐Sized Enterprises (SMEs) make up 99% of the European Union productive fabric, which are the main source of innovation. However, with the current financial crisis, the financing of these companies has been very difficult (EC, 2008). In countries with higher economic deficits, the question of funding, particularly to SMEs by financial institutions or the state itself, is very complex. Thus, if the report of the intellectual capital management is presented with credible, recognized and accepted metrics, it can function as a guarantee and at the same time can be used as an important marketing tool. The creation of a recognized methodology, certification / accreditation of intellectual capital management could be a very important tool in validating the sustainable innovation capacity of SMEs and in the evaluation of its sustainability. The research presented in this paper is a very important base of support for the establishment of an accreditation system for the management of intellectual capital. This paper describes the reasons why accreditation is important to the management of intellectual capital. The methodology and its procedures are also described.

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2. State of the art Most research projects related to the measurement of intellectual capital that took place particularly in the last decade focuses on theoretical concepts of intellectual capital, in its measurement and evaluation (e.g., DATI, 2000; FASB, 2001; Meritum, 2001, 2002, FRAME, 2003; DMSTI; 2003a, 2003b, Mouritsen, Bukh & Larsen, 2003; RICARDIS, 2006). Some of the newer approaches to evaluation of intellectual capital use a more practical perspective (e.g., ICS, Roos, Pike & Fernstrom, 2005; InCaS, 2006). To understand the abundance of attempts to measure intellectual capital, a list with some of the most stated models is presented, which were classified according to the classification of Williams (2000). Table 1: Chronological classification of methods and methodologies for measuring intellectual capital, according to the Williams classification (2000) Williams Classification 1 MCM

The Invisible Balance Sheet

Sveiby (1990)

Balanced Scorecard

Kaplan & Norton (1992)

DIC

Citation ‐ Weighted Patents

Dow Chemical (1996)

DIC

Technology Broker

Brooking (1996)

DIC

Citation‐Weighted Patents

Bontis (1996)

Model

Author

DIC

Human Resource Costing & Accounting

Johansson (1996)

MCM

Tobin´s Q

Tobin (1997)

ROA

Economic Value Added (EVA™)

Stern Stewart & Co (1997)

MCM

Calculated Intangible Value

Stewart (1997)

IC‐Index™

ROA

Value Added Intellectual Coefficient (VAIC™)

Roos et al. (1997) Pulic (1997)

Skandia Navigator™

Edvinsson & Malone (1997)

Intangible Assets Monitor

Sveiby (1997)

DIC

Accounting for the Future (AFTF)

Nash H. (1998)

DIC

HRSstatement

DIC

Inclusive Valuation Methodology (IVM)

Ahonen (1998) McPherson (1998)

ROA

Calculated Intangible Value

Luthy (1998)

Intelect Model

Euroforum (1998)

MCM

Investor Assigned Market Value (IAMV™)

Standfield (1998)

Holistic Accounts

Rambøll Group (1999)

ROA

Knowledge Capital Earnings

Lev (1999)

Modelo Nova

Camisón, Palácios et al.(1999)

Intangible Value Framework

Allee (2000)

Value Creation Index (VCI)

Baum et al. (2000)

IC Rating

Edvinsson (2000)

DIC

The Value Explorer

Andriessen & Tissen (2000)

DIC

Total Value Creation, TVC™

Anderson & McLean (2000)

DIC

Intellectual Asset Valuation

Sullivan (2000)

Intellectual Capital Rating

Joia (2000)

DIC

Inclusive Valuation Methodology

M´Pherson & Pike (2001)

Based on EFQM Model

Caba & Sierra (2001)

Market Capitalization Methods (MCM) Scorecard Methods (SC) Direct Intellectual Capital Methods (DIC) Return on Assets Methods (ROA)

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Florinda Matos, Albino Lopes and Nuno Matos Williams Classification 1

Model

Author

Knowledge Audit Cycle

Schiuma & Marr (2001)

Intangible Assets Statement

Garcia (2001)

Heng Model

Heng (2001)

Meritum Guidelines

Meritum (2001)

Value Chain Scoreboard™

Lev (2001)

DIC / MCM

FiMIAM

Rodov & Leliaert (2002)

Public Sector IC

Bossi (2003)

DIC

The 4‐Leaf Model

Leliaert, Candries et al. (2003)

Danish Guidelines

Mouritzen, Bukh et al. (2003)

IC‐dVAL™

Bonfour (2003)

Chen, Zhu e Xie Model

Chen, Zhu & Xie (2004)

IAbM

Japanese Ministry of Economy, Trade and Industry (2004)

SICAP ‐ EU Project

Bueno (2004)

Intellectus

IADE (2003)

National Intellectual Capital Index

Bontis (2004)

Topplinjen/ Business IQ

Sandvik (2004)

Intellectual Capital Value Creation

Boedker, Guthrie et al. (2005)

DIC

The Plexus Model

Litschka, Markom et al. (2006)

Intellectus Model

Sanchez‐Canizares et al. (2007)

DIC

Dynamic Monetary Model

Milost (2007)

DIC

EVVICAE™

McMcCutcheon (2008)

Regional Intellectual Capital Index (RICI)

Schiuma, Lerro et al.(2008)

Intellectual Capital Statements for Europe(InCaS)

InCaS Consortium (2006)

ICU Report

Sanchez (2009)

Source: Authors' ‐ Adapted from Sveiby (2010) These methods and methodologies have been seen with a more academic character and so have showed incomplete in the evaluation of intellectual capital management of organizations. ® The ICMA methodology is based on ICM (Matos and Lopes, 2009) and results of the evolution of an academic methodology for a practicing methodology that can be operationalized in a business context.

3. Presentation of the accreditation methodology Intellectual capital management accreditation is not an easy process to define or describe. In theory, accreditation is a process through which a collegial body recognizes that a company is capable of managing their intellectual capital. Indeed, according to this paper, the accreditation of intellectual capital management is a public statement that a company meets a set of criteria for accreditation established by the accrediting body. ® ‐ ICMA Intellectual Capital Management Accreditation ‐ consists of technical process validation and of the acknowledgement of the global capacity of the entity to be accredited, converting it into a member of a recognized group in which the Intellectual Capital best practices are predominant; practices that direct the accredited entities on a constant search of alignment through excellence. ® ICMA , as an international process, aims to be the highest standard of recognition of the management of intellectual capital.

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Companies with accreditation ICMA have a commitment to quality and continuous improvement of the management of their intellectual capital. Accreditation is an important indicator of progress towards improving productivity and competitiveness. It is also an important indicator of companies that take seriously their position in the market and their relationship with stakeholders. ® ICMA is a strategic tool for business management, based on a framework of high standards of quality, respecting the diversity and legitimacy of the rules of each country. ® ICMA is the international recognition of the ability to manage the intellectual capital of companies in their own environment. ® ICMA is a process that looks at the overall performance of the company and is designed to promote the skills of intellectual capital management with a view to innovation and sustainable competitiveness. ® ICMA is a process for the future, in view of the sustainability performance of accredited companies. ® ICMA will be a learning process with the encouragement of an international forum, supported by a body of international experts in intellectual capital management. ® ICMA is a continuous and dynamic process development of organization intellectual capital management. ® The average time a company is accredited is variable, depending on the size of the company and the ICMA criteria that are met by this. When a company meets all the criteria, the accreditation process can be completed in less than a year. ® ICMA is granted for 2 years. Therefore a company that wishes to maintain its accreditation must enter a process of re‐accreditation before expiration of the two year period.

3.1 Objectives of ICMA® methodology ®

ICMA has four main objectives:

Ensure the partners (shareholders, investors, customers, suppliers, etc.) the reliability of the management of their intellectual capital, serving as a market instrument and ensuring transparency.

Provide managers with a tool for decision‐making and innovation management.

Be a tool for benchmarking, allowing a comparison of companies according to national and international criteria, where best practices are encouraged and rewarded.

Promote continuous improvement at all levels of the organizational performance of SMEs, by providing a management tool to improve their competitiveness.

Provide an effective transnational network of SMEs that have common interests in the development of intellectual capital as a way to achieve high performance.

Promote the entities accredited by the recognition of responsibility for intellectual capital management.

3.2 Limitations of ICMA® methodology ®

The main limitation of ICMA is the need to be adapted to the reality of economic and legal systems of different countries. If for the countries of the European Union this adaptation is fairly complex, to third countries there will be some complexity. The additional work that the implementation of an accreditation system generates in a company (performance improvements, training, and changes systems and processes, etc.) can be seen as a limitation.

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Florinda Matos, Albino Lopes and Nuno Matos Also, the financial costs inherent to the process and the accreditation may limit adherence of smaller companies.

4. ICMA® support model ®

The accreditation is based on the evaluation of a set of parameters ‐ ICMA indicators. These indicators, allow us to evaluate the management of intellectual capital of companies, checking that there is evidence the presence of indicators related to the dimensions of intellectual capital, if they are valued and managed. ® The ICMA criteria are based on the Intellectual Capital Model (Matos and Lopes, 2009) which consists of 4 Quadrants divided by twenty‐five parameters and ninety‐seven indicators.

Figure 1: ICM ‐ Intellectual capital model (Matos and Lopes, 2009). The Quadrant Individual Capital, Team Capital and Processes Capital are related to the company's internal environment, the Quadrant Clients Capital is related to the external environment. Individual Capital In ICM, is called Individual Capital Tacit Knowledge / Human Capital Quadrant. It is the knowledge inherent to the individual himself, and containing the real source of value, talents and the skills to generate innovation. Here, one has included the theoretical and practical knowledge of the individuals and the capacities of different types, such as artistic, sporting or technical. Individual capital is interpreted as personal skills, social skills, qualifications, experience and formal education or skills that each individual is willing to put at the service of the company, in view of on‐going customer orientation. When individuals combine these skills with the ability to realize the company's business this is very valuable individual capital. This individual capital can be increased when the company invests in recruitment and selection, training / qualification, in talent management and personal development. Whenever the company dismiss any

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Florinda Matos, Albino Lopes and Nuno Matos employee, or where there are factors of internal or external that affect the performance of individuals (e.g.: motivation, compensation, the downgrading of skills), the individual capital may decrease. Companies that want to create knowledge must invest in training and skills, but not all the know‐how is acquired through formal channels. Much of this knowledge comes through the work that is developed at the company's result and, particularly, the interaction of the teams, especially teams that adopt innovation and development and also the interaction of individuals with the customer. Team Capital The Team Capital is the Human Capital / Explicit Knowledge Quadrant. The team shares the explicit knowledge. In this area, knowledge applies to the individual in the form of facts, concepts or tools. The team capital results from the way skills of individuals are combined, creating an affiliation group. Team capital assumes the existence of a type of group that shares common goals and differentiates itself from others by their level of performance in a given task. The teamwork is just an intangible asset but that results in the ability to perform tasks with efficiency and effectiveness, while generates satisfaction of team members. Teams are generating value for the organization and they are an essential source of competitive advantage. Teams operationalize the sharing of tacit knowledge from individuals and convert it into explicit knowledge or formalized in the form of specifications, process descriptions, rules, regulations, among others. When this tacit knowledge of individuals is shared with the collective, it earns a higher value and is able to pass the structural capital. We highlight the role of training / qualification, such as determining the possibility of transformation of tacit knowledge into explicit knowledge, since this parameter functions as inducer and facilitator of a team culture with a focus on total quality client service. The alignment between the different leaders of the company is a determining factor in the continuing development of teams, including the creative teams. Communication in teams is essential, because it is what allows the interactions between individuals. Processes Capital When the explicit knowledge of the teams is associated with the structural capital it emerges formalization and development of organizational memory, which supports, in turn, improving processes with a focus on total quality. The processes capital corresponds to knowledge that is not of individuals but of the organization and it is recognized in the structural capital. This Quadrant represents the ensemble of shared knowledge, summed up by experts (scientific community), recognized as the most advanced form of knowledge. This type of knowledge covers, among other dimensions, the organizational routines or the organizational memory. Organizational memory represents the register of an organization, represented by a set of documents and artefacts. Its goal is to expand and amplify knowledge through its acquisition, organization, dissemination, usage and refinement. Organizational memory can be a way of registering tacit knowledge, making it explicit, so that through business processes it becomes part of the patrimony of the company, to be shared and recreated.

