HRM in a Knowledge Based Economy 9781846634970, 9781846634963

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04/07/2007

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ISSN 0143-7720

Volume 28 Number 3/4 2007

International Journal of Manpower An interdisciplinary journal on human resources, management & labour economics

HRM in a knowledge-based economy Guest Editors: Ivan Svetlik and Eleni Stavrou-Costea

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International Journal of Manpower

ISSN 0143-7720 Volume 28 Number 3/4 2007

HRM in a knowledge-based economy Guest Editors Ivan Svetlik and Eleni Stavrou-Costea

Access this journal online _______________________________ 195 Editorial advisory board _________________________________ 196 INTRODUCTION Connecting human resources management and knowledge management Ivan Svetlik and Eleni Stavrou-Costea _______________________________

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Knowledge management and innovation performance ˚ ke Lundvall and Peter Nielsen_______________________________ Bengt-A

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Measuring organisational learning capability among the workforce Ricardo Chiva, Joaquin Alegre and Rafael Lapiedra____________________

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The role of HR actors in knowledge networks Nada Zupan and Robert Kasˇe _____________________________________

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Competency management in support of organisational change Maria Vakola, Klas Eric Soderquist and Gregory P. Prastacos ___________

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CONTENTS

CONTENTS continued

The effects of joint reward system in new product development Tsun Jin Chang, Shang Pao Yeh and I-Jan Yeh_______________________

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E-business through knowledge management in Spanish telecommunications companies Juan G. Cegarra-Navarro and Eusebio Angel Martı´nez-Conesa ___________

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Knowledge sharing and firm innovation capability: an empirical study Hsiu-Fen Lin ___________________________________________________

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EDITORIAL ADVISORY BOARD Professor David J. Bartholomew London School of Economics, UK

Professor Thomas Lange Auckland University of Technology, New Zealand

Professor Derek Bosworth Manchester Business School, University of Manchester, UK

Professor Lord Richard Layard Centre for Economic Performance, London School of Economics, UK

Professor Martin Carnoy School of Education, Stanford University, USA

Professor John Mangan University of Queensland, Brisbane, Australia

Professor Peter Dawkins Melbourne Institute for Applied Economic and Social Research, Melbourne University, Australia

Professor Stephen L. Mangum Ohio State University, Ohio, USA

Professor Morley Gunderson University of Toronto, Canada Professor Thomas J. Hyclak Lehigh University, Bethlehem, USA Professor Susan E. Jackson Rutgers University, New Jersey, USA Professor Harish C. Jain McMaster University, Canada Professor Geraint Johnes Lancaster University Management School, Lancaster University, UK Professor Meni Koslowsky Department of Psychology, Bar-Ilan University, Israel

International Journal of Manpower Vol. 28 No. 3/4, 2007 p. 196 # Emerald Group Publishing Limited 0143-7720

Professor David Sapsford Chairman Economics Division, University of Liverpool, UK Professor P.J. Sloane Department of Economics, University of Wales, Swansea Professor Zhong-Ming Wang School of Management, Zhejiang University, China Professor Klaus F. Zimmerman Department of Economics, University of Bonn, Germany

The current issue and full text archive of this journal is available at www.emeraldinsight.com/0143-7720.htm

INTRODUCTION

Connecting human resources management and knowledge management

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Ivan Svetlik University of Ljubljana, Ljubljana, Slovenia, and

Eleni Stavrou-Costea University of Cyprus, Nicosia, Cyprus Abstract Purpose – The article seeks to demonstrate the benefits of using an integrative approach between human resource management (HRM) and knowledge management (KM), where one reinforces and supports the other in enhancing organisational effectiveness and performance. Design/methodology/approach – This contribution is a collection of research articles that explore how HRM and KM are interrelated and provide empirical support for such connection. Findings – The authors firmly believe that the articles of this issue will not only provide for interesting and worthwhile reading material, but also set the stage for enlarging and enriching the research base on the relationship between HRM and KM. Research limitations/implications – It is not an exhaustive analysis of the connections between HRM and KM; however, it is a very good first step in that direction. Even though HRM and KM have much in common, there are few studies that make such a connection explicit. Practical implications – The article provides a very useful source of information and practical advice on how the connection between the two disciplines can enhance organisational functioning. Originality/value – This special issue fulfils a gap in the existing literature for both academics and practitioners on the merits of using HRM and KM integratively. Keywords Human resource management, Knowledge management Paper type Literature review

Introduction In this issue of the International Journal of Manpower we try to demonstrate the interface between human resource management (HRM) and knowledge management (KM) and the benefits of using an integrative approach between the two disciplines having the employee at the centre. While HRM, KM, and similar disciplines, such as management of intellectual capital and information management, address the issues of increasing the role of knowledge in contemporary organisations and the economy from different angles, it is felt that combining these angles into an integrative approach could be more fruitful. This belief has been recently put forward and empirically verified by various authors. To illustrate, Scarbrough (2003) found that the innovation process could be facilitated if HRM and KM are linked within organisations. Furthermore, Scholl et al. (2004) explain that the most effective approach to the theoretical and empirical issues of KM would be an interdisciplinary and a multi-disciplinary one. According to their

International Journal of Manpower Vol. 28 No. 3/4, 2007 pp. 197-206 q Emerald Group Publishing Limited 0143-7720 DOI 10.1108/01437720710755209

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research, the most pressing and challenging practical problem for the understanding and advancement of KM is to give priority to human factors. In a similar fashion, Oltra (2005) criticises academics for not taking rigorous and systematic steps toward comprehensive theory building in linking KM and HRM. Finally, Yahya and Goh (2002) argue that: The focus of KM should rightly be placed on humans themselves, and the impact made by human resource management on KM practices . . . and that KM is actually an evolved form of human resource management . . .

To address the aforementioned arguments, we have organised an international conference held in Ljubljana in June 2004 and titled HRM in the knowledge-based economy. The main idea of the conference was to explore the question on how HRM does, could and should contribute to knowledge-based organisations and the economy. The implicit assumption was that HRM and KM should come closer together. We used three articles from that conference, for this special issue. In addition, we recruited four additional ones through an open call in order to provide a wider array of studies to this link between HRM and KM. These articles are primarily empirical, each focusing on a different aspect of HRM and KM. Their conceptualisations, methods and findings demonstrate the importance of an interdisciplinary approach. Therefore, before providing an overview of each paper, we will first put forward some considerations regarding each as well as the interfaces between the two managerial disciplines, HRM and KM. Human resource management Strait forward definitions of human resource management are difficult to find. A typical handbook usually defines HRM as the management of the organisation’s employees (Scarpello and Ledvinka, 1988, p. 4). Armstrong (2000) defines HRM as strategic personnel management emphasising the acquisition, organisation and motivation of human resources. Beardwell and Holden (2001, pp. 9-16) hold that understanding HRM depends highly on the perspective taken: HRM could be conceived as traditional personnel management, as a fusion of personnel management and industrial relations, as a resource-based employment relationship or as a part of strategic managerial function. With respect to this, HRM involves managing employees, their interpersonal relations and relations with the organisation. Perhaps the most crucial point about HRM is that people and their interpersonal relations become and are treated as resources, something that could be considered both good and bad: the negative side is that recourses are often treated as expendable; we promote the positive side, that recourses are valuable and necessary for an organisation to become exceptional. In line with the resource-based view (Penrose, 1959) employees with all their capacities become desirable and real resources for the organisation if they are to a high degree: valuable and scarce, inimitable, non-substitutable and appropriable (Boxall and Purcell, 2003, p. 75). Boxall and Purcell continue that: Firms have the possibility of generating human capital advantage through recruiting and retaining outstanding people: through “capturing” a stock of exceptional human talent, latent with powerful forms of “tacit” knowledge. Organisational process advantage, on the other hand, may be understood as a function of historically evolved, socially complex, causally

ambiguous processes, such as team-based learning and cross-functional cooperation – processes which are very difficult to imitate . . . In a nutshell, “human resource advantage” . . . can be traced to better people in organisations with better process (Boxall and Purcell, 2003, pp. 85-86).

There are two points to remember: first, HRM does not manage people as such, but their personal and interpersonal (inter-group, organisational) characteristics, which could be considered resources and create organisational advantages; and second, human resources are not only brought into the organisation by means of recruitment and selection but also developed within the organisation by investment in their personal capacities and deployed by nurturing of interpersonal and inter-group relations. Another important point for our discussion is how human resources are composed; what is their structure and how it is changing? According to O’Donnell et al. (2003), people are evaluated through their competencies, knowledge, know-how, adaptability, network connections and experiences. Among these components, knowledge has become most accentuated: according to Drucker (1999), the basic economic resource is no longer capital, natural resources or labour, but knowledge. What really distinguishes work results from each other is the share of embedded knowledge (Burton, 1999, p. 4). In their study of the Irish ICT sector O’Donnell et al. (2003) found that approximately two thirds of organisational value is perceived to be composed of intellectual capital and that over half of this capital stems directly from people working, thinking and communicating. Knowledge management Unlike human resource management, which is seldom explicitly defined, a bundle of definitions of knowledge exist. However, like human resource management, definitions of knowledge and how to manage it, are usually incomplete because they deal with a rather slippery subject (Winter, 1987). Furthermore, no universally accepted foundation for knowledge has yet been developed (Barabas, 1990, p. 61). Perhaps the most profound distinction in the study of knowledge has been made between knowledge as a subjective state in individuals’ minds embedded in organisations and communities – constructivist approach (Davenport and Prusak, 1998, p. 5; Lang, 2001), and knowledge as an objective state of things – objectivist approach (Spender, 1998). This distinction coincides to some extent with that made between tacit and explicit knowledge (Polanyi, 1966; Nonaka, 2002), soft and hard knowledge (Hildreth et al., 1999), background and foreground knowledge (Bhatt, 2001). The proponents of the second view would argue that knowledge management is a conscious strategy of getting the right knowledge to the right people at the right time and helping people share and put information into action in ways that strive to improve organisational performance (O’Dell and Jackson, 1998, p. 4). Knowledge is a commodity to be traded (Gibbons et al., 2000) and needs to be managed (Dodgson, 2000, p. 37). The proponents of the first view rely on the difference between information and knowledge. According to Bhatt (2001) knowledge is different from data and information. Data are raw facts and when organised they become information. Knowledge is meaningful information. They claim that “the most important parts of knowledge cannot be handled as a thing for others” (Scholl et al., 2004). Rooney and

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Schneider (2005) explain that knowledge is bound to human consciousness while data, texts and images are contained in storage media. In a similar fashion, Kakabadse et al. (2003) argue that: KM is not about managing knowledge but about changing entire business cultures and strategies of organisations to ones that value learning and sharing. Although some aspects of knowledge, such as culture, organisational structure, communication process and information can be managed, knowledge itself, arguably, cannot . . . Hence, one can manage or support processes of learning rather than managing knowledge.

Finally, Rooney and Schneider (2005, p. 33) are explicit that “because knowledge is sensitive to context and is fallibly enacted, it cannot be managed”. The constructivist approach accepts not only individual knowledge but also for knowledge that exists in the social context of groups, organisations and societies (Yahya and Goh, 2002). While knowledge is created by and rests in individual employees, it is also created through social interaction and is embedded in the social structure of organisational members (Narasimha, 2000). According to Davenport and Prusak (1998) knowledge in organisations often becomes embedded not only in documents and repositories, but also in organisational routines, processes, practices and norms. As Malhotra (1998) states KM “embodies organisational processes that seek synergistic combination of data and information processing capacity of information technologies, and the creative and innovative capacity of human beings”. This means that the distinction made between knowledge as a thing and knowledge as a state of mind cannot be conceived in terms of “either or”. In our view they complement each other. Objective knowledge encoded in written, electronic and other forms has helped enormously in functioning of the existing educational systems, which strive for the transfer of knowledge to the new generations. A well-structured textbook keeps its value even in a modern study process. The same stands for the production systems, which use written plans, designs, manuals etc. However, to make encoded knowledge available to individuals and organisations and to create additional knowledge on this basis, human touch is unavoidable. They must read, listen and speak in order to reach a new level of comprehension. Only this way a new piece of knowledge could become encoded. If knowledge does not exist and cannot be observed and managed in its pure form, the concept of knowledge embeddedness deserves special attention. According to Blacker (2002, pp. 48-50) knowledge could be embedded in several ways: embrained in terms of conceptual skills and cognitive abilities; embodied in terms of being action oriented, situational and only partially explicit, linked to individuals’ senses and physical abilities; encultured in terms of shared understandings achieved in the process of socialisation and acculturation; embedded in systemic routines that include relationships between technologies, roles, formal procedures and emergent routines; and encoded in terms of information conveyed by signs and symbols in books, manuals, codes of practice and electronic media. Ingrained into the process of KM is the so-called knowledge cycle. This cycle integrates knowledge through four main phases, which should be observed interactively rather than by a linear approach (OECD, 2001): the first is knowledge acquisition, which focuses primarily on searching among various sources of information and knowledge, on their selection, and on ways to bring the existing knowledge in the possession of individuals and organisations; the second involves

knowledge creation, which focuses on the development and increasing bulk of new knowledge; the third is knowledge transfer, distribution, dissemination and sharing, aiming for relevant knowledge to reach relevant individuals, groups and organisations as soon as possible; and the fourth entails knowledge utilisation and application in various environments, which is the ultimate goal of the economic organisations and systems as well as individuals who work for them.

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201 Where HRM and KM meet If we compare the enumerated characteristics of HRM and KM as described above, the following observations could be made: If HRM is about managing people effectively and if people’s most valuable resource is knowledge, then HRM and KM come closely interrelated. Even more, HRM and KM share common activities and goals when creating work units, teams, cross-functional cooperation, as well as communication flows and networks inside the organisation and across its borders. If we compare the KM cycle with HRM processes, we will find the various activities shared between KM and HRM. Knowledge acquisition is about recruiting outstanding people and about helping them learn and grow as individuals and as professionals. It is also about encouraging employees to participate in professional networks and communities of practice that extend beyond organisational boundaries (Wenger et al., 2002). Knowledge creation is achieved by creating a supportive environment, through requisite HRM, for individuals, groups and teams in order to be challenged by the organisational problems, to search for the problems’ solutions and to innovate. It goes from the creation of positions and teams, to the provision of information feedback flows, to the design of stimulating remuneration and other systems of encouragement. It includes also investment in the training and development of human resources. Knowledge transfer concerns various forms of learning, the creation of a knowledge sharing climate, establishment of training units which assess and analyse training needs, provide and evaluate training, and lead towards learning organisations (Senge, 1994). Finally, knowledge utilisation is about the deployment of human resources by means of proper leadership, division of tasks and responsibilities, remuneration systems, and performance appraisal. It would be difficult to find an area where HRM and KM do not meet. Perhaps one such area could be management of the encoded knowledge, although one could argue that this is not a KM but an information management issue. It seems, however, that encoding knowledge and putting it in an explicit form could go beyond sheer information management. Furthermore, codification is usually associated with the process of abstraction, which should provide for effective diffusion (Boisot, 2005, pp. 178-190). Thus, managing knowledge and managing human resources, even though not interchangeable concepts, they are certainly highly inter-related. Teece (2000) takes this argument a step further, suggesting that KM is more multifaceted than HRM because it involves managing intellectual property rights and the development and transfer of individual and organisational know-how. Nevertheless, knowledge cannot be managed in a void – without people – and the other way around. Therefore, the two disciplines are not only inter-related but also highly interdependent By this comparison we propose an integrative approach between KM and HRM, one that will advance knowledge in both fields as well as improve organisational effectiveness. If HRM neglects the requisite management of knowledge and does not

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adjust its concepts and practices to the multi-faceted nature of knowledge, it puts itself on a side-track. The same stands for KM if it does not focus on the requisite management of individuals, their interpersonal relations and their relations with their respective organisations. To put it affirmatively: The focus of KM should rightly be placed on humans themselves, and the impact made by human resource management on KM practices . . . The main tasks of HRM are to monitor, measure and intervene in construction, embodiment, dissemination and use of knowledge of employees (Yahya and Goh, 2002).

Shih and Chiang (2005) have already attempted to provide empirical support for the connection among HRM, KM and corporate strategies and we seek to enrich such support with similar studies through this special issue. About the articles Given the aforementioned discussion and without further due, we introduce below the various articles in this special issue (International Journal of Manpower, Vol. 28 No. 3/4, 2007) that demonstrate the merits of integrating KM and HRM. The first article, by B.A. Lundvall and P. Nielsen, deals with the establishment of “learning organisations” as a central element of knowledge management – especially among firms operating in markets where product innovation is an important parameter of competition. The authors argue that the wide use of information extends the potential for codifying knowledge but at the same time it makes tacit knowledge scarcer and it contributes to the formation of “a learning economy”. They support their argument with an empirical analysis demonstrating that firms that introduce several human resource management practices assumed to characterise the learning organisation are more innovative than the average firm. HRM contributes thus to knowledge creation. Following the above is an article on measuring organisational learning among employees, by R. Chiva, J. Alegre and R. Lapiedra. In this article, the authors describe the development and validation of a diagnostic tool which aims to capture the organisational propensity to learn, something which as they claim is missing from extant literature. They propose five dimensions that represent the essential factors that determine organisational learning capability: experimentation, risk taking, interaction with the external environment, dialogue, and participation in decisions. This tool may be related to a dynamic training approach applied to organisations or serve as a mechanism to facilitate learning, as the five dimensions may represent a useful target for organisational change initiatives. In the third article, N. Zupan and R. Kase examine the structural positions of line managers and HR specialists (called “HR actors”) within relational networks for creating and sharing knowledge; and explore the implications for designing and implementing HR practices in knowledge-intensive firms (KIF). This is a very interesting article as it demonstrates that line managers who are HR actors are centrally positioned within the knowledge networks examined, while HR specialists are not. These results imply that a decentralised approach to HRM in KIF can be effective. Furthermore, the study shows that HRM can affect the process of knowledge creation and sharing by implementing HR practices through centrally positioned line managers.

In line with the aforementioned articles, but shifting gears a bit towards organisational competitive advantage, the fifth article of this issue deals with the development of a proactive approach to competency modelling and its application to facilitate strategic change by supporting communication, understanding of business goals and the incorporation of new behaviours, roles and competencies within the organisation. M. Vakola, K.E. Soderquist and G.P. Prastacos base their study on the central role that competencies have in integrating the different human resource management activities into a requisite system and the real need to translate business strategy into the people competencies necessary to implement and support that strategy at the operational organisational levels. Through a case study, M. Vakola and her colleagues have demonstrated that their suggested approach was successful in anchoring the competencies in the new organisational strategy, ensuring focus on job-related skills, and allowing for significant flexibility while keeping areas and competencies generic. Adding to the richness of this special issue, in the next article T.J. Chang and S.P. Yeh explore how knowledge sharing among new product development members of high technology Taiwanese firms is positively related to team-based joint reward systems and organisational citizenship behaviours. They also investigate the mediating effects of perceived procedural justice to the relationship between joint reward systems and organisational citizenship behaviour, thus highlighting the importance of perceived procedural justice in rewarding for organisational citizenship behaviour and in turn exhibiting high new product development performance. Next, J.G Cegarra-Navarro and E.A. Martinez-Conesa propose a model that examines how knowledge management has an impact on the adoption of e-business, particularly in SMEs. They find that in order for e-business to be successful, companies need to provide and support the acquisition, sharing and application of knowledge. The authors also find that companies have to be careful not to over-invest in technologies and under-invest in mechanisms – such as HRM processes – to facilitate the flow of knowledge creation. Last but not least, H. Lin provides closure to this special issue through studying the influence of enjoyment in helping others, knowledge self-efficacy, top management support, organisational rewards, and the use of information and communication technology on knowledge-sharing processes and superior firm innovation capability. Overall, this study demonstrates that employee willingness to both donate and collect knowledge enable the firm to improve innovation capability; and provides a guideline on how firms can promote a knowledge-sharing culture to sustain their innovation performance. We firmly believe that the articles of this issue will not only provide for an interesting and a worthwhile reading material, but will also set the stage for enlarging and enriching the research base on the relationship between HRM and KM. References Armstrong, M. (2000), “The name has changed but has the game remained the same?”, Employee Relations, Vol. 22 No. 6, pp. 576-93. Barabas, C. (1990), Technical Writing in a Corporate Culture, Ablex, Norwood, NJ. Beardwell, I. and Holden, L. (2001), Human Resource Management: A Contemporary Approach, Pearson Education, London.

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Bhatt, G.D. (2001), “Knowledge management in organisations: examining the interaction between technologies, techniques and people”, Journal of Knowledge Management, Vol. 5 No. 1, pp. 68-75. Blacker, F. (2002), “Knowledge, knowledge work, and organisations”, in Choo, W.C. and Bontis, N. (Eds), The Strategic Management of Intellectual Capital and Organisational Knowledge, Oxford University Press, Oxford. Boisot, M. (2005), “Exploring the information space: a strategic perspective on information systems”, in Rooney, D., Hearn, G. and Ninan, A. (Eds), Handbook on the Knowledge Economy, Edward Elgar, Cheltenham. Boxall, P. and Purcell, J. (2003), Strategy and Human Resource Management, Palgrave Macmillan, Basingstoke and New York, NY. Burton, J.A. (1999), Knowledge Capitalism, Oxford University Press, Oxford. Davenport, T.H. and Prusak, L. (1998), Working Knowledge: How Organisations Manage What They Know, Harvard Business School Press, Boston, MA. Dodgson, M. (2000), The Management of Technological Innovation, Oxford University Press, Oxford. Drucker, P. (1999), “Knowledge worker productivity: the biggest challenge”, California Management Review, Vol. 41 No. 2, pp. 79-105. Gibbons, M., Limoges, C., Nowotny, H., Schwartzman, S., Scott, P. and Trow, M. (2000), The New Production of Knowledge, Sage, London. Hildreth, P., Wright, P. and Kimble, C. (1999), “Knowledge management: are we missing something?”, in Brooks, L. and Kimble, C. (Eds), Information Systems – The Next Generation, Proceedings of the 4th UKAIS Conference, York. Kakabadse, N.K., Kakabadse, A. and Kouzmin, A. (2003), “Reviewing the knowledge management literature: towards a taxonomy”, Journal of Knowledge Management, Vol. 7 No. 4, pp. 75-91. Lang, J.C. (2001), “Managerial concerns in knowledge management”, Journal of Knowledge Management, Vol. 5 No. 1, pp. 43-57. Malhotra, Y. (1998), “Deciphering the knowledge management hype”, Journal for Quality and Participation, Vol. 21 No. 4, pp. 58-60. Narasimha, S. (2000), “Organisational knowledge, human resource management, and sustained competitive advantage: toward a framework”, Competitiveness Review, Vol. 10 No. 1, pp. 123-35. Nonaka, I. (2002), “A dynamic theory of organisational knowledge creation”, in Choo, W.C. and Bontis, N. (Eds), The Strategic Management of Intellectual Capital and Organisational Knowledge, Oxford University Press, Oxford. O’Dell, C. and Jackson, C. (1998), If Only We Know What we Know: The Transfer of Internal Knowledge and Best Practice, Free Press, New York, NY. O’Donnell, D., O’Regan, P., Coates, B., Kennedy, T., Keary, B. and Berkery, G. (2003), “Human interaction: the critical source of intangible value”, Journal of Intellectual Capital, Vol. 4 No. 1, pp. 82-99. OECD (2001), Knowledge Management in the Learning Society, OECD, Paris. Oltra, V. (2005), “Knowledge management effectiveness factors: the role of HRM”, Journal of Knowledge Management, Vol. 9 No. 4, pp. 70-86. Penrose, E. (1959), The Theory of the Growth of the Firm, Blackwell, Oxford. Polanyi, M. (1966), The Tacit Dimension, Routledge & Kegan Paul, London.