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Florinda Matos, Albino Lopes and Nuno Matos The structural capital result from the way of the know‐how belonging to people is embedded in the company, producing organization, providing answers to customer needs. Also, the ability to attract and retain skilled people is the structural capital, where they decided the processes of recruitment and selection, career development, reward systems, motivation, challenging tasks, internal organization, among others. Organizations have their own history which is documented through computerized files or files on paper resulting from routines that are being assimilated and in standardized in procedures manuals. Access to this information is facilitated through information management, held with the support of information technologies and communication. We can say that the capital process includes all powers to the customer orientation and all internal routines. The management of the intellectual capital of a company is thus a very important task which is to create processes that facilitate the creation of more structural capital. When companies invest in innovation and development, they make people's skills incorporated into structural capital. An example of this are the companies that use CRM systems (Customer Relationship Management) that incorporate the knowledge of individuals and transform into better skills in relationship management market. Clients Capital The Clients Capital is the result of the interaction Structural Capital / Tacit Knowledge. This typology represents the organizational knowledge in its practical form and is already incorporated into the tacit experiences formalized in the team. This knowledge, although hidden, becomes accessible through interaction, and it is the main characteristic of the performance of highly specialized teams. The customer capital arises when individuals are able to create solutions (products or services) to meet the needs or solve customer problems. The customer relationships that allow the formation of this capital, stable over time, requires a continuous work to establish long lasting relationships. Market research and analysis of customer satisfaction are some of the actions that can measure the image that customers have of the company. The systematization of the results of these studies, standards and procedures, is an example of structural capital transformation into clients’ capital. The customers are difficult to retain, whereby knowledge of the company must be invested in processes that facilitate the fixation of these clients. The correct use of networks and new technologies is crucial in interacting with customers and therefore to build a stable clients capital. Clients focus, assumes that there is a continued investment in innovation and development in order to meet needs previously scheduled. The clients’ capital thus includes all the knowledge that the company has in the market, including indicators to know the size of the target market and market potential, clients’ preferences, the purchasing decision factors and reputation or image of company in the market. The clients’ capital can be valued by upgrading skills of individuals and teams.

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Florinda Matos, Albino Lopes and Nuno Matos The analysis of the movements of clients’ capital should enable to predict in which direction they move the company's financial forecasts. NTIC (New Technology Information and Communication) and Network In the presented Model the Network and NTIC are essential in the relationship between the 4 Quadrants. Thus, the companies that put the NTIC at the service of human resources have a great advantage, because they can reduce the administrative difficulties in solving simple problems, increase the quality of services and promote continuous improvement and personal growth. ICM parameters are: I ‐ Individual Capital Quadrant Training / Qualification and Talent management Valuation of Know ‐ How and Innovation Investment in Innovation and Development (ID) Existence of a Policy for Talent Retention II – Team Capital Quadrant Training / Qualification Team Team Work Innovation in teams Leadership in teams III – Processes Capital Quadrant Processes Systematization Registration of Organizational Knowledge Existence of Certification, environmental and social policies Partnerships Investment in Innovation and Development (ID) The Brands Creation and Management Complaints System The Existence of Awards IV – Clients Capital Quadrant Market Audits Management of the Clients' Satisfaction Clients Complaints System New Markets and Internationalization Networks New Technologies of Information and Communication

4.1 Limitations of ICMA® support model The audit instrument of intellectual capital management presented considers a set of parameters and indicators that should be revised because they are incomplete in some analyses, especially considering the national and international financial environment and the new trends of intellectual capital, namely intellectual and social capital and green intellectual capital. Although it is estimated that the results of the implementation of the ICM in large companies is similar to those found in SMEs, the proposed model has not been studied in these companies, so it is not actually possible to make a generalization of the parameters of the same model. Also, at the sectorial level there have been no specific studies, since the considered samples, in empirical studies, involved several sectors, so there is no data to justify if the application of the model in certain sectors is more advisable than in others.

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Florinda Matos, Albino Lopes and Nuno Matos Currently, the ICM does not allow the quantification of benefits for the companies and their partners, of the audit of the intellectual capital management. Brief description of procedures for accreditation The ICMA® process consists of several distinct phases: Preliminary Inquiry According to the request of the company that wants to start the accreditation process, the accrediting body provides a preliminary questionnaire. This questionnaire shows that the company meets the minimum conditions for continuing the process of accreditation. Preliminary Eligibility After examining the preliminary investigation, the accredited entity sends the company the results of this questionnaire. If the preliminary assessment of the application for accreditation is favourable, the company will be invited to formalize its application. Formal Requests The formal request is made online, through the website of the accredited entity. This request is composed of several documents available on the same site. Access to these documents is done by assigning a password. The process is automatic, after completing the formal application. Self‐Assessment After completing the application form, the company is invited to perform a self‐assessment that allows them ® to make an initial diagnosis of their situation in terms of the ICMA standards. This process is based on the ® ICMA Guide. During the process of self‐evaluation, the company can clarify issues with the accrediting body. As a result of self‐evaluation, a report will be produced, which will be forwarded to the accredited entity. This report must have a diagnosis of the firm's position agains ICMA® standards and will indicate the period from which the company may be audited by ICMA® consultants, usually a period between 1 and 3 months. Audit On dates agreed with the company, it is visited by ICMA® auditors, usually three. The auditors use the ICMA® Audit Guide to evaluate the company. In the final evaluation the auditors produce a report that includes the improvements to be implemented in the company so that it can meet the ICMA® criteria. The auditors can produce four types of decision:

The accreditation was rejected because the company does not meet the minimum criteria for accreditation.

The company needs to go through a period of Guided Development. In this period, the company implements the improvements which meet the ICMA® criteria. This period of introduction of measures will be accompanied by an ICMA® auditor and have a maximum duration of one year. When the auditor finds that the company is prepared to be audited again, he invites the company to make a further formal request for accreditation.

The company may have Conditional Accreditation. This may happen if all significant criteria are satisfied and in general the company deserves immediate accreditation. In this case, a report of procedures to improve is produced. The company will agree to correct procedures in the time indicated by the auditors.

The company may have Accreditation, if it meets all ICMA® criteria.

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The process is designed to facilitate the companies which can develop the accreditation according to their particular situation.

Eligibility When the auditors' report is favourable to the accreditation, the company is deemed eligible, and a Report of ICMA® Acceptance is produced. This report should clearly state the reasons supporting the accreditation. ICMA® The Report produced by the team of auditors is signed by them and by the company. The Report is submitted to the Accreditation Committee which, after consideration, should grant accreditation. If the Accreditation Committee still has some doubts about the company, it may seek further information from the company and/or auditors. The final decision is always made by the Accreditation Committee. The accreditation committee will meet generally four times per year to make the decisions regarding accreditation. Following a decision of accreditation, the company is formally notified, and the process of accreditation is completed.

5. Conclusion In terms of practical implications, the creation of a recognized methodology for accreditation of intellectual capital management, based on ICM, may will be a very important tool in validating the innovation capacity of SMEs and thus to assess their sustainability in the medium and long‐term. A theoretical presentation of the accreditation system involves the development of several concepts that facilitate their practical implementation to SMEs. ® Emphasize, the ICMA intended to be a unifying system. However, ICMA respects the specificities of SMEs from different countries. Moreover, many empirical studies show that the management of intellectual capital is associated with high performance and therefore organizational accreditation act as a seal of guarantee of sustainability, innovation and competitiveness.

References DATI ‐ Danish Agency for Trade and Industry (1999). Developing Intellectual Capital Accounts. Experiences from 19 Companies, Ministry of Business and Industry, Copenhagen. DMSTI (2003a). Analysing Intellectual Capital Statements. Copenhagen: Danish Ministry of Science, Technology and Innovation. Available: http://www.pnbukh.com/files/pdf_filer/Analysing_Intellectual_Capital_Statements.pdf [15 January 2012] DMSTI (2003b). Intellectual Capital Statements – The New Guideline. Copenhagen. Denmark: Danish Ministry of Science, Technology and Innovation. Available: http://en.fivu.dk/publications/2003/intellectual‐capital‐statements‐the‐new‐ guideline [15 January 2012] FRAME Project (2003). Available: http://www.nordicinnovation.net/article.cfm?id=1‐853‐168 [20 May 2012] García ‐ Parra, M. (2001). La información contable de los activos intangibles. PhD research. Madrid: Universidad San Pablo Ceu. IADE‐CIC (2003). Modelo de medición y gestión del capital intelectual: Modelo Intellectus. Instituto de Administración de Empresas. Universidad Autónoma de Madrid. Available: www.iade.org/ [20 May 2012] InCaS (2006). Intellectual Capital Statement (ICS) – Made in Europe. Available: http://www.incas‐europe.eu/ [16 April 2012] Johanson, U., & Nilson, M. (1996). The usefulness of human resource costing and accounting. Journal of Human Resource Costing and Accounting, 1(1), 17‐138. Matos, F., & Lopes, A. (2009). Intellectual Capital Management – SMEs Accreditation Methodology. In Stam, C., & Andriessen, D. (Eds.): Proceedings of the European Conference on Intellectual Capital, 28 – 29 April 2009 (pp. 344– 354).The Netherlands, Haarlem: INHolland University of Applied Sciences. Academic Publishing Limited.

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Florinda Matos, Albino Lopes and Nuno Matos Meritum (2001). Intellectual capital guidelines for firms. Available http://ec.europa.eu/research/social‐ sciences/projects/073_en.html [10 April 2012] Meritum (2002). Guidelines for managing and reporting on intangibles. In Cañibano, L., Sanchez, M. Garcia‐Ayuso, & C. Chaminade (Eds.). Fundación Airtel Móvil. Mouritsen, J.; Bukh. P., & et al. (2003). Intellectual Capital Statements – the New Guideline Copenhagen: Danish Ministry of Sciences Technology and Innovation. Nash, H. (1998). Accounting for the Future, a disciplined Approach to Value‐Added Accounting. Available: http://home.sprintmail.com/~humphreynash/future_of_accounting.htm [21 January 2012] RICARDIS (2006). Reporting Intellectual Capital to Augment Research, Development an Innovation in SMEs. Available: http://ec.europa.eu/investinresearch/pdf/download_en/20062977_web1.pdf [17 May 2012] Roos, G., Pike, S., & Fernström, L. (2005). Intellectual Capital: Management approach in ICS Ltd. Journal of Intellectual Capital, 6(4), 489‐509. Sveiby, K. E. (2010). Methods for Measuring Intangible Assets. Available: http://www.sveiby.com/articles/IntangibleMethods.htm [05 March 2012] Williams, M. (2000). Is a company’s intellectual capital performance and intellectual capital disclosure practices related?

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Intellectual Capital and the System of Organisational Management Ludmila Mládková University of Economics Prague, Prague, Czech Republic mladkova@vse.cz Abstract: Intellectual capital is perceived as an important factor of the success of an organisation. This paper discusses the link between the system of organisational management and intellectual capital. The system of organisational management is a set of methods, techniques and approaches, usually divided into subsystems and interlinked to logical complexity. It is used for organisational management. Systems of organisational management are based on the prerequisite that organisation is a social system and that any resource (intellectual capital including) must be used in relation to other resources, e.g., in a systemic way. If the system of management is not complex, the exploitation of the resource may be less productive, even counter‐productive. Experience shows that organisations that manage to balance individual elements of their corporate system create a stable environment that can more efficiently answer changes and challenges of the external environment. In such a balanced corporate system individual elements create a positive synergic effect. E.g., if organisations want to be successful in the long run, they should build a proper system of organisational management. Intellectual capital is often perceived as one of many subsystems of the system of organisational management. This paper disagrees with this approach as it leads to underestimating the role of the intellectual capital in an organisation. The paper argues that intellectual capital can serve as an independent system of organisational management. The paper is a theoretical paper. It gives definitions of intellectual capital and explains the term of the system of organisational management and its role in organisations. The analysis of the relationship between intellectual capital and the system of organisational management is made. The theoretical analysis shows that concepts of intellectual capital and some concepts of the system of organisational management are of similar basis, e.g., intellectual capital can serve as an independent system of organisational management. This idea is also demonstrated by practical examples. Keywords: intellectual capital, the system of organisational management, system, synergy

1. Introduction This paper is a theoretical paper that discusses the relationship between intellectual capital and the system of organisational management. Intellectual capital is often perceived as one of many subsystems of the system of organisational management. The paper disagrees with this approach as it leads to underestimating the role of the intellectual capital in an organisation. The paper argues that intellectual capital can serve as an independent system of organisational management. To provide evidence for the idea that intellectual capital is an independent system of organisational management, theoretical research of relevant fields was made. The methodology used is the methodology typical for theoretical research. The data are secondary data collected from traditional and electronic media. The article pays attention to both historical approaches and the latest approaches in the field. The methods used for the review of the literature include typical methods of theoretical work, e.g., methods that allow interlinking separated pieces of knowledge such as analysis and synthesis, comparison, induction, deduction, abstraction, generalisation and critical thinking. The theoretical analysis shows that the concept of intellectual capital and some concepts of the system of organisational management are of a similar basis. The autopoietic concept of the system of organisational management is actually a different formulation of the canonical model of intellectual capital. This finding is supported by examples from practical life given at the end of the paper. The paper contributes to the academic debate by discussing the role of intellectual capital from a systemic perspective. Based on the findings discussed in the paper, intellectual capital is a distinctive system of organisational management, not only a part of such a system. As such, it has the potential to create a stable environment that benefits from synergy and can more efficiently answer changes and challenges of the external environment (Senge, 1990). The paper discusses relevant topics in the following way. First, the field of intellectual capital is examined and the ideas of different authors on definitions and classifications of the term intellectual capital are provided.

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Ludmila Mládková The same approach is used for the topic of the system of organisational management. Important ideas of different authors are collected and critically evaluated. Then similarities and differences between the concept of intellectual capital and the system of organisational management are identified and analysed. Finally the paper gives evidence of functional systems of organisational management based on intellectual capital identified in the practical world.

2. Intellectual capital Intellectual capital is perceived as an important factor of the success of an organisation. Some authors consider that intellectual capital creates a significant part of the market value of organisations (Bureš, 2007). There are many models of intellectual capital defined in the appropriate literature. For example, Scandia’s (Roos et al, 1998) model of intellectual capital consists of two main parts; human and structural capital. Structural capital is then divided into customer and organisational capital. Organisational capital divides further into innovation capital (created by intellectual ownership and intangible assets) and process capital. Human capital includes all knowledge, abilities, expertise and creativity of the employees of an organisation. Some parts of structural capital are also strongly connected to humans. For example, customer capital includes both customer databases and relationships with the customer. Relationships with the customer are also linked with employees (Bureš, 2007). The model of intellectual capital described by Brooking (Brooking, 1999, Bureš, 2007) consists of four parts; market assets, intellectual ownership, infrastructure and human assets. Sveiby (Sveiby, 1997) divides intellectual capital into structural capital, human capital and customer capital. Petrash (1996) offers a similar classification. He divides intellectual capital into human capital, customer capital and organisational capital. As Bratianu writes, the canonical model of intellectual capital is composed of three basic entities: human capital, organisational/structural capital, and customer/relational capital (Sveiby, 1997; Bratianu, 2012). Due to the simplicity and practical impact this concept is used as the leading concept for this article. Basic entities of the concept are understood in the way Bratianu understands them, e.g., human capital contains all the knowledge, experience, skills, intelligence and values of all employees. Human capital is important for its capacity for knowledge creation, sharing, transfer and transformation. As Jedinák and Šugár (2011) write, human capital is the most precious resource of any organisation and organisations should pay it due attention. Structural capital contains all the intangible structures within a given organisation, structures that reflect formal and informal relationships established between people and groups of people, as well as operational routines and processes, software systems and platforms, intellectual property, and organisational culture. Relational capital reflects relationships between the inner business environment and the external business environment of any company. It is the intellectual capital component crossing the functional interface between an organisation and its environment (Bratianu, 2012).

3. System of organisational management The system of organisational management is a set of methods, techniques and approaches, usually divided into subsystems and interlinked to logical complexity. It is used for organisational management. Systems of organisational management are based on the prerequisite that any resource must be used in relation to other resources. If the system of management is not complex, the exploitation of the resource may be less productive, even counter‐productive (Truneček, 2007). Relevant literature offers three different types of systemic approaches to organisational management. They are systemic approaches to organisational management based on division of labour, dynamic approaches and autopoietic approaches. Systems of management based on the division of labour classify activities to subsystems by the principle of specialisation. These systems work well especially in a stable linear environment, where an organisation can predict the future behaviour of its constituents with a high probability. Many such systems were developed for hierarchical organisational structures and the environment of centralised decision making and control (Mládková, 2010). One of the first who created a complex system of organisational management was Henry Fayol (Veber, 2000; Veber, 2009). His system is built of six areas of corporate activities; technical, commerce,

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Ludmila Mládková financial, security, accountancy and administrative (managerial). The administrative area consists of five administrative activities; prediction (planning), organising, directing, coordination and control. These five areas represent what we call today, managerial functions. The whole system is accompanied by 14 principles of management: division of labour, authority and responsibility, discipline, unity of command, unity of direction, subordination of individual interest to general interest, remuneration of personnel, centralisation, lines of authority, order, equity, stability of tenure of personnel, initiative and esprit de corps (Fayol, 1949; Veber, 2000; Veber 2009). The works of German sociologist Max Weber represent another early attempt in the field of the systemic approach to management. Weber’s system works on mechanical principles where every individual knows his duties and rights. In his researches in churches and the army Weber discovered some principles of management that are generally applicable and summarised them in a concept called rational bureaucracy (Veber, 2000). Weber emphasizes that bureaucratic organisations are an attempt to subdue human affairs to the rule of reason and that it is possible to conduct the business of the organisation according to calculable rules (Borgatti, 2010). Max Weber highlights six principles: formal hierarchical structure, management by rules, organisation by functional specialty, managers as salary officials, employment based on technical qualification, written documents. “Authority and responsibility are clearly defined and officially sanctioned. Job descriptions are specified with responsibilities and line of authority. All employees have thus clearly defined rules in a system of authority and subordination.” (Cutajar, 2010). Luther Gulic and Lyndall Urwick built on the work of H. Fayol and focused on the systemic management of internal managerial functions. Gulic created a classification of managerial functions called POSDCORB. The acronym includes seven basic functions – planning, organising, staffing, directing, coordinating, reporting and budgeting. As stated by the author, every executive should execute them as they are necessary for efficient management of corporate activities (Gulick, 1937; Veber, 2000). Urwick believed in social engineering and thought that employees should be managed in an impersonal way. He integrated Fayol’s administrative approach with Taylor’s scientific management (Parker & Ritson, 2010; Veber, 2009) and the human relations movement (Parker & Ritson, 2010). Urwick stressed that an organisation must understand its purpose, which influences its behaviour. Tomáš Baťa, a Czech entrepreneur, developed a simple but functional system of management based on planning, cost centres, self management of workshops, clear responsibilities of managers, weekly accountancy, participation of profit and loss and a stress on order and discipline (Veber, 2009). Dynamic approaches to a system of organisational management reflect changes from a static to a turbulent global environment. Peter Senge, the author of the concept of a learning organisation and Elliot Jaques a proponent of the theory of stratification represent these approaches. Senge (1990) understands the corporate system as a group of interlinked elements that depend on each other and that create a complex and unified whole. The structure of the system influences its behaviour and the behaviour of its parts. Senge’s system of management is based on five disciplines that when used in a balanced way promote organisational learning. Such an organisation is perceived as a learning organisation. Disciplines that support organisational learning are personal mastery, mental models, shared visions, team learning, system thinking. Personal mastery is the ability of individuals in an organisation to learn and develop continuously. It is important because individual learning is a basic prerequisite of organisational learning. Mental models are unconscious deeply rooted personal images that influence how we understand reality and respond to external and internal incentives. Mental models may accelerate or limit both individual and organisational learning. A shared vision is the ability to create and share objectives in organisations. Fully accepted shared visions motivate employees. Team learning represents the interaction of individuals and teams and leads to the acceleration of organisational learning. System thinking is a toolset of how to understand the world in a complex way (Senge, 1990, Mládkova, 2003). Elliot Jaques addresses the system of organisational management in a different way than the already mentioned authors. He divides an organisation into seven so called strata (Jaques, 1996). Each stratum is a level represented with certain decision making complexity. The higher the stratum, the more complex are tasks from the point of decision making and the more cognitive capacity they require from the work performer. To achieve effectiveness of the system of organisational management, it is important that the level

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Ludmila Mládková of work matches the current cognitive capabilities of the person in the role. Such a person can make more effective decisions and sound judgments (Stamp, 1992). The level of work in a layer (stratum), according to Jaques is the “target completion time of the longest task, project or programme assigned to that role” (Jaques, 1996). In terms of time‐span, Jaques has distinguished between very concrete levels of cognitive ability to the very abstract where a person has the capability of seeing several different possibilities and consequences and relating one possible outcome to the others. Jaques argues that when organisations’ hierarchies correspond to his identified strata, and when people have a clear picture of what is expected of them, companies can achieve a “requisite organisation”, allowing people to reach their full potential. The aim therefore is not to do away with or undermine hierarchies, but to rather make them work more effectively (Ross, 1992). An Autopoietic approach is the latest approach. According to Truneček (2007) an autopoietic system is an internally differentiated system that defines its relevant external environment, and keeps semi‐permeable borders with its surroundings. These borders are the result of internal processes which influence one another and are responsible for regeneration of system elements. Autopoietic systems of management search for inspiration in living cells. The theory of autopoiesis classifies self‐managing systems as autopoietic. Autopoietic systems develop independently from themselves and manage themselves. Such a system of organisational management simulates the behaviour of living organisms (Truneček, 2007). Autopoietic systems of organisational management do not fully abolish the previous two models of system of management. A modern system must have some structure similar to those based on the division of labour and be as dynamic as the system of Senge or Jaques. Compared to the two previous types of system, autopoietic systems put stress on different factors, mostly on human cooperation and knowledge sharing. Autopoietic systems are characteristic by displaying a flat organisational structure, suppression of the model of sub‐ordinance and power of one employee over the other, stress on permanent learning and the ability to self‐manage. Organisations in which this system was identified usually employ a high number of knowledge workers (Mládková, 2010). A system of organisational management based on six bubbles provides the example of autopoietic systems (Truneček, 2004).

Figure 1: System of organisational management based on six bubbles This system of management classifies all managerial activities into six organisational subsystems; strategy, corporate processes, culture, IT architecture, organisational structure and human capital. Six subsystems of this system of organisational management intercommunicate one with each other and are kept together by organisational knowledge. Organisations should optimise individual subsystems so that they create synergy, which is the benefit of this type of system of organisational management (Mládková, 2010).

4. Intellectual capital and the system of organisational management If understood as one of elements of the system of organisational management, intellectual capital must be synchronised with other elements of the system. If elements of the system are not synchronised, the system does not create synergy and an organisation cannot fully use the potential of their resources (Truneček, 2007). Historically intellectual capital played the role of elements of the system of organisational management. This was typical especially for systems of organisational management based on the division of labour (although attempts to make the intellectual capital the leading concept of the organisation can be found ‐ Baťa system).

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Ludmila Mládková Dynamic approaches offered a bigger role to individual parts of intellectual capital but they did not allow the exploiting of intellectual capital in a systemic way. Elliot Jaques’ approach gives precedence to human capital. Also some relationships between strata and structural capital (organisational structure) can be identified. Great attention is also paid to the clear definition of relationships in the organisation. Though Elliot Jaques’ approach is unique, it does not create an environment for systemic exploitation of intellectual capital. P Senge’s Learning Organisation also focuses mainly on human capital and its development (personal mastery, mental models, team work). The problems of structural capital are covered by the discipline of shared vision. Relational capital is not included even though some foundations may be seen in the discipline of system thinking. The autopoietic approach offers the intellectual capital a different role. Looking at the concept of six bubbles, all three basic dimensions of intellectual capital, e.g., human capital, organisational/structural capital, and customer or relational capital (Sveiby, 1997; Bratianu, 2012) can be identified there. Human capital is one of the bubbles (elements); the bubbles; corporate processes, IT architecture, and organisational structure are made of the tangible part of organisational/structural capital. The bubble culture, includes the intangible part of the intellectual capital. Customer/relationship capital creates the content of the bubble strategy. Strategies of organisations reflect relationships between the inner business environment and the external business environment of the company. Of all the elements, intellectual capital itself is a strong and powerful system of organisational management if the autopoietic approach to the system of organisational management is adopted. The autopoietic concept of the system of organisational management is actually a different formulation of the canonical model of the intellectual capital (Sveiby, 1997; Bratianu, 2012).