Rooney, D. and Schneider, U. (2005), “The material, mental, historical and social character of knowledge”, in Rooney, D., Hearn, G. and Ninan, A. (Eds), Handbook on the Knowledge Economy, Edward Elgar, Cheltenham. Scarbrough, H. (2003), “Knowledge management, HRM and the innovation process”, International Journal of Manpower, Vol. 24 No. 5, pp. 501-16. Scarpello, G.V. and Ledvinka, J. (1988), Personnel/Human Resource Management, PWS-Kent Publishing Company, Boston, MA. Scholl, W., Koenig, C., Meyer, B. and Heisig, P. (2004), “The future of knowledge management: an international Delphi study”, Journal of Knowledge Management, Vol. 8 No. 2, pp. 19-35. Senge, P. (1994), The Fifth Discipline: The Art and Practice of the Learning Organization, Doubleday, New York, NY. Shih, H.A. and Chiang, Y.H. (2005), “Strategy alignment between HRM, KM, and corporate development”, International Journal of Manpower, Vol. 26 No. 6, pp. 582-603. Spender, J.C. (1998), “Pluralist epistemology and the knowledge based theory of the firm”, Organisation, Vol. 5 No. 2, pp. 233-56. Teece, D.J. (2000), “Strategies for managing knowledge assets: the role of firm structure and industrial context”, Long Range Planning, Vol. 33 No. 1, pp. 27-43. Wenger, E., McDermott, R.A. and Snyder, W. (2002), Cultivating Communities of Practice: A Guide to Managing Knowledge, Harvard Business School Press, Boston, MA. Winter, S. (1987), “Knowledge and competence as strategic assets”, in Teece, D. (Ed.), The Competitive Challenge: Strategy for Industrial Innovation and Renewal, Ballinger Publishing Company, Cambridge, MA. Yahya, S. and Goh, W.K. (2002), “Managing human resources toward achieving knowledge management”, Journal of Knowledge Management, Vol. 6 No. 5, pp. 457-68.

Further reading Dana, L.P., Korot, L. and Tovstiga, G. (2005), “A cross-national comparison of knowledge management practices”, International Journal of Manpower, Vol. 26 No. 1, pp. 10-22. Di Bella, A. and Nevis, E.C. (1998), How Organisations Learn: An Integrated Strategy for Building Learning Capacity, Jossey-Bass Publishers, San Francisco, CA. Huseman, C.R. and Goodman, P.J. (1999), Leading with Knowledge – The Nature of Competition in the 21st Century, Sage Publications, London. Leibold, M., Probst, J.B. and Gibbert, M. (2002), Strategic Management in the Knowledge Economy, Wiley, Chichester. Matusik, S. (2002), “Managing public and private firm knowledge within the context of flexible firm boundaries”, in Choo, W.C. and Bontis, N. (Eds), The Strategic Management of Intellectual Capital and Organisational Knowledge, Oxford University Press, Oxford. Nonaka, I. and Takeuchi, H. (1995), The Knowledge-Creating Company: How Japanese Companies Create the Dynamics of Innovation, Oxford University Press, Oxford. O’Donnell, D., O’Regan, P. and Coates, B. (2000), “Intellectual capital: a Habermasian introduction”, Journal of Intellectual Capital, Vol. 1 Nos 2/3, pp. 187-200. Yoo, Y. and Torrey, B. (2002), “National culture and knowledge management in global learning organisation”, in Choo, W.C. and Bontis, N. (Eds), The Strategic Management of Intellectual Capital and Organisational Knowledge, Oxford University Press, Oxford.

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About the authors Ivan Svetlik is professor of HRM, labour market and employment policy at the University of Ljubljana. His research is focused on human resources and knowledge management, employment, education and training. He coordinates nationally the CRANET research project and contributes to the publications of this network, such as HRM’s Contribution to Hard Work, Human Resource Management in Europe and Managing Human Resources in Europe. Ivan Svetlik is the corresponding author and can be contacted at: [email protected] Eleni Stavrou-Costea is Assistant Professor of Management and Organisation at the University of Cyprus. She has published chapters in books as well as articles in various academic journals. Her research interests include flexibility at work, strategic human resource management, intergenerational transitions, and organisational culture.

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Knowledge management and innovation performance

Knowledge management

˚ ke Lundvall and Peter Nielsen Bengt-A Aalborg University, Aalborg, Denmark

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Abstract Purpose – The purpose of this paper is to show why the establishment of “learning organisations” must be a central element of knowledge management – especially in firms operating on markets where product innovation is an important parameter of competition. Design/methodology/approach – The argument straddles and combines insights related to management and organisation theory with an evolutionary economic analysis of the relationship between innovation, learning and knowledge. It is supported by an empirical analysis of survey data on Danish private sector firms. The survey was addressed to all firms in the private urban sector with 25 or more employees, supplemented with a stratified proportional sample of firms with 20-25 employees. Findings – The analysis shows that firms that introduce several organisational practices, assumed to characterise the learning organisation, are more innovative than the average firm. Research limitations/implications – The empirical findings are limited to the private sector and do not cover public sector organisations. Practical implications – The learning organisation characteristics have a positive impact on dynamic performance and there are obviously lessons to be learned from the successful firms operating in turbulent environments that introduce specific organisational characteristics such as job rotation, inter-divisional teams, delegation of responsibility and reducing the number of levels in the organisational hierarchy. Originality/value – The paper puts “knowledge management” into the wider concept of “learning economy” and shows how a key element of knowledge management is to enhance the learning capacity of the firm. Keywords Knowledge management, Learning, Organizational change Paper type Research paper

Introduction Taken in its broadest sense, knowledge management is an ancient phenomenon. The competences of employees and how they are combined into organisational capabilities has always been a key to economic performance and wise managers have always been aware of the need to utilise and develop knowledge in the interest of the organisation. But it is only recently that knowledge management has become explicit in the management literature. According to Prusak (2001), the first conference that focused on “knowledge management” took place in 1993. Today the concept has become commonplace all over the world. The major impact of making “knowledge management” explicit is its increased attention. Prusak (2001) writes that the concept has roots in three different management traditions: information management, the quality movement and human capital. These different perspectives give different emphasis to what knowledge management should accomplish. Their definition of what is valuable knowledge is different and the idea

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about what “managing” knowledge means is different, making the future direction of knowledge management difficult to predict. There is little doubt that the information technology revolution has changed fundamentally the role of knowledge in the economy. It has given inexpensive and worldwide access to some types of information. It has also offered new tools both for handling information and for advancing processes of knowledge creation and innovation. Therefore it is not surprising that knowledge management for some scholars and experts primarily signifies the use of advanced software, the codification of tacit knowledge and knowledge sharing through information systems. But as we shall argue below, the impact of the wider use of information and communication technology is complex and contradictory (Lundvall, 1997). One of the major impacts is that tacit knowledge becomes scarcer and therefore managing this kind of knowledge becomes more important. Another consequence is the acceleration in the rate of change that brings us into “a learning economy” where the capability to learn becomes more important than given sets of specific capabilities (Lundvall, 2003). At the end of the paper, we present an empirical study based upon Danish Survey data where a strong correlation is shown between the introduction of multiple management techniques associated with “the learning organisation” and the innovative performance of firms. Danish firms that use many of these techniques are much more prone to introduce new products than firms that use few of these techniques, even after we control for size, sector and form of ownership. This implies that knowledge management, especially in sectors with rapid technological change, needs to focus more on the process of learning than on locating and allocating a given set of knowledge assets. Without forming learning organisations, information systems do not contribute to the dynamic performance of the firm and such systems need to be designed in such a way that they support the formation, diffusion and use of tacit knowledge. So while, at first glance, the wide use of information technology points us toward a definition of knowledge management as increasingly related to the use of information systems and to the management of codified knowledge we argue that paradoxically it calls for giving the formation and use of tacit knowledge more attention than before. We conclude that one of the most important tasks of knowledge management is not to steer in detail the processes of knowledge creation but rather to create “framework conditions” that stimulate agents within and outside the organisation to engage in interactive learning. Information technology is a helpful tool in this process but it is seldom “the solution” to knowledge management problems. We propose that knowledge management is more of a “social art” than a scientific discipline; knowledge management cannot be reduced to a set of techniques. The fact that knowledge management operates close to the human mind makes it necessary for managers to operate with finesse and on the basis of intuition and wisdom. On the contradictory impact of information technology There is a normative bias in western civilization in favour of explicit and well-structured knowledge and there are continuous efforts to automate human skills. One historical example is the effort to transfer the knowledge of skilled workers into machinery connected with Taylorism. Present efforts to develop general business information systems and expert systems may be seen as symptoms of this bias. For the

knowledge manager, codifying knowledge may be seen as a way to make the organisation less dependent on employees (Lundvall, 1997). But the business experience of firms that should be assumed to be world champions in managing knowledge, be it IBM, Hewlett Packard or Microsoft, is rather mixed, with ups and downs in performance (Eliasson, 1996). As can be seen from their history none of these organisations has been able to develop the perfect expert system to manage the firm. They remain highly dependent on the skills, know how and intuition of their top managers. Actually management is an area where codifying knowledge is most difficult and this is especially true for the management of knowledge (OECD, 2000). So far automating human skills has proved to be quite successful in relation to tasks taking place in a stable environment. The success of chess programs demonstrate that in games where the rules remain constant even very complex decision making may be programmed and automated. The most important delimitation on codification efforts is a high rate of change in the environment. Where the rules or the problems encountered change the benefits from codifying knowledge are limited since codification tends to create routines that are unsustainable and inefficient in the long run (Hatchuel and Weil, 1995). Highly automated process industries may be extremely cost-efficient as long as technologies and markets remain stable but at some time when the products lose their competitiveness because of more attractive substitutes they leave behind them rust-belt problems. The wider use of information and communication technology (ICT) enhances both the incentives and the possibilities to codify knowledge (David and Foray, 1995). The share of knowledge that can be transformed from being tacit to becoming explicit information grows. The capacity to codify and to handle codified knowledge becomes more important in the firm. In this light it seems natural that knowledge management should be seen just as a further development of information management. It might even be considered that the era of tacit knowledge is over. But this is only one side of the coin (Johnson et al., 2002); the other side is that the very growth in the amount of information made accessible to economic agents increases the demand for skills in selecting and using information intelligently. So, as more skills are transformed into a codified form, demand will grow for complementary tacit knowledge. This is one reason why experience-based learning becomes increasingly important. But the most important reason is that the widened use of ICT speeds up change and the acceleration makes it less meaningful and attractive to engage in the development of codification and information systems. ICT speeds up change through different mechanisms. First, the rate of innovation within ICT is high and its diffusion to all sectors of the economy imposes change on these sectors. Second, ICT has become an important tool in speeding up innovation in several sectors including drug design in pharmaceuticals and physical design in most other sectors[1]. Third, it makes it easier to communicate over long distances and hereby it fuels the globalisation of the world economy. While the potential for codification of activities may be growing, more and more activities operate in contexts where rules and problems change more rapidly than before. Automation and introduction of codified routines in such activities will be costly and give dubious results. The capacity most in demand is to cope with new

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tasks and problems. This is why skills and know-how become scarcer and more important for performance than before. If the main impact of ICT is a speed-up of processes of change, the use of information technology may be regarded from a different perspective where the emphasis is upon its potential to re-enforce human interaction and interactive learning. Here the focus is not upon its potential for substituting for tacit knowledge but rather upon how it can support the creation, use and sharing of tacit knowledge. Electronic mail systems connecting agents sharing common specific codes of communication and frameworks of understanding can have this effect. Communities of practice and epistemological communities tend to become increasingly important for the creation of use of knowledge both locally and globally. Wide access to data and information among employees can further the development of common perspectives and objectives for the firm. Interactive learning in external networks may be re-enforced by the intelligent use of ICT-technology. A taxonomy of knowledge One reason why it is difficult to design successful knowledge management is that knowledge is a slippery subject (Winter, 1987). If it is difficult to agree on what knowledge means; it is even more difficult to agree on how to manage it. There have been different attempts to work out the most important distinctions between different kinds of knowledge; in turn, different taxonomies have been proposed (Lam, 2000). Knowledge may be embodied in people or built into artefacts. Much knowledge is collective rather than individual and it may be embedded in organisations or networks (Arrow, 1994). Standing alone it is intangible and difficult to grasp. The very meaning of knowledge differs depending on context. A classical taxonomy makes a distinction between the four categories: data, information, knowledge and wisdom (Ackoff, 1989). It is assumed that data are raw facts without internal organisation. When structured and put into context they carry some meaning and become information. It is only when the human mind activates information that it becomes knowledge. Wisdom is assumed to bring in a deeper understanding and ethical grounds for action. In relation to knowledge management we do not find this taxonomy very useful. Actually it fails to make some of the most important distinctions and by doing so it sometimes results in a biased understanding of knowledge as basically a cognitive category referring to the individual. This is problematic since procedural knowledge (know-how) both individual and collective (as shared routines) is a key to economic performance. More than a decade ago Lundvall and Johnson (1994) introduced a different set of distinctions: know-what, know-why, know-how and know-who[2]. Know-what refers to knowledge about “facts”. How many people live in New York, what are the ingredients in pancakes and when was the battle of Waterloo, are examples of this kind of knowledge. Here, knowledge is close to what is normally called information – it can be broken down into bits. Know-why refers to knowledge about principles and laws of motion in nature, in the human mind and in society. This kind of knowledge has been extremely important for technological development in certain science-based areas such as for example chemical and electric/electronic industries. To have access to this kind of knowledge will often make advances in technology more rapid and reduce the frequency of errors in procedures of trial and error.