5. Examples of intellectual capital as the system of organisational management Three examples of intellectual capital serving as a highly functional system of organisational management are provided. The first example is an Indonesian travel agency Perama. The managerial concept of Perama is based on all three components of intellectual capital (Sveiby, 1997; Bratianu, 2012). Customer/relational capital refers to a strategy defined as increasing the flow of visitors/tourists and developing tourist destinations with a mission motto “Provide easiness & safety at a reasonable price, all for your convenience and to preserve the environment and raise people’s awareness”. Organisational/structural capital includes a culture based on cooperation and integrated promotion. The motto is “one mind, one word, one action”. Organisations also promote the so called BISA culture: berish=clean, indah= beautiful, sehat= healthy, aman= safe. Another part of organisational/structural capital, organisational structure is built of 6 operational divisions. They are own business, finance department, trainee‐ education & training, associate businesses, wholesale, promotion & marketing. Processes are not mentioned in materials provided by the organisation but they are defined and used. The IT architecture of the organisation is very good. Human capital management is based on the corporate motto “Perama is people’s daily life” Employees really understand it this way because Perama is a highly credited employer in the region. The organisation distinguishes several levels of employees ‐ trainee, apprentice, employee, self‐employed, businessman, investor, organiser. An employee may develop through all these levels. They also define demands on leaders who are supposed to be humble, upright, honest and sincere, wise, brave (Perama, 2012). The company owner promotes the idea that nothing will last forever except change itself and he tries to adjust the behaviour of the company to the external environment. This approach gives Perama flexibility but also stability concerning the quality of service. The second example is the Baťa Company. Even though their system of organisational management was based on the division of labour and the term intellectual capital was not known in the time the system was founded this company worked as a knowledge company. Their major advantage was intellectual capital and the ability to use it. Baťa’s system was built on high ethical and moral values and is known for its relations to social responsibility (Zelený, 2010). The organisation had a very strong strategy and link to both national and international customers. The owners of the company forced strong ethical and moral values with the company

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Ludmila Mládková paying great attention to social development and service to the society and communities in its surroundings, both local and global. Corporate processes were focused on innovation and originality. Corporate culture was focused on cooperation and knowledge sharing. The organisational structure supported autonomy of workshops; employees participated in profit and losses. Of course, IT did not exist in those days and the reporting system followed the system of plans, quarterly, monthly and weekly. The organisation understood human capital as the leading force. They have their own schools and training programmes and special programmes for talented people (Zelený, 2010). The third example is a knowledge society developed by the Aboriginal people Nhunggabarra. Nhunggabarra people lived on the border of south Queensland and northwest New South Wales. Nhunggabarra created a knowledge society based on intense work with knowledge that as historians and anthropologists suspect lasted and prospered for thousands of years (Sveiby, Skuthorpe, 2006). It is possible to say that this society was based on P Senges’s concept of a learning organisation. From what we know about Nhunggabarra communities, their system of management was similar to all the five factors highlighted by P Senge (Senge, 1990). All Nhunggabarra people shared one vision and they had a sophisticated system of development of personal mastery, and they actively worked with mental models and preferred community (team) work. As for system thinking their knowledge about their environment and the interrelations between things, people, nature and the whole social and environmental system was so deep that their society existed for a very long time. They also had an extraordinary system of sharing knowledge. The Nhunggabarra system of organisational management also fits the requirements of the autopoietic concept. The community strategy (customer/relational capital) was: “Keep all alive”. Nhunggabarra felt responsible for their world, plants, animals, and their environment and for community members and their well being. They behaved as good gardeners and game keepers and kept their fragile environment in balance. The structure of the community (organisational/structural capital) was based on equality. All members of the Nhunggabarra society were credited as important and all had the same importance to the society. Nobody was more important than others, which prevented the rise of hierarchies and supported flat and equal structures. Their methods of how to undertake everyday tasks were clearly defined. Deep knowledge of their environment, a sense for interconnectedness of everything that happens and the awareness of the principles of their system dynamics built a friendly and supportive culture. As for human capital, every Nhunggabarra men and women had clear social and environmental responsibilities. These responsibilities were inherited and they made boundaries of what they could and could not do. The community paid huge attention to personal development of the individual in his or her given field. They used various tools to share knowledge – learning by doing, observation and stories. Every man and woman was a knowledge worker responsible for certain knowledge or its part and had clear social and environmental responsibilities related to the knowledge for which he or she was responsible. Nhunggabarra had built an educational system based on apprenticeship and storytelling. So as not to lose knowledge because of death or some incident, the Nhunggabarra doubled their knowledge workers. When the person who owned certain knowledge died, there were always other people who had the same knowledge and the same type of responsibility. Knowledge doubling and perfect health of the population led the Nhunggabarra people to be confident that the system was protected against the loss of knowledge. They developed something like a script but they used it only for some special pieces of knowledge, everything else was stored in tacit form (Kelemen, 2010). To be fair it is necessary to stress that even though Nhunggabarra system was very sophisticated and worked well for a very long time, the society disappeared. Over‐dependence on tacit knowledge is blamed for the end of the Nhunggabarra flourishing society. It is assumed that the whole totally failed during the severe smallpox epidemic that hit the Nhunggabarra people sometime around 1829‐1831. Too many people died in a very short period which led to a huge loss of knowledge. Losses in knowledge led to the total confusion of those who survived because they suddenly missed important guidelines on how to act and behave.

6. In conclusion This paper has provided an analysis of the relationships between intellectual capital and the system of organisational management. The findings that intellectual capital can serve as an independent system of organisational management were demonstrated by theoretical research and practical examples. Theoretical data and practical examples showed that intellectual capital in the role of a system of organisational

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Ludmila Mládková management provides an environment where any corporate resource can be used in relation to other resources as required by Truneček (2007). The topic discussed above opens the field for further research. Especially the question of a why some companies, even societies create a successful system of organisational management and some do not, is very interesting. There are many factors that may be responsible and need to be examined in more detail; one of them is the factor of internal cooperation. Organisations that support internal cooperation seem to be more successful than those that support or do not eliminate internal competition. When competing, managers responsible for individual parts of the system tend to see their field as the leading one, underestimate the needs of other fields and fight with them for resources. In such a situation the system of organisational management becomes unbalanced. The theory of games supports this idea by the discovery that cooperative strategies always lead to better results than competitive ones (Mládková, 2010) but of course, the idea requires proper research in the future.

References Borgatti, S. P. (2010) Weber’s Rational Bureaucracy. Retrieved from http://www.analytictech.com/mb021/bureau.htm 4.10.2010 19:04. Bratianu, C., Orzea, I. (2012) The Gordian Knot of the Intellectual Capital of Universities, ICICKM 2012, The Universidad Del Rosario and the Universidad Jorge Tadeo Lozano Bogotá, Colombia, ISBN 978‐1‐908272‐72‐0 Brooking, A. (1999) Corporate Memory. Thomson Business Press. London. Bureš, V. (2007) Znalostní management a proces jeho zavádění. Grada Publishing, a.s. 2007. ISBN 978‐247‐1978‐8. Pg. 31‐ 36. Cutajar, M. (2010) Max Weber Bureaucracy Theory. Retrieved from http://www.suite101.com/content/max‐weber‐ bureaucracy‐theory‐a267433 4.10.2010 18:54. Fayol, H. (1949) General and industrial management. Retrieved fromhttp://www.hrmguide.co.uk/history/classical_organisation_theory_modified.htm 4.10.2010 Gulick, L. (1937) Papers on the Science of Administration. New York, Institute of Public Administration. Jaques, E. (1996) Requisite Organisation. Cason Hall & Co. 2nd Edition. USA. ISBN 1‐886436‐03‐7. Jedinák, P., & Šugár J. (2011). Method of evaluation interview in the Police of CR. Results of Research. Security Theory and Practice. 3/2011. Prague. ISSN 1801‐8211. Kelemen J. et al. (2010): Knowledge in Context. Iura Edition. SR. 2010. pg. 139‐172. Mládková, L. (2010) Systems of Organisational Management. Maneko 2/2010. Issue 2. Institute of Management of Slovak University of Technology in Bratislava. SR. Pp 171‐181. ISSN 1337‐9488. Parker, L.D., & Ritson, P. (2010) Rage, Rage, against the Dying of the Light: Lyndall’s Urwick Scientific Management. Retrieved from http://www.ncl.ac.uk/nubs/assets/documents/camh_leeparker.pdf 3.2.2012 22:05. Perama (2012) http://www.peramatour.com/ 8.11.2012 Petrash, G. (1996) Dow’s Journey to a Knowledge Value Management Culture. European Management Journal. 14 (4). Pg. 365‐373. Ross, A. (1992) The Long View of Leadership. Canadian Business. May 1992. Retrieved from http://www.canadiancentre.com/canbross.htm. 6.3.2012. 10:18. Roos, J., et al (1998) Intellectual Capital. Navigating in the New Business Landscape. New York University Presss. New York. 1998. Senge, P., M. (1990) The Fifth Discipline. Doubleday NY. ISBN 0‐385‐26095‐4. Stamp, G. (1992) Day of Judgement ‐ in Festschrift for Elliott Jaques, Cason Hall and Co. Publishers. Available at Grobler S. W. (2006). Organisational structure and Elliott Jaques’s Stratified Systems Theory. The Essence of Levels of Work. Retrieved fromhttp://www.google.com/search?hl=en&ei=705ITIuHDJSSjAew7MmlDg&q=Theory+of+stratified+systems&start= 30&sa=N 22.7.2010 16:14. Sveiby, K. E. (1997) The New Organisational Wealth: Managing and Measuring Knowledge‐Based Assests. 1997. Berrett‐ Koehler. Sveiby, K. E., Skuthorpe T. (2006) Treading Lightly. Allen & Unwin. Crows Nest. Australia. 2006. ISBN 1 74114874. Truneček, J. (2004) Znalostní podnik ve znalostní společnosti. Professional Publishing. 2. vydání, ISBN‐80‐86419‐35‐5. Truneček, J. (2007) Filozofie podnikového řízení pro 21. století. Sborník mezinárodní konference Svět práce a kvalita života v globalizované ekonomice. Retrieved from http://kvalitazivota.vubp.cz/prispevky/sbornik_mezinarodni_konference/sbornik_3.pdf 9.10.2010 11:34 Veber, J. (2000) Management, základy, prosperita, globalizace. Management Press. Praha. ISBN 80‐7261‐029‐5. Veber, J. (2009) Management. Management Press. ISBN 978‐80‐7261‐200‐0. Zelený M. (2010) Soustava řízení Baťa – základ pro evropský system podnikového řízení. Retrieved from http://www.milanzeleny.com/documents/bata_management_system_cz/zlin.pdf. 22.7.2010 15:28

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Validation Scale for Measuring Social Capital in Higher Education Institutions Fattah Nazem and Madankar Anahita Department of Education, Roudehen Branch, Islamic Azad University, Iran nazem@riau.ac.ir anahitamadankar@ymail.com Abstract: The higher education system has caused a great deal of changes in social, economic and political fields. In addition, universities are social systems which have been known as the center of knowledge and information as well as thinking bases for leading societies. In today’s complex, competitive world, social capital is considered as a competitive advantage for organizations. The purpose of the present study is to validate a scale for measuring the social capital in universities. The population of the study included all the staffs who were employed in branches of Islamic Azad University in Iran (administrative region 8, 12). The research sample consisted of 595 staffs randomly selected from 6 branches and educational centers using stratified random sampling. The research instrument was Abili and Abilis’ (2010) social capital questionnaire which consisted of 24 items with three underlying constructs of cognitive dimension, relational dimension, and structural dimension with Cronbach Alpha of 0.94. The results of factor analysis and principal components analysis, using a varimax rotation, showed that building blocks of social capital includes cognitive (Items 1, 2, 3, 4, 5, and 6), relational (Items 7, 8, 9, 10, 11, 12, and 13), and structural (Items 15, 16, 17, 18, 19, 20, 21, and 22. In this study, structural dimension has the highest level of contribution to the formation of social capital in universities. Keywords: social capital, varimax rotation, universities, higher education institutions

1. Introduction Higher education system is one of the most important and complicated products of human achievements. In recent decades, the changes have revolutionized the social systems and organizations; hence, scientific centers, in general, and universities, in particular, are more addressed to satisfy new expectations. Regarding the key role of higher education, Green (1997) explains that higher education provides the technical knowledge and skill that industry requires it in future and the fact that governments depend on this knowledge to have an effective and strategic programming. Because of profound changes which have affected our today's world, the universities, even more than ever, have been in the focus of prolonged international and social discussions which devote to the goals and ideals of universities as well as their roles in guidance and leadership (Feigenbaum, 1994).

2. Literature review The evolution from an industrial society to a knowledge society is characterized by the rapid growth of intangible assets and social activities with regard to tangible resources and physical based processes (Eurostat, 2000). Although intuitive, the notion of social capital is difficult to define, particularly at aggregate levels, it could be defined as a sum of current and potential resources incorporated in, available in and derived from the network of relations possessed by an individual or social unity (Nahapiet and Ghoshal, 1996). From a corporative point of view, which means that social unity is the organization, social capital could be understood, according to Bourdieu and Wacquant (1992), as a sum of resources accumulated in the organization by a stable network of intraorganizational relationships. In the same vision, Coleman (1990) defines the concept as the appropriateness of social structure, strategically legitimized in the organization. Leana and Van Buren (1999) introduce the term organizational social capital as an attribute of a social entity, and argue that the translation of individual into organizational social capital is crucial for an organization to reap the benefits that develop through its employees’ social exchanges. In this vein, organizational social capital can be understood as a resource reflecting the character of social relations within the firm that is realized through members’ levels of collective goal orientation and shared trust. The concepts of social capital seem to have been classified into three different groups:

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Fattah Nazem and Madankar Anahita Pfeffer, 2003).The cognitive dimension refers to those resources that provide shared representations, interpretations, and systems of meaning among parties. This includes shared language and codes as well as shared narratives, which increase the mutual understanding among individuals and help members to communicate more effectively. (Cabrera & Cabrera, 2005)

Relation dimension: McDonald (2003) has tried to include a motivational element into the design of expertise recommender systems. He augmented an expert recommendation system with social networks. So the recommender system would suggest first those experts who had the closest social ties with the person asking.

Researchers highlight social capital as being a crucial resource for accessing, exploiting and leveraging individual and collective knowledge, thereby providing its strategic value for organizations (Burt, 1992; Nahapiet & Ghoshal, 1998). Empirical studies have shown social capital to positively influence knowledge search (Nebus, 2006), product innovation (Tsai & Ghoshal, 1998) and inter‐unit feedback‐seeking (Barner‐ Rasmussen, 2003). From a conceptual perspective, social capital is argued to affect the creation of intellectual capital due to the social resources (e.g., access to information) embedded within social capital (Nahapiet & Ghoshal, 1998). Several findings of the studies show that social capital facilitates the creation of new intellectual capital in organizations (Reiche et al., 2009; Wu et al., 2008; Wu, & Tsai, 2005; Bueno et al., 2004; Nahapiet, & Ghoshal, 1998). Therefore, in order to increase social capital in universities, it should first be measured through a valid instrument. Consequently, necessary managerial actions should be taken. Taking social capital variable as a guarantee to survive and a competitive advantage for universities and the lack of a valid measurement tool for social capital and its components in universities were the main motives of the present research to design a valid instrument that identifies the constructs which form the social capital, measure the variable of social capital in each of the underlying dimensions, and find a way to strengthen the social capital in universities. The purpose of the present study is to validate a scale for measuring the social capital of universities.

3. Research questions

What are the indexes which construct the social capital in universities?

What items are included in each index?

Which of these indexes has more contribution in forming social capital in universities?

4. Method of the study The population of the study includes all the staffs who work in 6 branches and educational centers of Islamic Azad University. In order to estimate the least volume of sample, n =

z 2σ d 2

formula was used. The

research sample consisted of 595 staffs randomly selected from 6 branches and educational centers using stratified random sampling. The research instrument was Abili and Abilis’ (2010) social capital questionnaire which consisted of 24 items with three underlying constructs of cognitive dimension(Items 1, 5, 10, 15, 18, and 22), relational dimension(Items 2, 4, 6, 7, 8, 11, 13, 14, 17, 23, and 24), and structural dimension (Items 3, 9, 12, 16, 19, 20 and 21), with Cronbach Alpha of 0.94. The researcher has used factor analysis and principal components analysis, using a varimax rotation in order to identify the underlying constructs of social capital.

5. Findings of the study The preliminary analysis of different indexes of central tendency, variability, and the distribution of the staff’s scores obtained from the questionnaire of social capital and its 3 components show that the distribution of the staff’s scores in the given variables have tendency toward normality.

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Fattah Nazem and Madankar Anahita To answer the research questions, factor analysis procedure was applied. The first step in factor analysis process which is also its first assumption is checking missing data. In this step, subjects number 178, 404, and 565 including three persons altogether were eliminated from statistical analysis so that the factor analysis assumption under the heading of at least missing (0.02) could be observed in each subject. Hence, in this research no item has been eliminated except three subjects. And the given situation shows that there is no need to omit some of the items and it is possible to follow the process of Factor Analysis while having all the items. The second factor analysis assumption denotes enough sample size. In this research, Kaiser‐ Meyer‐ Olkin (KMO) equals 0.955 consequently, the sample size is sufficient. The third factor analysis assumption is normality of multi‐variation distribution known as Sphericity. As the Approximate Chi Square equalled 70840548 with the 276 degrees of freedom, it can be stated that the amount of the Approximate Chi Square is statistically significant and the given statistics is significant at least at the 0.999 level of confidence ( α = 0.001). According to component matrix of items we can determine both the specific factor of each item and its position in the related factor based on loading factor. After studying table of component matrix precisely, the researcher used Rotation Method so that loading factor of each item can be determined stressing at recognition of each item in one of the 3 factors. Reiterating that in this research, the researcher has followed Exploratory Factor Analysis and has used Principal Component Methods from Extraction of Factors, Varimax Method was applied (table 1). According to Varimax, the researcher was able to determine both the factor to which the item belongs after rotation and the position of each item in related factor with reference to loading factor. This table shows in which factor each item has been located after the rotation. For instance, Items 1, 2, 3, 4 5 and 6 have been located in the third factor (cognitive dimension). To fulfill the purposes of the study to determine the indexes of social capital and its components, the underlying items, and the index with the highest contribution, eventually, 3 factors have been extracted from rotation of factor analysis; in fact, social capital consists of 3 factors respectively as follows: structural dimension, relational dimension, and cognitive dimension. The table also indicates that structural dimension has the highest level of contribution to the formation of social capital in universities. Table 1: Rotated component matrix

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Fattah Nazem and Madankar Anahita Hence, emphasizing at the three‐fold factors of social capital, items related to each factor have been summarized in table 2 respectively. Table 2: Results of factor analysis of social capital construct Factors First Factor

Index Structural dimension

Second Factor Relational dimension Third Factor

Cognitive dimension

Items 15, 16,17,18,19,20,21,22 7, 8,9,10,11,12,13 1, 2,3,4,5,6

6. Discussion and conclusions The higher education system has caused a great deal of changes in social, economic and political fields. In addition, universities are social systems which have been known as the centers of knowledge and information as well as the thinking bases for leading societies. Social capital is a commodity necessary to guarantee organizational survival and universities’ competitive advantage in the complex and competitive world. In this study, in order to assess the social capital, Abili and Abilis’ (2010) social capital questionnaire was applied which contains 3 scales of structural dimension, relational dimension, and cognitive dimension. The results of factor analysis and principal components analysis, using a varimax rotation, showed that building blocks of social capital includes cognitive , relational, and structural. The 3 factors which were used to assess the social capital in this study agree with the theories and studies carried out in and out of Iran. Some of the similar studies done in the same field are as follows: Azizi(2011), Pérez‐Luño et al. ( 2011) Gorton(2010), Shiu(2008), Ameri Dahabady,A. (2007) , Yang ( 2006), Merlo et al. ( 2006.),Sinha(2004), Jafariyan(2000),and Nahapiet and Ghoshal(1998). In order to assess the social capital, they have also administered the questionnaires covering the structural, relational and cognitive dimensions. Regarding the research background and the related theories, the three factors involved in social capital show that the social capital scale almost generally covers the underlying factors. Hence, it can be concluded that the results obtained from the administration of the tool and the level of social capital in universities determined by the application of the tool as well as its validity are generally acceptable. The increasing need of universities for determining the level of social capital from the one side and the lack of valid instrument of the social capital from the other side were the main causes of doing the present study. In addition, the research was done to identify the precise and complete dimensions, aspects and factors which make social capital through measuring the validity of a scale which was designed and administered to the staff of higher education institutions. In this way, it is possible to locate the theoretical position of social capital and identify the importance of the variables which have been introduced by different theories as the factors which form social capital. The ultimate purpose of the study, then, is to design and administer a valid tool which can determine the extent of social capital in higher education institutions. According to Eurostat (2000), knowledge society is characterized by the relevant growing of intangible assets and social activities; due to the above‐mentioned factors, social capital is one of the forms of capital of the World Bank classification that is acquiring the greatest level of importance. As Lesser and Cothrel (2001) note that, social activities have an eminent role in the knowledge‐based economy. They are a set of critical resources that enable the creation of essential competences. Moreover, social activities increase the capacities for the creation, sharing and management of knowledge generating sustainable competitive advantages (Bueno, 2002). Bueno’s findings are in line with those presented by Adler and Kwon, in this respect, social capital provides the organization with values such as solidarity and cooperation, especially when interactions fix patterns of obligations and expectations based on rules of reciprocity and equality (Adler and Kwon, 2002). As Lazerson (1995) remarks that social capital solves conflicts, improves consensus with surrounding organizations, enhances the understanding with public administration, supports the development of business strategy, mitigates the imperfections of information in the market, and reduces transaction costs. According to Cohen and Prusak (2001), social capital represents the value of human connections based on confidence and on personal networks with a community vocation. Without social capital innovation, the sharing of knowledge and productivity can be dramatically reduced. According to Koening (1998) social capital facilitates the behavior rules of the organization, reducing transaction costs and promoting cooperation. These reasons justify the introduction of social capital into intellectual capital.

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Fattah Nazem and Madankar Anahita The questionnaire administered here also enjoys some psychoanalytic features, specifically construct validity. These are some of the reasons which lead the researcher to recommend that the same study be carried out not only in Islamic Azad University but in all other universities and its findings, in turn, be taken into consideration in those universities. In addition, with regard to the research findings of the present research and in order to increase the social capital in Islamic Azad Universities, it is suggested to increase the different dimensions of social capital including structural, relational and cognitive aspects, moreover, regarding the dominant and leading role of the structural dimension in building the social capital at universities, following suggestions are offered.

The employees' ideas should be welcomed.

The staff members receive constructive and legitimate criticism among themselves.

While confronted by problems, efficient and reliable methods are devised and followed.

Employees express and share the viewpoints and they also have access to the required information in order to reach informed decision.

The staff members facilitate the inter‐unit assistance and help among themselves.

Employees voluntarily provide the required information for their colleagues.

The close cooperation is encouraged in terms of the organizational issues and problems.

In conclusion, with regard to the crucial importance of the social capital as a competitive advantage, it's proposed that similar studies are carried out in different universities of the world and the newly-proposed results in this research can be effectively employed to enhance the social capital.

Acknowledgements This paper is extracted from a research project sponsored by the research department of the Islamic Azad University, Roudehen Branch to whom I owe a debt of gratitude.