Know-how refers to skills, such as the capability to do something. It might relate to the skills of manual workers. But actually it plays a key role in all activities in the economic sphere. The businessman judging the market prospects for a new product or the personnel manager selecting and training the staff have to use their know-how. It would also be misleading to characterise know-why as science-related and know-how as practical-related. One of the most interesting and profound analyses of the role of know-how is actually about how the advanced scientist makes research on the basis of personal skills (Polanyi, 1958/1978, 1966). Conversely not all know-why knowledge is scientific. In everyday life, when interpreting what is happening, models of causality that have very little to do with science are applied. Know-how is typically a kind of knowledge developed and kept within the border of the individual firm or the single research team. But as the complexity of the knowledge base is increasing co-operation between organisations tends to develop. One of the most important rationales for the formation of industrial networks is the need for firms to be able to share and combine elements of know-how. Similar networks may be formed between research teams and laboratories. This is one reason why know-who becomes increasingly important. The general trend towards a more composite knowledge base where a new product typically combines many technologies and each technology is rooted in several different scientific disciplines, together with the speed up of change, makes it crucial to have access to many different sources of knowledge. Know-who involves information about who knows what and who knows to do what. But it also involves the social capability to co-operate and communicate with different kinds of people and experts. These distinctions are closer to everyday language than the first taxonomy. We prefer to use “information” as part of knowledge rather than as something distinct from knowledge. We define information as knowledge that has been transformed into codes so that it can be saved in a computer and sent through electronic media. In the next section we will discuss what elements of knowledge can be transformed into information and the consequences for knowledge management of the wider use of information and communication technologies. The impact of the information technology revolution on the four kinds of knowledge Know-what is a kind of knowledge that can be brought into databases and search machines in a rather simple way. These are still far from costless to use, however. Still it may take many attempts to surf the net before the precise information looked for pops up on the screen. ICT has made this kind of knowledge much more accessible all over the world. Even so, having direct access to persons (know-who) who are experts in a specific field may save much time and lead to more precise results. For specialised kinds of “know what” such as seldom addressed medical and legal cases, the only reliable source of information may still be a human expert and her/his personal memory. Know-why with roots in science may already exist in a codified form. Sometimes the code is so complex that knowledge gives meaning only to a handful of outstanding scientists but in principle there is open access to the information through the Internet and other channels. In other fields “know-why” is experience-based and there is no scientific causal analysis to explain why a certain factor regularly triggers specific effects. Here information technology may play a role in speeding up analytical

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processes. The growth of codified knowledge may be dramatic in certain fields such as pharmaceuticals and even experts will get growing difficulties to follow new developments in their respective field of knowledge. In order to make this kind of knowledge useful it is again crucial to have access to human expertise (know-who) that can sort out the most promising directions to follow. Know-how is perhaps the kind of knowledge where information technology and codification has the most to offer but also the one where the greatest barriers have to be overcome. Work on “expert systems” shows that even when tasks are reasonably simple the operation of the expert system developed will differ from the actual operation of the expert (Hatchuel and Weil, 1995). Firms that have over-emphasised the use of business information systems in their decision-making process have often run into trouble (i.e. the problems of the business system’s giant IBM to develop a successful management strategy illustrate the point) (Eliasson, 1996). Know-who sounds somewhat pedestrian as compared to “know-why” and “know-how” but actually it may have become the most important kind of knowledge in the learning economy. The combination of increasing complexity and rapid change makes it crucial to know who knows what and who knows to do what. Information technology has a role to play since it makes informal networks more efficient in overcoming distance in time and space. The increased importance of “know-who” type of knowledge makes it necessary to take into account the social dimension of economic processes. This kind of knowledge is strongly intertwined with trust and what has increasingly been defined as “social capital” (Woolcock, 1998). And trust is a very peculiar resource. According to Arrow (1971) “it cannot be bought on the market and if it could it would have no value whatsoever”. Therefore, in this area, the role of ICT can only be to operate as a superstructure that must be built upon a basis of social relationships. Summing up on the impact of ICT on knowledge creation It follows from the analysis of the four kinds of knowledge that information technology increases the stock of codified knowledge and that skill and competencies (tacit and explicit) related to the use of ICT- technologies become increasingly important. But it also follows that rapid change that is a major consequence of the wide use of ICT, gives an even stronger weight to tacit skills. This is one reason why outstanding experts in management, finance and science get even better paid in the learning economy. If their skills could readily be transferred to expert systems we would expect to observe a very different development of income distribution. Individual knowledge remains important. Attempts to gather it and codify it into data banks to be shared among large numbers of employees will often prove costly and result in information overflow. Only if organisations are involved in a rather homogenous and stable set of activities is such a strategy attractive. But since the long-term economic success of firms increasingly reflect the capability to adapt to change (flexibility) and the capability to impose change (innovation) tacit knowledge remains crucial for economic success. Collective tacit knowledge also tends to grow in importance. Especially in fields where the rate of innovation and knowledge creation is high, there will be a growing tendency to take over other organisations with the collective tacit knowledge that they embed.

The learning economy as context We see the information technology revolution as one major factor behind the formation of “the learning economy” (Lundvall, 2003). The term marks a distinction from the more generally used term “the knowledge-based economy”. The learning economy concept signals that the most important change is, not the more intensive use of knowledge in the economy, but rather that knowledge becomes obsolete more rapidly than before. Therefore it is imperative that firms engage in organisational learning and that workers constantly develop new competencies. The increased rate of change can be illustrated by the fact that it is claimed that half of the skills that a computer engineer has obtained during his education will have become obsolete one year after the exam has been passed, while the “half-life” of skills for all educated wage earners is estimated to be eight years (Ministry of Education, 1997, p. 56)[3]. A learning economy is thus one in which the ability to attain new competencies is crucial for the success of individuals and for the performance of firms, countries and regions. The background for the crucial importance of learning is that the combination of globalisation, information technology and deregulation of formerly protected markets leads to more intense competition and to more rapid transformation and change. Both individuals and companies are increasingly confronted with problems that can be solved only through forgetting old and obtaining new competencies. The rapid rate of change is reinforced by the fact that intensified competition leads to a selection of organisations and individuals that are capable of rapid learning, thus further accelerating the rate of change. The transition to a learning economy confronts individuals and companies with new challenges. We see the growing emphasis on new organisation forms and networking as a response to the challenges posed by the learning economy. In a rapidly changing environment it is not efficient to operate a hierarchical organisation with many vertical layers and with departments and functions operating separately within the firm. In a rapidly changing environment it takes too long to respond when the information obtained at the lower levels has to be transmitted to the top and back down to the bottom of the pyramid. This is why we see a drive toward flat organisations with strong focus on decentralisation and horizontal communication. In many instances relational contracting and networking enhance functional flexibility since it gives access to complementary external competence that it would take too long to build in-house. One important result from the empirical analysis that follows is that the new organisation forms which tend to support competence building through “learning by doing” and “learning by interacting” enhance the capability to pursue product or service innovation. As we shall see in the next section innovation, learning and knowledge creation are interrelated. Knowledge is both a crucial input and a crucial output of innovation processes. Innovation and knowledge creation A problem with linking organisational forms to economic performance is that it is difficult to develop valid and reliable indicators both for organisational forms and for economic performance. Do specific management techniques promote learning? Do they contribute to knowledge creation? Without some systematic analysis of these issues we have to rely on “story-telling” about the success of specific changes in specific

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organisations. But it is well-known that transferring a “best practice” from one context to another is highly problematic (Lundvall and Tomlinson, 2002). One way to overcome this problem is to link innovation, learning and knowledge creation with each other. Innovation represents – by definition – something new and therefore adds to existing knowledge. Actually, many authors using the concept of knowledge creation and knowledge production refer to technological knowledge and to technical innovation as the output of the process (Antonelli, 1999; Nonaka and Takeuchi, 1995). In new growth theory, the output of the R&D sector is viewed either as a blueprint for a new production process that is more efficient than the previous one or as a production of new semi-manufactured goods that cannot easily be copied by competitors (Verspagen, 1992, pp. 29-30). A striking characteristic of knowledge production resulting in innovation is that knowledge, in terms of skills and competencies may be perceived as the most important input. In this sense, it recalls a “corn economy”, in which corn and labour produce more corn than is used up in the process. But it differs from such an economy in one important respect. While the corn used to produce corn “disappears” in the process, skills and competencies improve with use. Important characteristics of knowledge reflect that its elements are not scarce in the traditional sense: the more skills and competencies are used, the more they develop. This points to knowledge production as a process of joint production, in which innovation is one kind of output and the learning and skill enhancement that takes place in the process is another. It is tempting to see innovation as a linear process and to assume that new scientific results are the first step in the process, technological invention the second step, and the market introduction of innovations as new processes or products the third step. A rich body of empirical and historical literature shows that feedback loops are fundamental and that the one-way road from new scientific results to the new product is the exception rather than the rule (Rothwell, 1977; von Hippel, 1988; Lundvall, 1988). The recent models of innovation emphasise that knowledge production/innovation is an interactive process where the interaction of firms with customers, suppliers and knowledge institutions is crucial for the outcome. Empirical analysis confirms that firms seldom innovate alone (Christensen and Lundvall, 2004)[4]. One important implication is that any analysis of innovation and knowledge production at the firm level needs to take into account the network positioning of the firm and the degree to which the firm can draw upon competence from outside. Learning organisations combine inter- with intra-organisational processes. Competence as the outcome of knowledge production The change from a linear to an interactive view of innovation and knowledge production has also been a way to connect to each other innovation and the further development of competence. The innovation process may be described as a process of interactive learning in which those involved increase their competence through engaging in the innovation process. In economics, various approaches to competence-building and learning exist. One important contribution is Arrow’s (1962) analysis of “learning by doing”, in which he demonstrated that the efficiency of a production unit engaged in producing complex systems (airplane frames) grew with the number of units already produced and argued that this reflected experience-based learning. Later, Rosenberg (1982) introduced