References Abili,K., and Abili,M.(2011)Social capital management in Iranian knowledge‐based SMEs, ECIC2011, University of Nicosia, Cyprus,18‐19 April 2011. Adler, P. S. & Kwon, S. W. (2002) Social capital: prospects for a new concept. Academy of Management Review, 27(1), 17‐ 40. Ameri Dahabady, A. (2007) The relationship between social capital and organizational entrepreneurship in Mellat Banks , [M.A. dissertation], Iran, Islamic Azad University‐Tehran North Branch . Azizi, Z. (2011) The relationship between managers' emotional intelligence and intellectual capital with organizational justice in Islamic Azad University‐Kermanshah Branch , [M.A. dissertation], Iran, Islamic Azad University‐Roudehen Branch . Barner‐Rasmussen, W.( 2003) Determinants of the feedbackseeking behaviour of subsidiary top managers in multinational corporations, International Business Review, 12(1), 41–60. Bourdieu, P. and Wacquant, L.(1992) An invitation to reflexive sociology, University of Chicago Press, Chicago, IL. Bueno, E. (2002) Direccion estrategica basada en conocimiento: teoria y practica de la nueva perspectiva, in Morcillo, P .(Ed), Nuevas claves para la Direccion Estrategica de la Empresa, Ariel, Barcelona. Bueno, E., Salmador, M. P.,and Rodríguez, Ó.( 2004) The role of social capital in today's economy: Empirical evidence and proposal of a new model of intellectual capital , Journal of Intellectual Capital, 5( 4), 556‐574 . Burt, R. S. (1992) Structural holes: The social structure of competition. Cambridge, MA:Harvard University Press. Cabrera, E.F., and Cabrera, A., "Fostering knowledge sharing through people management practices," International Journal of Human Resource Management (16:5), 2005, 720‐735. Coleman, J. (1990) Foundations of Social Theory, Harvard University Press, Boston, MA. Eurostat (2000), EPROS‐The European plan for research in official statistics, EU, Brussels. Feigenbaum, A. V. (1994) Quality education and American’s competitiveness. Quality progress, 27(9). Gorton, M., Sauer,J. Peshevski, M.,Bosev, D., Shekerinov, D., and Quarrire, S. (2010) The dimensions of social capital and rural development: Evidence from water communities in the Republic of Macedonia, 118th EAAE Seminar, Ljubljana, Slovenia, August, 25‐27 2010. Green, A. (1997) Education and state formation in Europe and Asia, In K. Kennedy (Ed), Citizenship educational and the modern state, London: The Falmer Press. Hinds, P.J. and Pfeffer, J. (2003). Why Organizations Don't 'Know What They Know': Cognitive and motivational factors affecting the transfer of expertise, in M. Ackerman, V. Pipek and V. Wulf (eds.) Beyond Knowledge Management: Sharing expertise , Cambridge, MA: MIT Press, pp. 3‐26.

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Fattah Nazem and Madankar Anahita Jafarian,V. (2000)The relationship between social capital and organizational entrepreneurship, [M.A. dissertation], Iran, Mazandaran University. Koening, M.E.D (1998) From intellectual capital to knowledge management: what are they talking about?,64th IFLA General Conference, Amsterdam, August, PP.222‐33. Lazerson, M. (1995) A new phoenix: modern putting‐out in the Modena knitwear industry, Administrative Science Quarterly, 40 ,PP. 34‐59. Leana, C. R., & Van Buren III, H. J. (1999)Organizational social capital and employment practices, Academy of Management Review, 24(3): 538–555. Lesser, E. and Cothrel, J. (2001) Fast friends: virtuality and social capital, Knowledge Directions, Spring‐Summer, PP. 66‐79. McDonald, D.W. (2000). Supporting Nuance in Groupware Design: Moving from naturalistic expertise location to expertise recommendation, PhD Thesis, University of California, Irvine. Merlo, O., Bell, S. J., Mengüç B.,and Whitwell, G. J.( 2006.) Social capital, customer service orientation and creativity in retail stores, Journal of Business Research, 59( 12), 1214 . Nahapiet, J. & Ghoshal, S. (1998) Social capital, intellectual capital, and the organizational advantage. The Academy of Management Review, 23(2), 242–266. Nebus, J. (2006) Building collegial information networks: A theory of advice network generation, Academy of Management Review, 31(3): 615–637. Pérez‐Luño, A., Medina, C. C., Lavado, A. C.,and Cuevas, G.( 2011) How social capital and knowledge affect innovation, Journal of Business Research, 64( 12), 1369. Reiche, B. S., Harzing, A.‐w. and Kraimer, M. L.( 2009) The role of international assignees' social capital in creating inter‐ unit intellectual capital: A cross‐level model, Journal of International Sinha,M.(2004)Presence of community of practice : Its effect on social capital and competitive advantage of the firm , [M.A. dissertation], Canada, Concordia, University. Tsai, W. and Ghoshal, S. (1998), "Social capital and value creation: the role of intrafirm network", Academy of Management Journal, Vol. 41 No. 4, pp. 464‐76. Uphoff, N. (2001), "Understanding social capital: learning from the analysis and experience of participation",in Dasgupta, P. and Serageldin, I. (Eds), Social Capital: A Multifaceted Perspective, World Bank, Washington, DC. Wu, W.‐Y.,and Tsai, H.‐J.( 2005) Impact of social capital and business operation mode on intellectual capital and knowledge management, International Journal of Technology Management, 30( 1,2) ,147‐171. Wu, W.‐Y., Chang, M.‐L.,and Chen, C.‐W.( 2008) Promoting innovation through the accumulation of intellectual capital, social capital, and entrepreneurial orientation , R & D Management, 38( 3), 265. Yang, X.,( 2006) Social capital, cognitions, and firm innovation: Theoretical model and empirical studies , [Ph.D. dissertation].United States ‐‐ Virginia: Virginia Polytechnic Institute and State University.

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Intellectual Capital’s Leverage on Shareholder Value Growth: A Lesson for Developing Economies Bongani Ngwenya Solusi University, Zimbabwe ngwenyab@solusi.ac.zw nbongani@gmail.com Abstract: The purpose of this study is to evaluate whether there is a correlation between the intellectual capital of employees, i.e. top managers (the agent) and the shareholder (the principal) value growth among Zimbabwean companies, listed on the Zimbabwe Stock Exchange. By using these results the aim is to further examine a possible indicator for leveraging the efficiency of intellectual capital in a developing economy as a lesson. In order to perform this investigation, intellectual capital and shareholder value are quantified with, respectively, value added per employee and share price value per employee. These measurements are gathered from 17 Zimbabwean listed companies, divided into five industry sectors, that is, services, manufacturing, agriculture, mining and information technology, and tested statistically in order to find a relationship. This means, according to the researcher`s propositions on the return on intellectual capital, that there also is a correlation between intellectual capital and shareholder value growth. In order to provide an indicator for improving companies’ intellectual capital, a statistical examination concerning the relationship between the Intellectual Capital Multiplier (IC Multiplier) and value added is also performed. This examination shows that there is a strong correlation between the IC Multiplier and value added, value added can to a degree of 84 percent be predicted by the IC Multiplier, and that working with the ratio between structural and human capital is an excellent method for companies in developing economies to increase their intellectual capital. In conclusion it can be said that most companies in this investigation show moderately low values regarding the IC Multiplier, leading to an erosion of the companies’ human capital. In order to become more stable and lower the degree of risk, these companies must improve their IC Multiplier. What this study demonstrates is that an improvement of the IC Multiplier also will have an extensive effect on the company’s shareholder value growth. Keywords: intellectual capital, Value added per employee, structural capital, human capital, IC multiplier, shareholder value growth

1. Introduction According to Johnson (2002) the economies and individual companies have slowly transformed during the last few years, such that many companies of today have realized that their prime assets no longer consist of real estate and machine parks. He asserts that innovation and above all, knowledge of the workforce has hence evolved into one of the economy’s prime resource, more important than raw material and sometimes more important than money itself. Empirical evidence reveals that knowledge, like other physical and financial corporate assets that are firm specific, creates shareholder value and is generally expected to generate above‐ normal benefits (Lev, 2000).Information age companies or knowledge companies do not hire people for their physical abilities but for the ability to exploit their knowledge that is, intellectual capital. Literature is very clear with evidence that many practitioners and scholars agree that Intellectual Capital is of major importance, however a few are able to define and quantify it. Gu and Lev (2001, p.3) argue to the effect that, “One searches for measures of intangibles value in order to provide new information to managers and investors. What is the use of a measure that is derived from what investors already know? Estimating the value of intangible assets, through a different approach, is naturally something that seems of great importance”. Furthermore, the lack of means concerning the ability to make the Intellectual Capital visible leads to an inefficient basis for decisions for investors (Edvinsson and Malone, 1998). The researcher believes that the importance of Intellectual Capital and its measurement will grow substantially in the future and that this issue demands further enlightening through further future research. From here the researcher proceeds by way of discussing briefly the problem, highlights the research questions and suggested propositions or hypotheses, and reviews literature that informs the theoretical frame work of the study. After literature review the researcher discusses the research methodology employed in the study, data analysis and then finally highlights conclusions drawn from the study and recommendations thereto.

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2. Problem discussion Gone are the times when physical or tangible assets were the prime resources to steer desired performance of firms. In the knowledge economy that has risen, most companies now base their business on knowledge, compared to formerly, in which physical assets were regarded as only the prime resources. Some scholars in fact, suggest that in today’s economy, all firms are knowledge companies (Eneroth, 2002). The knowledge is possessed by the employees who convert it into value depending on their capabilities and the right support offered by the company. Several researchers reiterate that focus should now shift from cost control efforts to value addition strategy. In other words in order to manage and understand value creation within companies more modern management methods and new measuring tools are needed (Pulic, 2000). To conduct this study the researcher is making important assumption regarding the measurement of intellectual capital. The researcher uses Value added per employee as an approximation of the return of intellectual capital. The main question is how do companies manage to do this equally well and do the financial markets acknowledge this effort? However in today’s economy where the majority of the companies’ value laid in their Intellectual Capital the main research question can be further divided into sub‐questions formulated as follows?

Is there a relationship between the value of companies` Intellectual Capital and their market value?

Is there a relationship between Intellectual Capital and market value of the company easier to determine when companies within the same industry sector are compared?

Is the relationship between Intellectual Capital and market value of the company affected by the size of the company?

Does company age has any effect on the relationship between Intellectual Capital and market value of the company?

Is there any relationship between the companies` IC Multiplier and the companies` market value?

From the questions above the researcher has attempted to define the following proposition: Proposition 1: There is a significant relationship between companies’ value added per employee and their share price value per employee when comparing all the selected companies. Proposition 2: There is a significant relationship between companies’ value added per employee and their share price value per employee when comparing companies by industry sector. Proposition 3: There is a significant relationship between companies’ value added per employee and their share price value per employee when comparing companies divided by company size. Proposition 4: There is a significant relationship between companies’ value added per employee and their share price value per employee when comparing companies divided by company age. Proposition 5: There is a significant relationship between companies’ IC Multiplier and their value added per employee.