“learning by using” to explain why efficiency in using complex systems increased over time (the users were airline companies introducing new models). The concept of “learning by interacting” points to how interaction between producers and users in innovation enhances the competence of both (Lundvall, 1988). A more recent analysis of learning by doing focuses on how confronting new problems in the production process triggers searching and learning, which imply interaction between several parties as they seek solutions (von Hippel and Tyre, 1995). In most of the contributions in economic theory, learning is regarded as the unintended outcome of processes with a different aim than learning and increasing competence. Learning is seen as a side effect of processes of production, use, marketing, or innovation. The management literature has a more instrumental perspective and points to the importance of establishing “learning organisations” (Senge, 1990). According to this literature, the way an organisation is structured will have a major effect on the rate of learning that takes place. The appropriate institutional structures may improve knowledge production in terms of competence building based on daily activities. It follows from our analysis of innovation and competence-building that a move towards learning organisations needs to be reflected in changes both in the firm’s internal organisation and in its inter-firm relationships. Within firms, the accelerating rate of change makes multi-level hierarchies and strict borders between functions and departments inefficient. It makes decentralisation of responsibility to lower-level employees and formation of multi-functional teams a necessity. This is reflected in the increasing demand for workers who are at the same time skilful, flexible, co-operative and willing to shoulder responsibility. But in order to speed up the response to changes in markets and technologies relationships with suppliers, customers and knowledge institutions may need to become both more selective and more intense. Learning organisations and innovation – the Danish case In what follows we will show first that the probability of successful product innovation increases when the firm has organised itself in such a way that it promotes learning. Second, we will demonstrate that organisational forms promoting learning are multi-dimensional: they typically combine several of a number of internal and external relationships and activities. Methodology The empirical analysis is based on a 2001 survey addressed to all Danish firms in the private sector – not including agriculture – with 25 or more employees, supplemented with a stratified proportional sample of firms with 20-25 employees. In turn 6991 questionnaires were sent to the selected firms. Information was collected from human resource managers. We got 2007 usable responses and we have integrated them into a cross-sectioned data set. The overall response rate of the survey was 29 per cent. A closer response analysis, broken down by industry and size, shows acceptable variations on response rates. Non-respondent information on some of the potential dependent variables together with comparison to other surveys, do not indicate unacceptable bias (Lundvall and Nielsen, 2005). Obtaining a meaningful quantitative measure of innovation and innovative behaviour on the basis of information collected in firms belonging to industries with

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very different conditions is not unproblematic. The phenomenon that firms refer to may vary in relation to conditions and configurations. Our data indicate that we are confronted with incremental qualitative change rather than radical change when firms declare that they, in the period of 1998-2000, have introduced new products or services on the market. Three fourths of the innovations introduced within the period 1998-2000, were already known at the national as well as on the international markets. About 13 per cent of the firms have introduced at least one product or service innovation new for the national market, although already existing in world markets. A small group of firms (6 per cent) have introduced at least one innovation new both on the national and the world market. In the survey, we measured the incidence of an array of organisational dimensions, which all directly or indirectly refer to both classical and contemporary theories dealing with the relation between communication, knowledge transformation, interaction and learning in relation to innovation in organisations. In this way the dimensions become our operational expressions of “the learning and innovating organisation”: cross occupational work groups, integration of functions, softening demarcations, delegation of responsibility and self directed teams are empirical indicators, referring to Moss Kanter’s theory of integrative organisation (Moss Kanter, 1983) and Burn’s and Stalker’s organic organisations (Burns and Stalker, 1961). Indirectly these dimensions also concern the leadership dimension, which is highly relevant for knowledge creation (Dierkes et al., 2001). Quality circles and proposal collection systems are indicators of quality management and knowledge management (Nonaka and Takeuchi, 1995). Tailored educational system and educational planning indicate human resources development (Bratton and Gold, 2003) and cooperation with external actors refer to innovation as an interactive process (Lundvall, 1992). In Table I the dimensions are classified in relation to theoretical aspects. Here we will analyse to what degree the organisational characteristics and practises complement each other and thus increase the chances of product and service (P/S) innovation cumulatively. This might reflect “bundles” of organisational techniques that support each other and that only when the firm has got several of the elements working together will it harvest the full benefits in terms of innovative behaviour. Building on such arguments, an additive index has been constructed based upon all the 14 organisational characteristics shown in Table I. On the basis of the additive index we have classified the firms in three groups according to how many organisational characteristics and practices they have adopted in their organisations. We have thus divided the firms into three main groups: (1) Low level learning organisations – firms that have introduced zero to four of the practices. (2) Medium level learning organisations – firms that have introduced five to eight practices. (3) High level learning organisations – firms that have introduced nine to 14 practices. This index may be assumed to reflect the degree of organisational sophistication. Applying many characteristics and practices signals conscious-ness in terms of knowledge management. In other words it signals a culture of change and learning in the firms. In Table II results of this construction are shown. Table II shows how

Knowledge management

Theoretical perspective

Organisational characteristics and practices

The organic and integrative organisation – focus on internal functional flexibility Burns and Stalker (1961) Moss Kanter (1983)

Cross-occupational working groups Integration of functions Softened demarcations Delegation of responsibility Self-directed teams Quality circles/groups Systems for collection of employee proposals

Quality management – focus on engaging employees Nonaka and Takeuchi (1995) Human development – focus on competence building Bratton and Gold (2003) Compensation system – focus on incentives Bratton and Gold (2003) External communication – focus on external functional flexibility Lundvall (1992)

Variables All firms Fewer than 50 employees 50-99 employees 100 and more employees Manufacturing Construction Trade Other services Business services Danish group Foreign group Single firm Standard product Customised product

217

Education activities tailored to the firm Long-term educational planning Wages based on qualifications and functions Wages based on results Closer cooperation with customers Closer cooperation with subcontractors Closer cooperation with universities and technological institutes

Table I. Theoretical perspectives and organisational characteristics

High (9-14) (%)

Medium (5-8) (%)

Low (0-4) (%)

n

28.5 18.1 35.0 45.1 36.3 14.5 24.5 19.6 41.2 30.1 40.7 22.3 29.2 29.8

44.3 45.9 42.3 43.3 42.9 42.8 48.3 45.1 40.3 44.7 43.8 44.5 45.1 44.9

27.2 36.0 22.7 11.6 20.8 42.8 27.2 35.3 18.5 25.3 15.5 33.2 25.7 25.3

2,007 1,048 437 490 725 318 563 184 213 701 388 903 725 1,192

frequent high-level learning organisations are in different categories of size, industry, ownership and production. By grouping all the firms according to the index of learning organisation development we get 27 per cent in the low category, 44 per cent in the medium and 28 per cent in the high category. Table II shows that this distribution is size-dependent. Among firms with fewer than 50 employees, only one out of five firms have developed a learning organisation at the high level while the same is true for every second of the larger firms. With growing firm size, the share of highly developed firms increases. Table II also shows that the frequency of high level learning organisations varies across industries. More than 40 per cent of the firms in business services are in the category of highly developed learning organisations, while the same is true for 36 per cent of the firms in manufacturing. The rest of the industries lie below the average.

Table II. Learning organisation development (percent horizontal)

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Another interesting result is that firms owned by foreign groups have high share in the category of most developed. Firms owned by Danish groups are closer to the general average and single – stand alone, often family firms – are below the average. The presence of foreign owned firms seems to constitute “a progressive element” in the Danish economy while the often cherished family owned stand-alone firms seem to be lagging behind both in terms of technological and organisational sophistication.

218 Organisational practices and product innovation How does the frequency of use of organisational dimensions affect knowledge production and learning in the firms, as indicated by product and service (P/S) innovations? In Table III the different categories, representing increasing levels of learning organisations are tested in a logistic model with P/S innovation as dependant variable, and with control for firm size, industry, as well as form of ownership. We find a five times higher chance of P/S innovation in the high level category, and even in the medium category the chance is twice as high as in the low category, which is used as a baseline. Among the other factors included in the model, manufacturing and business services remain significant with 2.3 higher chance of P/S innovation and construction is negatively significant with a chance of 0.7. The effect of large size (100 þ ) is positive but moderate. Danish group ownership and single firms have a chance below the benchmark category, i.e. foreign-owned firms. In sum, the model has shown important and significant effects of the development of what we call learning organisation on P/S innovation. This illustrates that “learning organisations” that combine functional flexibility with investment in human resources, incentive systems and networking are much more prone to innovate irrespective of sector and size. It also illustrates that there is no clear distinction between “innovation management” and “knowledge management”. The organisational characteristics that promote adaptive learning also promote innovation. To install them is an important task both for “knowledge managers” and “innovation managers”. It does not follow from the analysis that the adoption of any single set of characteristics used to classify the learning organisation will enhance the capacity of the firm to innovate, learn and create new knowledge. The context matters and we find that in certain sectors where change is slow, such as construction and transport firms may survive and prosper with little effort to engage in innovation and learning. However, the study indicates a general direction in which knowledge management Variables

Table III. Logistic regression of learning organisation level categories

High level Medium level Manufacturing Construction Business services 100 and more Danish group Single firm

Effect

Lower

Higher

Estimate

Chi-sq.

P-value

5.18 2.20 2.35 0.69 2.27 1.61 0.76 0.58

3.90 1.71 1.62 0.45 1.46 1.26 0.58 0.44

6.90 2.83 3.40 1.08 3.54 2.07 1.00 0.76

0.82 0.39 0.54 2 0.68 0.51 0.30 2 0.14 2 0.28

127.30 37.11 38.69 28.35 15.40 14.23 3.93 15.85

, 0.0001 , 0.0001 , 0.0001 , 0.0001 , 0.0001 0.0002 0.0475 , 0.0001

may enhance the dynamic performance of firms in sectors where there is rapid change in technologies and customer needs. It is interesting to note that organisational forms that are often thought of as stimulating “learning as adaptation” also seem to be supportive of knowledge creation and innovation. As argued above innovation, competence building and adaptation are intertwined, and promoting one is a way of promoting the other. The distinction between HRM, knowledge management and management of innovation as different analytical fields and as the responsibility of distinct professions may therefore be worth to reconsider. Conclusions In the first three sections we discussed knowledge management in the light of the contradictory impact of information technology on the relationships between tacit and codified knowledge. We argued that paradoxically the wide use of information makes tacit knowledge more crucial for the performance of the firm. In the third section we went a step further and argued that the information technology revolution has given rise to a new type of economic dynamic at the macro-level and we referred to this as “a learning economy”. In the learning economy the dynamic performance will reflect the capability to build new competences and to respond to change. In the fifth section we tested this hypothesis on the basis of Danish data and showed that learning organisation characteristics have a positive impact on dynamic performance. One implication of our analysis is that any attempt to reduce knowledge management to the use of advanced information systems would be misdirected and harmful. But we also think that the very idea of “managing” knowledge may be misleading. In his seminal paper on knowledge and competence Winter (1987) makes an attempt to specify in what sense and to what degree knowledge is an “asset” and we believe that he tries to do so because most management scholars would prefer knowledge to be thought of as one among other kinds of assets. The efforts to bring annual reports on company knowledge in line with the accountancy and reporting systems of other assets may also be seen in this light. A focus on knowledge as “a set of assets” may be too static in the rapidly changing world we have indicated by the concept “the learning economy”. Here the key to long-term competitiveness is the learning (and forgetting) capability of the firm rather than what is already known. Therefore a key element of knowledge management is to enhance the learning capacity of the firm. One way to do so is by building a learning organisation. This is more related to designing organisational procedures and routines than it is to managing assets. Software programs and specific techniques such as the use of the balanced score-card (Kaplan and Norton, 1992) may be useful ways to organise an increasingly complex knowledge-base in firms. However, they are not efficient substitutes for managers with experience-based skills in handling human relationships. To leave it to inexperienced managers to implement and use, such tools may be not only inefficient but actually damaging for the learning capability of the firm. For instance, one outcome of using the balanced scorecard technique might be a characterisation of people within the organisation once and for all, based upon who they are and what they can do at a specific moment in time. This might lead to a “freezing” of the competence

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profile of individuals, which is not at all useful either for the individual or for the learning capability of the organisation. Therefore it might be a good idea to think carefully about what should be meant by “managing” in the context of knowledge management. If “management” refers to an ambition to give managers complete control of what employees learn, “knowledge management” would damage the dynamic performance of the organisation. Little space would be left for individual and collective creativity and for the use of intuition. The alternative is to establish “framework conditions” – organisational and cultural – promoting efficient use, creation and diffusion of knowledge and then to leave the process to evolve as best as it can. Actually, we have argued that this second model is much closer to representing “best-practice” for organisations exposed to strong competition and operating on the basis of on-going innovation. As illustrated by the data presented above and by many other empirical studies of “learning organisations” or “high-performance workplaces” lessons may be learnt from successful firms operating in turbulent environments that introduce specific organisational characteristics such as job rotation, inter-divisional teams, delegation of responsibility and reduction in the number of levels in the organisational hierarchy. The idea behind such changes is to enhance the learning in the firm and to make the firm more responsive to changes in its environment. As long as they work well they may also reduce the need for daily management, including knowledge management. Specialist “knowledge managers” may play a role in initiating processes of organisational change in the right direction together with managers in charge of human resources, R&D and innovation. But each single person with a management responsibility from the foreman at the factory floor to the top manager can contribute to, or block, the kind of organisational change that is required. Our data and case studies indicate that it is not always employees who block and top management who promote change. Often the necessary changes take place in connection with a change in top management (Gjerding, 1996; Lund and Gjerding, 1996). But, again, the use of such techniques, while helpful, cannot substitute for skilful knowledge management where the focus is on people and on relationships between people. Even in a science-based economy with wide use of information technology the social dimension remains crucial for learning. To make sure that people get recognition both for what they do and learn and for what they are and want to be is crucial. Employees need to know who to contact and collaborate with in specific situations and they need to have the confidence and incentive to do so when necessary. To establish a “learning culture” is a difficult management art that needs to be based on personal experience and wisdom. Notes 1. New applications of information technology change the character of knowledge-creation at certain stages of the innovation process. Developing and testing drugs, and the design of aircrafts with the help of computers and the use of computer aided design in many other areas illustrate a successful transfer of problem-solving from human skills to computers. One consequence is a dramatic speed-up formerly time-consuming trial and error processes and of testing new combinations (Foray and Lundvall, 1996, pp. 14-15). 2. At least two of these categories have roots back to Aristoteles’ three intellectual virtues. Know-why is similar to Episteme and know-how to his concept of “Techne”. But the

correspondence is not perfect since we will follow Polanyi and argue that scientific activities always involve a combination of know-how and know-why. Aristoteles’ third category “Phronesis” relates to the ethical dimension and to current debates on the importance of trust and social capital in the context of learning. Flyvbjerg (1991) includes an interesting discussion of the relevance of Aristoteles for modern social science. 3. The outlines of the learning economy perspective were first sketched in Lundvall (1992) and further developed in Lundvall and Johnson (1994). The analysis has much in common with ideas developed in Drucker (1993) but was developed without direct inspiration from this source. 4. This is also the background for developing a systemic approach to knowledge production. Innovation systems are constituted by actors involved in innovation and by relationships between actors. The actors include firms, technological institutes, universities, training systems and venture capital. Together they constitute the context for knowledge production and innovation. The specific constellations differ across sectors, regions and nations. Innovation systems are typically specialised in terms of their knowledge base, and the specific mode of innovation will reflect institutional differences (Freeman, 1987; Lundvall, 1992; Nelson, 1993; Edquist, 1997; Lundvall, 2002).

References Ackoff, R.L. (1989), “From data to wisdom”, Journal of Applied System Analysis, Vol. 16, pp. 3-9. Antonelli, C. (1999), The Microdynamics of Technological Change, Routledge, London. Arrow, K.J. (1962), “The economic implications of learning by doing”, Review of Economic Studies, Vol. 29 No. 3, pp. 155-73. Arrow, K.J. (1971), “Political and economic evaluation of social effects and externalities”, in Intrilligator, M. (Ed.), Frontiers of Quantitative Economics, North Holland, Amsterdam. Arrow, K.J. (1994), “Methodological individualism and social knowledge”, American Economic Review, Vol. 89 No. 2, pp. 1-9. Bratton, J. and Gold, J. (2003), Human Resource Management – Theory and Practice, Palgrave Macmillan, New York, NY. Burns, T. and Stalker, G.M. (1961), The Management of Innovation, Tavistock, London. ˚ . (Eds) (2004), Product Innovation, Interactive Learning and Christensen, J.L. and Lundvall, B.-A Economic Performance, Elsevier, Amsterdam. David, P. and Foray, D. (1995), “Accessing and expanding the science and technology knowledge-base”, STI Review, No. 16, pp. 16-38. Dierkes, M., Antal, A.B., Child, J. and Nonaka, I. (Eds) (2001), Handbook of Organizational Learning and Knowledge, Oxford University Press, New York, NY. Drucker, P. (1993), The Post-Capitalist Society, Butterworth-Heinemann, Oxford. Edquist, C. (Ed.) (1997), Systems of Innovation: Technologies, Institutions and Organizations, Pinter Publishers, London. Eliasson, G. (1996), Firm Objectives, Controls and Organization, Kluwer Academic Publishers, Amsterdam. Flyvbjerg, B. (1991), Rationalitet og magt, Akademisk Forlag, Odense. ˚ . (1996), “The knowledge-based economy: from the economics of Foray, D. and Lundvall, B.-A ˚ . (Eds), Employment knowledge to the learning economy”, in Foray, D. and Lundvall, B.-A and Growth in the Knowledge-based Economy, OECD Documents, Paris.

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Freeman, C. (1987), Technology Policy and Economic Performance: Lessons from Japan, Pinter Publishers, London. Gjerding, A.N. (1996), “Organisational innovation in the Danish private business”, DRUID working paper, No. 96-16, Department of Business Studies, Aalborg University, Aalborg. Hatchuel, A. and Weil, B. (1995), Experts in Organisations, Walter de Gruyter, Berlin. ˚ . (2002), “Why all this fuss about codified and tacit Johnson, B., Lorenz, E. and Lundvall, B.-A knowledge?”, Industrial and Corporate Change, Vol. 11 No. 2, pp. 245-62. Kaplan, R.S. and Norton, D.P. (1992), “The balanced scorecard: measures that drive performance”, Harvard Business Review, Vol. 83, pp. 172-80. Lam, A. (2000), “Tacit knowledge, organisational learning and societal institutions: an integrated framework”, Organization Studies, Vol. 21 No. 3, pp. 487-513. Lund, R. and Gjerding, A.N. (1996), “The flexible company, innovation, work organisation and human resource management”, DRUID working paper No. 96-17, Department of Business Studies, Aalborg University, Aalborg. ˚ . (1988), “Innovation as an interactive process: from user-producer interaction to Lundvall, B.-A the national system of innovation”, in Dosi, G., Freeman, C., Nelson, R.R., Silverberg, G. and Soete, L. (Eds), Technology and Economic Theory, Printer Publishers, London. ˚ . (Ed.) (1992), National Systems of Innovation: Towards a Theory of Innovation Lundvall, B.-A and Interactive Learning, Pinter Publishers, London. ˚ . (1997), “Information technology in the learning economy”, Communications and Lundvall, B.-A Strategies, No. 28, pp. 117-92. ˚ . (2002), Innovation, Growth and Social Cohesion: The Danish Model, Edward Lundvall, B.-A Elgar Publishers, London. ˚ . (2003), “Why the new economy is a learning economy”, Economia e Politica Lundvall, B.-A Industriale, No. 117, Rassegna trimestrale diretta da Sergio Vacca`, pp. 173-85. ˚ . and Johnson, B. (1994), “The learning economy”, Journal of Industry Studies, Lundvall, B.-A Vol. 1 No. 2, pp. 23-42. ˚ . and Nielsen, P. (2005), “Innovation, organizational learning and job creation”, Lundvall, B.-A European Journal of Economic and Social Systems, Vol. 18 No. 1, pp. 79-97. ˚ . and Tomlinson, M. (2002), “International benchmarking as a policy learning Lundvall, B.-A tool”, in Rodrigues, M.J. (Ed.), The New Knowledge Economy in Europe, Elgar Publishers, Cheltenham. Ministry of Education (1997), National Kompetenceudvikling, Ministry of Education, Copenhagen. Moss Kanter, R. (1983), The Change Masters, Unwin, London. Nelson, R.R. (Ed.) (1993), National Systems of Innovations: A Comparative Analysis, Oxford University Press, Oxford. Nonaka, I. and Takeuchi, H. (1995), The Knowledge Creating Company, Oxford University Press, Oxford. OECD (2000), Knowledge Management in the Learning Society, OECD, Paris. Polanyi, M. (1958/1978), Personal Knowledge, Routledge & Kegan Paul, London. Polanyi, M. (1966), The Tacit Dimension, Routledge & Kegan Paul, London. Prusak, L. (2001), “Where did knowledge management come from?”, IBM Systems Journal, Vol. 40 No. 4, pp. 1002-7.

Rosenberg, N. (1982), Inside the Black Box: Technology and Economics, Cambridge University Press, Cambridge. Rothwell, R. (1977), “The characteristics of successful innovators and technically progressive firms”, R&D Management, Vol. 7 No. 3, pp. 191-206. Senge, P. (1990), The Fifth Discipline: The Art and Practice of Learning, Doubleday, New York, NY. Verspagen, B. (1992), Uneven Growth between Interdependent Economies, Faculty of Economics and Business Administration, Maastricht. von Hippel, E. (1988), The Sources of Innovation, Oxford University Press, Oxford. von Hippel, E. and Tyre, M. (1995), “How learning by doing is done: problem identification and novel process equipment”, Research Policy, Vol. 24 No. 1, pp. 1-12. Winter, S. (1987), “Knowledge and competence as strategic assets”, in Teece, D. (Ed.), The Competitive Challenge: Strategy for Industrial Innovation and Renewal, Ballinger Publishing Company, Cambridge, MA. Woolcock, M. (1998), “Social capital and economic development: toward a theoretical synthesis and policy framework”, Theory and Society, Vol. 27 No. 2, pp. 151-207. About the authors ˚ ke Lundvall is professor in economics at Department of Business Studies, Aalborg Bengt-A University. His research is organised around innovation systems and learning economies. Lundvall worked as Deputy director at DSTI-OECD 1992-95. He has coordinated major empirical projects on the Danish economy and initiated the worldwide network on innovation research, Globelics. Lundvall has been engaged as expert on innovation policy by several national governments in Europe and given advice to UNCTAD, the World Bank and the EU-commission. Peter Nielsen is an associate professor at Department of Economics, Politics and Public Administration, Aalborg University and a study leader of Master of Labour Market Relations ˚ rhus and Human Resources Management at Aalborg University. He was educated atA University: MA Political Science. He has long experience in empirical research. He has been a member of the DISKO research group since the start and project manager on the National Centre for Labour Market Research (CARMA) at Aalborg University. He is the corresponding author and can be contacted at: [email protected]

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Measuring organisational learning capability among the workforce

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Ricardo Chiva Universitat Jaume I, Castello´n, Spain

Joaquin Alegre Universitat de Vale`ncia, Vale`ncia, Spain, and

Rafael Lapiedra Universitat Jaume I, Castello´n, Spain Abstract Purpose – The present study sets out to propose and validate a measurement scale that aims to capture the organisational capability to learn, based on a comprehensive analysis of the facilitating factors for learning. The organisational learning capability scale consists of 14 items grouped into five dimensions: experimentation, risk taking, interaction with the external environment, dialogue, and participative decision making. Design/methodology/approach – Data were collected from eight Spanish ceramic tile manufacturers. The survey was addressed to shop floor workers. A total of 157 valid questionnaires were obtained, representing a response rate of 61 per cent. Using confirmatory factor analysis, the construct measurement model was tested and the scale was validated. Findings – The results of the study indicate that the operational measure developed here satisfies the criteria for unidimensionality, reliability, and validity. Research limitations/implications – Because of the sample features, final results should be considered with caution. Further research is needed to validate the organisational learning capability scale in other contexts and addressed to other kinds of respondents. However, this study contributes to organisational learning research by providing a valid and reliable operational measure that is expected to help researchers in future theory testing. Practical implications – The proposed measurement scale for organisational learning capability could be implemented as an audit tool. Thus, managers could unveil which organisational learning issues are strong and which are weak. This would provide guidance for improvement. Originality/value – This paper provides a new measurement instrument for organisational learning capability. Keywords Learning organizations, Measurement Paper type Research paper

International Journal of Manpower Vol. 28 No. 3/4, 2007 pp. 224-242 q Emerald Group Publishing Limited 0143-7720 DOI 10.1108/01437720710755227

Introduction The concept of organisational learning has been dealt with extensively in the literature, and generates many academic publications both in specialised journals and those of a more general scope. Organisational learning, generally defined as the process by which organisations learn, has been considered by academics and The authors would like to thank the Bancaja-UJI and Generalitat Valenciana Programmes (Ref. P1-1A2002-18, P1 · 1A2004-05, GV05/082, GV06/082) for their financial support for this research.