3. Literature review and theoretical framework The Intellectual Capital Perspective According to Peppard and Rylander (2001) the Intellectual Capital perspective has its origins grounded firmly within the practitioner and have evolved through practice. The two authors suggest that Intellectual Capital was pioneered in the early 1990s by a group of companies around the world. These were led by Skandia, a Swedish Financial Services firm. The Intellectual Capital perspective emerged as a result or response to the frustrations caused by traditional management tools and their ability to leveraging intangible resources. Peppard and Rylander (2001) further state that the concern of these companies had been of a new language and framework that would allow them to address the issues surrounding the true drivers of value creation in knowledge intensive firms. The main argument during this time was that the dominating financial view of the

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Bongani Ngwenya firm, which was the fundamental base of the management theories and tools by then, was not able to provide these companies with a comprehensive perspective of their process for creating value. The accounting framework therefore provides little guidance in understanding and managing the firm`s intellectual resources and their effect on value creation (Peppard and Rylander, 2001). Several academics have adopted the Intellectual Capital perspective as a useful framework for describing all of the resources a firm has at its disposal to create value (Bontis, 1996; 1998; 1999; 2001; Bonits et al., 1999; Pike et al., 2001: Roos et al., 1997; Rylander et al., 2000; Sveiby, 1997). Peppard and Rylander (2001) go on to suggest that some theoretical underpinnings of the Intellectual Capital literature draw on aspects of resources‐ based theory from the strategic management literature (Barney, 1991; Collis and Montgomery, 1995; Wernerfelt, 1984). Thus, the Intellectual Capital perspective offers a bridge between the conceptual thinking of the resources‐based theory and a practical approach necessary for the adoption of the framework by managers. Intellectual capital In the section above the researcher attempted to describe the perspective of Intellectual Capital. However in this section the researcher seeks to define Intellectual Capital. Johnson (2002) laments that, the definition of Intellectual Capital is far from obvious, because the opinions regarding which components to be included differ to a large extent depending on the researcher. The most common and straightforward definition of Intellectual Capital may well be, as stated earlier, the gap between market value and book value. According to Gu and Lev (2001) there have been attempts to estimate the value of intangible assets by using the difference between the market value and the book value of companies. This has, however proven to be inadequate since this approach is based on two incorrect assumptions. The first assumption is that the financial markets are efficient (the efficient market hypothesis), i.e. there exist no mispricing of shares, for example. The second incorrect assumption is that the assets on the balance sheet reflect their current values (Gu and Lev, 2001). Edvinsson and Malone (1997, p.24) define Intellectual Capital as “a combination of human capital – the brains, skills, insights and potential of those in an organization – and structural capital – things like the capital wrapped up in customers, processes, databases, brands and IT systems. It is the ability to transform knowledge and intangible assets into wealth creating resources, by multiplying human capital with structural capital”. Skandia was a pioneer in the area of defining, measuring and working with Intellectual Capital. They provided one of the initial classification schemes and their definition of Intellectual Capital was "the possession of knowledge, applied experience, organizational technology, customer relationships and professional skills" which later on has been simplified to human capital plus structural capital equals intellectual capital (Edvinsson & Malone, 1997, p. 65). Human Capital + Structural Capital = Intellectual Capital Literature depicts this definition of Intellectual Capital as one of the most widespread and very practical. Guthrie (2001) states that Intellectual Capital is the economic value of two categories of intangible assets of a company, i.e. organizational (structural) capital and human capital. Johnson (2002) emphasizes that this definition has since its creation, been further developed. For example Onge et al., (1997) have developed one model regarding Intellectual Capital management. Their model or “Value Platform” as it is also called, can best be described as a development of the model used by Skandia, but distinguishes Customer Capital from the previous two components (Dzinkowski, 2001). They further suggest that it is not enough to merely include the three components individually. Instead they have to be grouped so that they enhance each other. In other words the value does not arise from the separate components of Intellectual Capital, it takes place in the interplay or interaction between them and they are all equally important for this value creation (Edvinsson and Malone, 1997). The Skandia Market Value Scheme also comprehensibly illustrates the structure of the components of Intellectual Capital, as shown in figure 1 below. That is, Intellectual Capital is divided into Human Capital and Structural Capital. Structural capital is further divided into Customer Capital and Organizational Capital and Organizational Capital in turn, is divided into Innovation Capital and Process Capital, etc.

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Source: Johnson (2002) Figure 1: The Skandia Market Value Scheme

4. Research methodology Following quantitative research method, the sampling plan was purposeful (Lincoln and Guba, 1985; Spiggle, 1994). Seventeen Companies listed in the Zimbabwe Stock Exchange were selected from the services, manufacturing, agriculture, and mining and information technology sectors of the economy. The research sampling selection began with the group of firms in the manufacturing sector because they consist of the largest number of listed companies, followed by agriculture, mining and then information technology. Of the 12 manufacturing firms that met the purposeful criterion, 7 were selected for the purposes of this study. All the 5 agric‐based firms were selected, 3 from the mining sector and 2 from the information technology sector. A total of 53 top managers comprising Chief Executive Officers or Managing Directors, Chief Financial Officers, Chief Operations Officers and Human Resources Officers participated in this study as employees. The study sought to investigate if there is a relationship between the value of intellectual capital and market value of the firm, and also between the Intellectual Capital Multiplier (IC Multiplier) and the value of intellectual capital. To achieve these measurements the researcher applied the Value Added Approach over a period of five years. That is, Value added per employee as a measure of the return of intellectual capital and the stock exchange value (share price) as a measure of company`s market value and hence shareholder value.

5. Analysis of data The researcher used the spreadsheet program MS Excel to calculate the figures into statistical observations. In order to find relationship between value added per employee (intellectual capital measure) and stock exchange value (Share price/shareholder value) regression analysis was performed as the main statistical method for presenting and analyzing the figures. All regressions were analyzed to see whether there was any statistical significance. In order to test the propositions the researcher`s first step was to find adequate measurements for quantifying company’s Intellectual Capital and market value. The researcher chose Value added and Stock exchange value, both divided by the number of employees to be able to compare companies of different sizes. Share price or Stock exchange value per employee (SEV/e) is rather self‐explanatory for measuring market value of a company. However Value added per employee (VA/e), as an approximate measurement of a company’s return of intellectual capital, might nevertheless need some additional explaining. According to Sveiby (1990) Value added is the additional value, which is created within the company by using value added per employee, one can get an impression of how much individual employees contribute to this value (Sveiby, 1995). For the purposes of this study the researcher used the most established definition, which is operating profit before depreciation and personnel costs divided by number of employees (Konsultguiden, 2001).

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Bongani Ngwenya Table 1 below presents some statistical descriptions for the entire observed secondary information, i.e. all selected companies and years. The mean value for the Intellectual Capital measure (VA/e) is 1 371 while the Stock exchange value per employee (SEV/e) is more than four times higher at 5 282. The median suggests the following observations that can be made regarding the difference between VA/e and SEV/e. The difference is less than three times greater for SEV/e. Notable is further that the difference between the mean and median value has been halved when observing VA/e. This indicates that the Mean value could be somewhat misleading, at least regarding VA/e. Table1: Statistical descriptions for the entire observed data

VA/e

SEV/e

Mean Median

1371 719

5282 1936

Standard deviation

1836

8209

Largest

8567

60743

Smallest

‐449

236

The standard deviation indicates that the spread around the mean of SEV/e is greater than that of VA/e, i.e. 8 209 and 1836 respectively. It is clear indication that the stock exchange value is more volatile than the value added, which may not be a surprise to most people. This volatility is also clearly seen when observing the largest and smallest observation for VA/e and SEV/e. The largest value of VA/e is 8 567, while that of SEV/e is as high as 60 643, a difference of more than seven times, however in contrast to the smallest values of VA/e and SEV/e which are, ‐449 and 236 respectively. The following statistics summarizes the regression analysis and indicates that there is a correlation between VA/e and SEV/e on the entire observed data; on decision scope (Summary Model); a). Predictors: (Constant), data collection b).p‐value=3.73E‐39 c). Significant level= 95% d). Beta Value of 0.79 (Coefficients) e). Adjusted R, squared=0.62 Industry sectors The researcher sought to illustrate in more detail the observations discussed above by dividing the data into separate industry sectors. From table 2, below the sector with the highest values in mean is Agriculture with regard to both VA/e and SEV/e, at 3 129 and 11 508. In contrast the sector with the lowest mean values is Information Communication and Technology, with VA/e at 526 and SEV/e at 1 315 respectively. These trends also include the median values, for each measure. Table 2: Statistical description, by industry sector

Services

Manufacturing

Agriculture

Mining

I C Technology

Mean Median Standard deviation Largest Smallest

VA/e SEV/e 625 1414 615 1401 247 831 1189 3942 332 337

VA/e SEV/e 806 4360 756 2074 365 6103 1427 29775 356 433

VA/e SEV/e 3129 11508 2579 10684 2691 12299 8567 60743 458 236

VA/e SEV/e 711 3298 719 2310 303 2993 1010 17084 ‐449 964

VA/e SEV/e 526 1315 515 1301 147 731 1089 3842 232 237

The Standard deviation for SEV/e, as observed in the table above is highest for the Agricultural, more than double compared to the next sector in line; Manufacturing. The IC Technology sector demonstrates the smallest standard deviation. Regarding the standard deviation of VA/e, the three sectors Services, Manufacturing and Mining are all approximately centered on 300. However, Agriculture once again shows indicators of diversification compared with the other sectors since its standard deviation prove to be 2 691. The researcher believes the main reason for this large standard deviation concerning VA/e is caused by the sector being the main back bone of the Zimbabwean economy. Agriculture on the one hand, contains

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Bongani Ngwenya companies characterized of few top managers and large amounts of financial capital. These factors naturally influence, to a large extent, the values of VA/e and SEV/e since they are calculated per employee. An interesting observation, concerning the smallest values observed, is that the Mining sector holds the smallest lowest VA/e of the four sectors, at –449. In contrast, this sector also holds the highest SEV/e at 964, in the smallest category. The same discussion above, concerning VA/e and the average amount of total salary per employee (salary/e) could be applied here as well. The average salary cost per employee compared to VA/e can be seen in table 3 below. As can be seen in the table, the Agricultural sector demonstrate the largest difference of 2 626. The following statistics summarizes the regression analysis and indicates that there is a medium strong correlation between VA/e and SEV/e on an industry sector basis; on decision scope (Summary Model); a). Predictors: (Constant), data collection b).p‐value=0.083 c). Significant level= 95% d). Beta Value of 0.25 (Coefficients) e). Adjusted R, squared=0.06 Table 3: The salary per employee share out of value added per employee VA/e Salary/e Difference

Services 625 464 161

Manufacturing 806 550 256

Agriculture 3129 503 2626

Mining 711 637 74

IC Technology 525 364 161

The reason for the great difference of Agriculture ought to be caused by the small number of top managers in the Agricultural sector. In contrast, the four other sectors demonstrate very low values of VA/e. The Mining sector for instance proves to have the smallest difference between VA/e and Salary/e, with a value of only 74. This is alarmingly low considering all the other “stakeholders” that are to take part of the value added after salary costs. However, as these companies’ value and business operations consist primarily of their employees, this figure could be adequate. The following statistics summarizes the regression analysis and indicates that there is a medium strong correlation between VA/e and Salary/e on an industry sector basis; on decision scope (Summary Model); a). Predictors: (Constant), data collection b).p‐value=0.081 c). Significant level= 95% d). Beta Value of 0.23 (Coefficients) e). Adjusted R, squared=0.05

6. Company size In table 4 below the same descriptions of the statistical data, as in the two previous tables above, can be found regarding company size. The Large companies have the lowest mean values, while the Small companies demonstrate the highest mean values and the Medium sized companies in between the other two, both regarding VA/e and SEV/e. The same trends can be found regarding the median values. The Small companies have the largest standard deviation. The other two size segments have both much lower values, especially the larger companies. This difference is also revealed when comparing the largest and smallest values. The reason for this deviation is because the Small companies contain mainly a mix of Agricultural companies and Mining companies. These two sectors are extremes concerning both VA/e and SEV/e, i.e. mining have low VA/e and SEV/e and the Agricultural companies have very high values regarding VA/e as well as SEV/e. Table 4: Statistical descriptions by company size

Large

Medium Sized

Small

Mean

VA/e SEV/e 660 1773

VA/e SEV/e 1406 5144

VA/e SEV/e 2111 9208

Median

609 1370

753 2343

880 3958

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Bongani Ngwenya

Large

Medium Sized

Small

Standard Deviation Large Small

316 2179 1427 10984 332 337

1852 6929 7784 31536 ‐449 236

2375 11170 8567 60743 356 433

The same trends as was mentioned above can be found with reference to solely the largest values when comparing VA/e and SEV/e. However, the smallest values of VA/e and SEV/e show a different picture. The values of VA/e and SEV/e are relatively correlated regarding large companies. The biggest difference is found within the Medium sized companies, in which the smallest value of VA/e is ‐449 and 236 for SEV/e. The following statistics summarizes the regression analysis and indicates that there is even a much significantly stronger correlation between VA/e and SEV/e on company size basis, on decision scope (Summary Model); a). Predictors: (Constant), data collection b).p‐value=1.63E‐13 c). Significant level= 95% d). Beta Value of 0.81 (Coefficients) e). Adjusted R, squared=0.65 Company age In table 5 below the same descriptions of the data, divided by company age can be viewed. The Old companies prove to have the lowest mean values, regarding both VA/e and SEV/e. The low values originate from the fact that the majority of these companies mainly consist of Services. The Young companies have the largest mean value for SEV/e at 5 523, though the Medium aged companies are not far off at 5 371. Concerning the mean Value of VA/e, the medium aged companies’ holds the largest value. Regarding the Median values the Old companies as before holds the lowest values of VA/e and SEV/e. The Medium aged and Young companies median values, as for the mean values, are very much alike, i.e. 753 and 759 for VA/e and 2 202 and 2 170 for SEV/e. This cannot be explained by the composition of companies in these two age segments, instead this similarity could actually be caused by a coincidence. The Medium aged companies consist of all the five sectors while the Young companies consist mainly of Mining. Table 5: Statistical description by, company age Mean Median Standard Deviation Large Small