practitioners as essential for organisations mainly due to the fast changing environment. Consequently, organisational learning capability, considered as the organisational and managerial characteristics that facilitate the organisational learning process or allow an organisation to learn, plays an essential role in this process. However, despite the importance of the subject, widespread controversy, confusion and theoretical disarray are still in evidence as a consequence of the natural evolutionary process of such a complex dynamic concept. A range of studies on organisational learning (Easterby-Smith et al., 2000; Lyles and Easterby-Smith, 2003) points out these deficiencies and prioritises future research lines, amongst which the development of a valid reliable measurement instrument for organisational learning is given prominence. Lyles and Easterby-Smith (2003, p. 650) affirm that the choice of an appropriate measure of organisational learning remains a debated topic. Several of the authors in their handbook lament the lack of agreement on appropriate measures for organisational learning. As Bapuji et al. (2005, p. 538) state, some measures (Bontis et al., 2002; Tippins and Sohi, 2003) are aimed to capture organisational learning that occurs as a psychosocial process at various levels (Crossan et al., 1999; Huber, 1991). These scales are organised according to each one of the phases of the organisational learning process, in an attempt to determine the existence of these phases within the organisation. Other measures (Goh and Richards, 1997; Hult and Ferrell, 1997, Jerez-Go´mez et al., 2005) have to do with the organisational propensity to learn or determine the organisational learning capability (OLC). These questionnaires are organised according to the main facilitators of organisational learning. They are aimed to determine whether the organisation possesses the characteristics or dimensions (e.g. teamwork, experimentation etc.) that facilitate organisational learning. These scales are developed on the basis of a single perspective or literature, mainly the learning organisation literature (Senge, 1990). However, the study of the facilitating factors for learning has not only been advocated and underlined by this literature, but also by the different perspectives put forward in the organisational learning literature (Brown and Duguid, 1991; Weick and Westley, 1996; Fiol and Lyles, 1985; Hedberg, 1981). Therefore, the development of a new measurement instrument aimed to capture the organisational capability to learn, and that takes into account all the theoretical perspectives and literatures involved in the facilitating factors for organisational learning may justify a re-examination of how the organisational learning construct is measured. The aim of this paper is to propose a measurement scale of organisational learning capability, based on a comprehensive analysis of the facilitating factors for organisational learning, and to describe its development and validation. Next we provide a literature-guided framework, which includes an analysis of the concept of OLC and a discussion of the instruments for measurement. Then, we explain the methodology followed for the development of the measurement instrument and detail the identification of the indicators (items), the design of the measurement scale and finally the data-gathering process in eight organisations in the ceramic tile industry. We then discuss the results, in this case the sociometric properties of the measurement scale, and conclude by outlining the implications of the measurement instrument for organisational learning capability and proposals for future research.

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Theoretical background Organisational learning capability The concept of organisational learning capability (OLC) (Dibella et al., 1996; Goh and Richards, 1997; Hult and Ferrell, 1997; Jerez-Go´mez et al., 2005) seems to stress the importance of the facilitating factors for organisational learning or the organisational propensity to learn. Goh and Richards (1997, p. 577) define it as the organisational and managerial characteristics or factors that facilitate the organisational learning process or allow an organisation to learn. The importance of the factors that facilitate organisational learning has traditionally been outlined by the learning organisation literature, which develops prescriptive models to become a learning organisation. This implies the facilitating factors for organisational learning. Consequently, measures of organisational learning capability have traditionally looked to this literature to determine their dimensions or facilitating factors (Goh and Richards, 1997; Hult and Ferrell, 1997). Dimensions outlined by the OLC scales depend on the specific part of the literature underlined by researchers. For instance, as Hult and Ferrell (1997) focused on Senge’s fifth discipline their dimensions were team orientation, systems orientation, learning orientation and memory orientation. Nevertheless, organisational learning literature has also suggested factors that facilitate the existence of learning. In his review of the facilitating factors for learning, Chiva (2004) took into account authors from both the organisational learning and the learning organisation literatures. Following the same comprehensive approach, we analysed both literatures. Through a synthesis analysis, organisational learning facilitating factors were grouped so that a simplified essential set of dimensions for organisational learning was obtained (Spector, 1992; Gatignon et al., 2002). Five underlying dimensions were arrived at: experimentation, risk taking, interaction with the external environment, dialogue and participative decision making. These dimensions were considered as the most underlined facilitating factors in the literature. In “experimentation”, we have included factors such as support for new ideas, continuous training or workers that want to learn and improve. In “dialogue”, we considered communication, diversity, teamwork, or collaboration. In “participative decision making”, we incorporated delegation, flexible organisational structure, or knowledge of the organisation. Several factors were considered to be implicit in all the five underlying dimensions: commitment to learning, involved leadership or learning as an essential element in the strategy. The five underlying dimensions sum up the facilitating factors for organisational learning proposed by Chiva (2004). Figure 1 shows the conceptual model of organisational learning capability. The figure includes the dimensions of the model and definitions of each one of them. The five conceptual dimensions of organisational learning capability (Figure 1) are described below, together with an explanation of their links with other conceptual categories and with organisational learning capability itself. Experimentation. Experimentation is defined as the degree to which new ideas and suggestions are attended to and dealt with sympathetically. Experimentation is the most heavily supported dimension in the literature of OL (Hedberg, 1981; Nevis et al., 1995; Tannenbaum, 1997; Weick and Westley, 1996; Goh and Richards, 1997; Pedler et al., 1997). Nevis et al. (1995) consider that experimentation involves trying out new ideas, being curious about how things work, or carrying out changes in work

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Figure 1. The conceptual model of organisational learning capability (OLC)

processes. It includes the search for innovative solutions to problems, based on the possible use of distinct methods and procedures. Weick and Westley (1996) explain the importance to organisational learning of small rather than big changes or experiments. Risk taking. Risk taking is understood as the tolerance of ambiguity, uncertainty, and errors. Hedberg (1981) proposes a range of activities to facilitate organisational learning, amongst which is stressed the design of environments that assume risk taking and accept mistakes. Accepting or taking risks involves the possibility of mistakes and failures occurring. Sitkin (1996, p. 541) goes as far as to state that failure is an essential requirement for effective organisational learning, and to this end, examines the advantages and disadvantages of success and errors. If the organisation aims to promote short-term stability and performance, then success is recommended, since it tends to encourage maintenance of the status quo. According to Sitkin (1996, p. 547), the benefits brought about by error are risk tolerance, prompting of attention to problems and the search for solutions, ease of problem recognition and interpretation, and variety in organisational responses. Since the appearance of this work, many authors have underlined the importance of risk taking and accepting mistakes in order for organisations to learn (Popper and Lipshitz, 2000). Interaction with the external environment. We define this dimension as the scope of relationships with the external environment. The external environment of an organisation is defined as factors that are beyond the organisation’s direct control of influence among others. It consists of industrial agents such as competitors, and the economic, social, monetary and political/legal systems. Environmental characteristics play an important role in learning, and their influence on organisational learning has been studied by a number of researchers (Bapuji and Crossan, 2004, p. 407). Relations and connections with the environment are very important, since the organisation attempts to evolve simultaneously with its

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changing environment. Hedberg (1981) considers the environment as the prime mover behind organisational learning. More turbulent environments generate organisations with greater needs and desires to learn (Popper and Lipshitz, 2000). According to Nevis et al. (1995), in recent years researchers have stressed the importance of observing, opening up to and interacting with the environment (e.g. Goh and Richards, 1997). Dialogue. In particular, authors from the social perspective (Brown and Duguid, 1991; Weick and Westley, 1996) highlight the importance of dialogue and communication for organisational learning. Dialogue is defined as a sustained collective inquiry into the processes, assumptions, and certainties that make up everyday experience (Isaacs, 1993, p. 25). Schein (1993, p. 47) considers dialogue as a basic process for building common understanding, in that it allows one to see the hidden meanings of words, first by revealing these hidden meanings in our own communication. The vision of organisational learning as a social construction implies the development of a common understanding, starting from a social base and relationships between individuals (Brown and Duguid, 1991, p. 47). Nevis et al. (1995) argue that learning is a function of the spontaneous daily interactions between individuals. The chance to meet people from other areas and groups increases learning. Similarly, Goh and Richards (1997) advocate teamwork and problem solving in groups, with particular emphasis on multi-functional teams. By working in a team, knowledge can be shared and developed amongst its members (Senge, 1990). Easterby-Smith et al. (2000, p. 792) hold that the recent literature is moving away from a vision of an integrating dialogue in which consensus is sought, towards one that seeks pluralism and even conflict. Oswick et al. (2000) claim that authentic dialogue fosters organisational learning because it creates, rather than suppresses, plural perceptions. Individuals or groups with different visions who meet to solve a problem or work together create a dialogic community. Participative decision making. Participative decision making refers to the level of influence employees have in the decision-making process (Cotton et al., 1988). Organisations implement participative decision making to benefit from the motivational effects of increased employee involvement, job satisfaction and organisational commitment (Scott-Ladd and Chan, 2004). Scott-Ladd and Chan (2004) provide evidence to suggest that participative decision making gives better access to information and improves the quality and ownership of decision outcomes. Parnell and Crandall (2000) also maintain that divulging information is a requirement for participative decision making. Subordinates are assumed to be informed in order to participate efficiently. Bapuji and Crossan (2004), Nevis et al. (1995), Goh and Richards (1997), Pedler et al. (1997) or Scott-Ladd and Chan (2004) consider participative decision making as one of the aspects that can facilitate learning. Measurement of organisational learning Studying organisational phenomena usually involves some type or form of measurement. Organisational learning is no exception. There seems to be a serious need for the development of a valid and reliable measurement instrument for organisational learning (Easterby-Smith et al., 2000).

Organisational learning empirical research (Bapuji and Crossan, 2004) has not only used scale measurements and survey-based methods. Much of this empirical research uses qualitative methods (Finger and Bu¨rgin Brand, 1999), but also quantitative methods other than surveys, such as learning and experience curve analysis (Epple et al., 1991). However, the problem of learning and experience curves when applied to measuring organisational learning is that they focus on outputs, not on the learning processes, sources or capabilities. These objective measures contrast with judgemental/opinion measures. Unfortunately, organisational learning does not usually directly generate “hard” numbers with which to make comparisons (Luthans et al., 1995). The learning effects are most often difficult to measure quantitatively. Questionnaire surveys of and interviews with the participants, and/or those external to the organisation such as suppliers or customers, are the most likely sources of information with which to judge organisational learning (Luthans et al., 1995, p. 37). Table I summarises some of the characteristics of the OL scales. Two main perspectives appear to emerge in the development of an organisational learning scale. These perspectives are determined by their aims, which as they are different, their dimensions also differ. The first perspective attempts to determine whether a certain process of organisational learning is being accomplished. When this perspective is adopted, instruments to measure organisational learning are organised according to each of the phases of the organisational learning process in an attempt to determine the existence of these phases within the organisation. Each of these phases is therefore taken as the dimensions of the scale. These scales are based on models such as that of Huber (1991) or Crossan et al. (1999). The studies of Bontis et al. (2002) or Tippins and Sohi (2003) are notable examples of this perspective of OL measurement. The second perspective aims to determine the organisational propensity or capability to learn. When this perspective is adopted, instruments are organised according to the main facilitators of organisational learning. The main facilitators of organisational learning are therefore taken as the dimensions. These measurement scales are mainly based on the learning organisation literature. Pedler et al. (1997), Goh and Richards (1997) and Jerez-Go´mez et al. (2005) are outstanding examples of this measurement perspective. Items from both scales are statements about individual or social behaviours and organisational characteristics; however the two types of scale seem to measure different concepts and therefore their theoretical dimensions are different. The first measures whether the organisational learning process is fluid or is being completed, and the second, whether the organisation has the capability to learn. Furthermore, conclusions obtained from the two kinds of scale differ. As an example, Bontis et al. (2002) suggest that companies were over-investing in individual learning and under-investing in mechanisms to facilitate the flow of learning between levels (individual-group-organisational). In contrast, Goh and Richards (1997) determined that some companies scored high or low in certain characteristics such as “clarity of purpose” or “teamwork”. The measurement scale we propose follows the second perspective as it aims to weight the organisational capability to learn. However, its five dimensions, the main facilitating factors of organisational learning, are retrieved from a comprehensive analysis of both perspectives.