Old VA/e SEV/e 1057 4887 687 1499 1584 1560 8567 60743 412 337

Medium Aged VA/e SEV/e 1748 5371 753 2202 2130 6955 8119 31536 332 236

Young VA/e SEV/e 1231 5523 759 2170 1479 7151 6199 29775 ‐449 433

Interesting to observation is the standard deviation for the Old companies. The standard deviation for VA/e proves to be higher than the SEV/e, something that has not occurred previously in the descriptions. The values for VA/e and SEV/e are also very similar at 1 584 and 1 560. The standard deviation for the two other age segments are fairly similar, though the Young companies have the largest values of SEV/e and the Medium aged companies have the largest values regarding VA/e. The Old companies have the largest values, both concerning VA/e and SEV/e at 8 576 and 60 743 respectively, which is interesting considering that these companies had the lowest standard deviation. The following statistics summarizes the regression analysis and indicates the highest significantly stronger correlation between VA/e and SEV/e on company age basis; on decision scope (Summary Model); a). Predictors: (Constant), data collection b).p‐value=2.14E‐30 c). Significant level= 95% d). Beta Value of 0.96 (Coefficients) e). Adjusted R, squared=0.93

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Bongani Ngwenya IC Multiplier For the purposes of this study the Intellectual Capital Multiplier is calculated by dividing Structural Capital (SC) with Human Capital (HC).To calculate the SC/HC‐ratio the researcher examined each company thoroughly in order to come up with an approximation of the IC Multiplier. This approximation is calculated by stating that a company’s HC‐ratio equals its salaries divided by its VA. Since the SC‐ratio equals 1‐HC this approximation gives the researcher SC, HC and consequently also the IC Multiplier. Using the approximation in calculating the HC‐ and SC‐ratios shows that the HC, in average, accounts for 63 percent of the total Intellectual Capital value. This HC‐value must be seen as surprisingly high. As implied by the term IC Multiplier the SC‐ratio should be at least equal to the HC‐ratio. Otherwise erosion rather than a multiplication of the HC is occurring here. The argument is with the diverse aspects of the five industries on this study, one might expect the SC/HC‐ratio to differ between these industries (Edvinsson, 2002). As presented in table 6 below, the difference between the industry sectors also proves to be evident, although the outcome does not completely correlate with the researchers thoughts. Table 6: Average proportions by industry sector Structural Capital Human Capital IC Multiplier

Services 0,31 0,69 0,54

Manufacturing 0,63 0,37 6,05

Agriculture 0,30 0,70 0,63

Mining 0,20 0,80 0,16

IC Technology 0,32 0,68 0,53

The mining sector, with focus on man‐hours, does in fact show the highest HC‐ratio, with HC about four times as large as SC ratio. What this implies is that most of company value lies with employees. When the employees go home at night, so does most of the company value. The process of transforming HC into SC is therefore of utter importance for these companies. An average IC Multiplier value of 0, 16 further strengthens the previous assertion, as there in this sector lays a major erosion in the value created by the HC. A larger amount of SC would probably also stabilize these companies’ stocks, thus halting the volatility that historically has characterized the Mining stocks. Agricultural sector, with the second highest HC‐ratio, does also correspond with the researcher`s expectations. With focus on research and development, a lot of value obviously lies in the tacit knowledge of the employees. But with an average IC Multiplier of below 1, this tacit knowledge is still not employed to its full potential. The following statistics summarizes the regression analysis and indicates that there is significantly strong correlation between VA/e and IC Multiplier on industry sector basis; on decision scope (Summary Model); a). Predictors: (Constant), data collection b).p‐value=3.09E‐73 c). Significant level= 95% d). Beta Value of 0.92 (Coefficients) e). Adjusted R, squared=0.84 When dividing the companies by size, presented in table 7 below, the average SC and the average HC are quite similar for all three segments. They do also, logically, resemble the ratios for all companies, with HC about twice the size of SC. However, more interesting is the difference in IC Multiplier, although the most probable explanation for this change in value is the low amount of employees in the Manufacturing sector portion of the small companies. The following statistics summarizes the regression analysis and indicates that there is significantly strong correlation between VA/e and IC Multiplier on company size basis; on decision scope (Summary Model); a). Predictors: (Constant), data collection b).p‐value=2.27E‐23 c). Significant level= 95%

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Bongani Ngwenya d). Beta Value of 0.89 (Coefficients) e). Adjusted R, squared=0.80 Table 7: Average proportions by company size Structural Capital Human Capital IC Multiplier

Large 0,35 0,67 0,65

Medium sized 0,39 0,63 1,87

Small 0,39 0,63 3,11

Considering the companies divided by age, in table 8 below, the results are rather similar to those in the table above. All three segments show HC and SC ratios in the vicinity of the ones for all companies, although there is a difference between the Young and Medium aged companies. Notable is also that all three segments have IC Multipliers above 1. The following statistics summarizes the regression analysis and indicates that there is significantly strong correlation between VA/e and IC Multiplier on company age basis; on decision scope (Summary Model); a). Predictors: (Constant), data collection b).p‐value=5.32E‐39 c). Significant level= 95% d). Beta Value of 0.98 (Coefficients) e). Adjusted R, squared=0.97 Table 8: Average proportions by company age Structural Capital Human Capital IC Multiplier

Old 0,40 0,62 1,34

Medium aged 0,43 0,59 2,81

Young 0,32 0,70 1,26

7. Summary and conclusion The purpose of this study has been to examine whether there is a correlation between the intellectual capital and the market value of selected listed companies on the Zimbabwe Stock Exchange and further examine IC Multiplier as a possible indicator for leveraging Intellectual Capital. The researcher considers this purpose to be fulfilled since propositions defined for the purposes of this study have been statistically tested. Proposition 1: There is a significant relationship between companies’ value added per employee and their share price value per employee when comparing all the selected companies. The first proposition proves that there, in general, is a correlation between companies’ value added and their stock exchange value. The correlation is also significantly strong. What this strong correlation further implies is that most company’s perceived value, in today’s knowledge economy, is related to the company’s intellectual capital. Proposition 2: There is a significant relationship between companies’ value added per employee and their share price value per employee when comparing companies by industry sector. The second hypothesis proves that there is a medium strong correlation when comparing companies by industry sector mainly because of the variations in companies` value added per employee and their share price per employee, industry sector by industry sector. The sectors also differ from each other because the focus is on man‐hours. In other words the value added can therefore be traced almost solely to the employees. Proposition 3: There is a significant relationship between companies’ value added per employee and their share price value per employee when comparing companies divided by company size. Testing the third proposition, this study proves that there in fact is a significant relationship between the intellectual capital and the market value when dividing the companies into size segments. The findings,

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Bongani Ngwenya however, also prove that there is no identifiable size pattern, i.e. the correlation is not as strong within the larger companies, compared to the correlation of the medium or small companies. Proposition 4: There is a significant relationship between companies’ value added per employee and their share price value per employee when comparing companies divided by company age. The study further proves, regarding the fourth proposition, that there also is a clear relationship between the intellectual capital and the market value of the companies, when they are divided into segments according to age. What is most interesting about these results is that there in fact is a relationship which proves that the older a company gets, the clearer the correlation becomes between companies’ intellectual capital and their market value, i.e. the sought pattern becomes more legible over time. The researcher believes that this observed fact originates from that the investors/financial markets knowledge of the companies’ intellectual capital increases over time. Proposition 5: There is a significant relationship between companies’ IC Multiplier and their value added per employee. The fifth proposition has been proven to be correct in this study, i.e. there is, according to the approximations made, a correlation between the IC Multiplier and the intellectual capital. The findings further show strong correlations, especially concerning companies divided by size and age. However, the results also prove that most companies do not have enough structural capital to support the individuals, i.e. the human capital. The conclusion can therefore be drawn that Zimbabwean companies in general cannot exploit the value of its employees’ brains to their full potential. In fact the low amount of structural capital that exists within companies constrains the employees, i.e. the employees’ knowledge is being hollowed out. The study also indicates that there seem to be a relationship between the IC Multiplier and market value. The reason for this is that the IC Multiplier affects the intellectual capital, which in turn affects the market value. The leverage effect regarding these both relationships means that a small improvement of the SC/HC‐ratio dramatically can affect the market value. In conclusion it would be noted that the findings of this study demonstrate that some important company performance indicators are much better in companies with well performing human and structural capitals, such as competitiveness, customer service, quality, flexibility, innovation and processes. These have also contributed to higher share price value per employees in these companies. These human and structural capitals have accrued to companies in well developed industries, companies that are fairly big and have been in the industry for a long time. In other words they have enjoyed economies of scale from the industry sector, company size and company age.

8. Recommendations for further study The researcher is suggesting some practical recommendations to practitioners/business people and future researchers interested in further exploring the field. Mainly or alternatively, to compare the results of this study and conduct a similar one, using the same industry sectors and number of companies, on another stock exchange, e.g. South African/Johannesburg stock exchange. The similarities and deviations between two different stock exchanges would, according to the researcher, be of great interest.

References Barney, J.B 1991, “Firm resources and sustained competitive advantage”, Journal of Management, 17, 99‐120. Bontis, N 1996, “There`s a price on your head: managing intellectual capital strategically”, Business Quarterly, Summer, pp. 40‐47. Bontis, N 1998, “Intellectual capital: an exploratory study that develops measures and models” Management Decision, 36(2), 63‐76. Bontis, N 1999, "Managing Organizational Knowledge by Diagnosing Intellectual Capital: Framing and advancing the state of the field", International Journal of Technology Management, 18(5/6/7/8), 433‐462. Collis, D.J., Montgomery, C.A 1995, “Competing on resources: strategy in the 1990s”, Harvard Business Review, pp. 118‐ 128. Dzinkowski, R 2000, “The measurement and management of intellectual capital: an introduction”, Management Accounting: Magazine for Chartered Management Accountants, 78(2), 32‐36. Edvinsson, L 1997, “Developing IC at Skandia”, Long Range Planning, 30(3), 320‐321‐366‐373. Edvinsson, L 2002, “Corporate Longitude: navigating the knowledge economy”, Book house.

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Bongani Ngwenya Edvinsson, L., Malone, M. S, 1997, “Intellectual Capital: realizing your company’s true value by finding its hidden brainpower”, Harper Business. Gu, F., and Lev, B 2001, “Intangible Assets–Measurement, drivers, usefulness”, http://www.stern.nyu.edu/~blev/intangible‐assets.doc, download April 10 2002. Guthrie, J 2001, “The management, measurement and the reporting of intellectual capital, Journal of Intellectual Capital, 2(1), 27‐41. Johnson, M 2002, “Intellectual Capital`s Leverage on Market Value”, Book House. Lev, B 2000, “Knowledge and shareholder value”, http://www.stern.nyu.edu/~blev/knowledge&shareholdervalue.doc, download May 15 2002. Pike, S., Rylander, A., and Roos, G 2001, “Intellectual capital management disclosure”, In Choo, C.W., and Bontis, N (Ed), The Strategic Management of Intellectual Capital and Organizational Knowledge: A Selection of Readings, Oxford University Press, New York, NY. Pulic, A 2000, “VAIC–An Accounting Tool for IC Management”, http://www.measuring‐ip.at/Papers/ham99txt.htm, download April 15th 2002. Rylander, A., Jackobsen, K., and Roos, G 2000, “Towards improved information disclosure on intellectual capital”, International Journal of Technology Management, 20(5/6/7/8), 715‐741. Sveiby, K 1997, “The new organizational wealth: managing & measuring knowledge‐based assets”, Berrett‐Koehler. Sveiby, K., Arbetsgruppen, K 1990, “Den osynliga balansräkningen”, Affärsvärlden förlag AB. www.intellectualcapital.org 2000, Curry, A., and Cavendish, S (editors), download April 14th 2002 http://www.intellectualcapital.org/evolution/index.html.

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