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Table I. Summary of OL scales

Tippins and Sohi (2003)

Templeton et al. (2002)

Bontis et al. (2002)

Jerez-Go´mez et al. (2005)

Watkins and Marsick (2003)

Hult et al. (2000)

Hurley and Hult (1998)

Hult (1998)

Pedler et al. (1997) Tannenbaum (1997)

Hult and Ferrell (1997)

Organisational learning survey scale (21 items): administered to 632 people from four organisations The organisational learning capacity scale (OLC) (23 items): administered to 179 SBUs þ167 SBUs. Emphasis on purchasing Learning company questionnaire (55 items): destined for audits Learning environment survey (69 items): administered to 500 people in seven organisations The organisational learning capacity scale (OLC) (17 items): administered to 179 SBUs þ167 SBUs. Emphasis on sourcing process Learning and development (four items): administered to 9,648 employees from 56 organisations The organisational learning capacity scale (OLC) (17 items): administered to 355 SBUs þ200 SBUs. Emphasis on purchasing Dimensions of the learning organisation questionnaire (DLOQ) (43 items): administered to 191 managers and human resource developers from different organisations Organisational learning scale (16 items): administered to 111 Spanish firms from the chemical industry Strategic learning assessment map (SLAM) (23 items): administered to 480 individuals from 32 organisations Measure for the OL construct (31 items): administered to 119 firms. Emphasis on IT OL: administered to 271 firms (29 items): Emphasis on IT and customers Process

Process

Process

Slater and Narver (1995); Huber (1991)

Huber (1991)

Crossan et al. (1999): 4I framework

The learning organisation

The learning organisation

Capability Capability

The learning organisation

Capability

Individual learning

The learning organisation

Capability Capability

The learning organisation Individual learning

The learning organisation

The learning organisation

Conceptual background

Capability Capability

Capability

Capability

Aim

230

Goh and Richards (1997)

OL measurement instrument

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Methodology Broad agreement exists in the literature on the steps to be followed in the creation of a measurement scale (Churchill, 1979; Spector, 1992): (1) theoretical representation of the concept in such a way as to reflect its defining features; (2) specification of the concept, by breaking it down into the various dimensions or relevant aspects it covers; (3) choice of indicators; and (4) synthesis of the indicators through the elaboration of a weighted index for each of the conceptual dimensions. Development of the OLC measurement scale From the concept of organisational learning capability adopted in our theoretical review, we proceed to the development of a measurement instrument comprising a set of scales that represent theoretical dimensions or latent variables through their items. We understand organisational learning capability (OLC) to consist of the organisational and managerial characteristics that foster the organisational propensity to learn or facilitate the organisational learning process. Five dimensions are proposed to represent the essential factors that determine organisational learning capability: experimentation, risk taking, interaction with the external environment, dialogue and participative decision making. Spector (1992) argues that the content of existing scales may help in the development of a new scale. Accordingly, we selected a brief number of items belonging to other scales that could synthesise the content of each OLC dimension (Figure 1). For example, for experimentation, we reviewed the measurement scales in the literature that exist for this concept and we found that two items from the Isaksen et al. (1999) creative climate measurement scale could adequately represent the experimentation dimension we propose in the theory section. Table II shows the literature source of each item of our proposed OLC measurement scale. The OLC measurement instrument was applied using a seven-point Likert scale, where 1 represented total disagreement and 7, total agreement. A pre-test was administered to four technicians from ALICER (Centre for Innovation and Technology in Ceramic Industrial Design), to assure that the translation into Spanish was fully understandable. Data gathering We tested our OLC measurement scale in eight companies from the Spanish ceramic tile sector. Most of the firms from this sector are considered to be SMEs, as they do not exceed an average of 250 workers. Ceramic tile production is a globalised industry whose features belong to the scale-intensive and to the science-based trajectories of Pavitt’s taxonomy (Alegre et al., 2004). In 2003, the Spanish ceramic tile industry was the world’s biggest exporter and its production represented almost half of EU production (Chamber of Commerce of Valencia, 2004) The fieldwork was carried out from January to April 2004. With the help of ALICER technicians, we selected eight ceramic tile manufacturers that are representative of the two main design strategies in the ceramic tile industry (Chiva, 2004): firms 1 to 4 are design followers while firms 5 to 8 are design innovators. Design is an important

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Dimension

Item

Literature source

Experimentation

V1. People here receive support and encouragement when presenting new ideas V2. Initiative often receives a favourable response here, so people feel encouraged to generate new ideas V3. People are encouraged to take risks in this organisation V4. People here often venture into unknown territory V5. It is part of the work of all staff to collect, bring back, and report information about what is going on outside the company V6. There are systems and procedures for receiving, collating and sharing information from outside the company V7. People are encouraged to interact with the environment: competitors, customers, technological institutes, universities, suppliers etc. V8. Employees are encouraged to communicate V9. There is a free and open communication within my work group V10. Managers facilitate communication V11. Cross-functional teamwork is a common practice here V12. Managers in this organisation frequently involve employees in important decisions V13. Policies are significantly influenced by the view of employees V14. People feel involved in main company decisions

Isaksen et al. (1999)

Risk taking

Interaction with the external environment

Dialogue

Participative decision making Table II. Items composing the OLC scale

Isaksen et al. (1999) Amabile et al. (1996) Isaksen et al. (1999) Pedler et al. (1997) Pedler et al. (1997) Pedler et al. (1997)

Templeton et al. (2002) Amabile et al. (1996) Pedler et al. (1997) Hult and Ferrell (1997) Goh and Richards (1997) Pedler et al. (1997) Pedler et al. (1997)

competitive issue in the ceramic tile industry and is narrowly related to organisational learning. The questionnaire was addressed to the shopfloor operations workers in each firm. We excluded managers and office employees in order to obtain a homogeneous set of respondents expressing their perception about OLC in their organisation. It was agreed with the participating firms that the questionnaire would be answered during working time. Participating firms received a feedback report on the survey. We received a total of 157 valid completed questionnaires. The survey response rate was 61 per cent (see Table III). Both the number of responses and the response rate can be considered satisfactory (Spector, 1992). The response rate at a firm level can also be considered adequate: the maximum response rate is for firm 5 (100 per cent) and the minimum is for firm 7 (42 per cent). Workers were under no obligation to answer the questionnaire. The variation in non-response could be due to a number of reasons such as lack of time because of work pressure or the support given by management to the survey.

FIRM FIRM FIRM FIRM FIRM FIRM FIRM FIRM Total

1 2 3 4 5 6 7 8

Total number of shop floor workers

Total number of respondents

Response rate (%)

50 40 20 20 25 30 35 35 255

35 19 14 11 25 20 15 18 157

70 47 70 55 100 66 42 51 61

Results: sociometric properties of the measurement scale The sociometric properties of the measurement scale were evaluated by following accepted practice in the literature (Anderson and Gerbing, 1988). Specifically, this evaluation included the scale’s dimensionality, reliability, content validity, convergent validity, and discriminant validity (Tippins and Sohi, 2003). By verifying the dimensionality of the scale, the researcher ensures that the factorial structure used to conceive the latent variable is correct. Reliability is an indication of the degree to which a measure is free from random error, and therefore yields consistent results. Finally, verification of validity guarantees that the scale satisfactorily measures what it sets out to measure. Dimensionality Structural equations modelling can be employed to evaluate the dimensionality of measurement scales through confirmatory factor analysis. Such analysis allows the researcher, based on theory, to establish a priori the number of latent variables and the relations between them and the observable variables (Hair et al., 1998). The organisational learning capability (OLC) concept is understood as a second order factor made up of five dimensions (Figure 2): experimentation (exp), risk taking (risk), interaction with the external environment (env), dialogue (dialog), and participative decision making (particip). Figure 2 shows the parameters of the OLC measurement model obtained with the statistical programme EQS 5.7 using maximum likelihood estimators. All the estimated parameters are statistically significant; the factor loadings are high, falling above the minimum recommended values (Hair et al., 1998; Tippins and Sohi, 2003). The proposed dimensionality of OLC is supported by the correct fit of the second order factor model. The fit, in absolute terms, is satisfactory as shown by its indicators: the chi-squared statistic is not significant at the 0.05 level, which means that the null hypothesis of perfect fit cannot be rejected; the root mean squared residual (RMR) is close to 0, while the goodness of fit index (GFI) falls above the recommended minimum of 0.9. The fit in incremental terms is also good: the comparative fit index (CFI) is close to 1; the Bentler-Bonett normed fit index (BBNFI) exceeds the recommended acceptance threshold of 0.9. Finally, the Normed chi squared (NC) falls between 1 and 2, which indicates an excellent parsimonious fit.

Organisational learning capability 233 Table III. Response rates

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Figure 2. OLC measurement model

Reliability Reliability is the ratio of the true score’s variance to the observed variable’s variance. In order to perform a thorough reliability assessment, we use both the Cronbach’s alpha coefficient and the composite reliability to assess each dimension’s reliability (Table IV). The composite reliability values and the Cronbach’s alpha coefficients are satisfactory, all above 0.7 or close to this threshold (Hair et al., 1998; Nunnally, 1978). Our analysis therefore confirms the reliability of the measurement scales for each dimension of the OLC concept.

Validity Content validity. A measurement scale is considered to have content validity if its items are representative of the construct they are proposed to measure, and they are easy to respond to (Bearden et al., 1999, p. 4). Accordingly, the generation of the dimensions and the items that make up the OLC measurement scale is grounded in previous theoretical arguments, scales and empirical research. Moreover, by means of a pre-test addressed to four industry experts (ALICER technicians) we made sure that the items were clear and understandable.

Organisational learning capability 235

Discriminant and convergent validity The convergent validity of a concept implies that the measure being used has a high correlation with other measures that evaluate the same concept (Churchill, 1979, p. 70). Confirmatory factor analysis was used to establish convergent validity by confirming that all scale items loaded significantly on their hypothesised construct factors (Anderson and Gerbing, 1988). Discriminant validity verifies that one construct’s dimensions are different from each other (Gatignon et al., 2002). In this way, we are able to confirm that the scale measures the concept under evaluation, and not other closely connected concepts. The discriminant validity of the OLC dimensions was ascertained by comparing measurement models where the correlation between the constructs was estimated with a model in which the correlation was constrained to 1 (thereby assuming a single-factor structure) (Table IV). The discriminant validity was examined for each pair of constructs at a time (Table V). Results showed that the model where the correlation is not equal to 1 improved the fit for all pairs of constructs, confirming that the two constructs are distinct from each other (Gatignon et al., 2002). Survey results The OLC measurement scale could be used as a diagnostic tool by practitioners and researchers. The set of responses for a particular organisation would allow us to determine its OLC. As the OLC measurement instrument is formed of five dimensions, global results should be obtained through the average score of each dimension. Consequently, it could be possible to find out strengths and weaknesses in each of the OLC dimensions. We present the results of the eight firms in Table VI. Firms 5 and 8 have persistently higher scores for the five dimensions revealing a higher degree of OLC. In contrast, firms 2 and 3 show a consistently lower degree of OLC.

EXP RISK ENV DIALOG PARTICIP

Mean

Standard deviation

Composite reliability

EXP

RISK

ENV

3.69 3.27 3.20 3.87 2.69

1.54 1.31 1.57 1.30 1.40

0.78 0.65 0.76 0.80 0.78

(0.89) 0.488 * 0.561 * 0.505 * 0.533 *

(0.74) 0.480 * 0.344 * 0.481 *

(0.84) 0.470 * 0.593 *

DIALOG

(0.86) 0.541 *

PARTICIP

(0.85)

Notes: All correlation coefficients are statistically significant ( *p , 0.01); Cronbach’s alphas are shown on the diagonal; the correlation coefficients were calculated using the means of the items from each dimension

Table IV. Means, standard deviations, composite reliabilities, Cronbach’s alphas, and correlations between the dimensions of the OLC second order factor model

Table V. Pairwise confirmatory analyses

PARTICIP

DIALOG

ENV

0.60 1 0.64 1 0.60 1 0.62 1

1 2 4 5 8 9 4 5

0.73 11.18 0.82 10.71 13.44 21.24 4.83 13.32 8.49

7.80

9.89

10.45

0.39 0.00 0.93 0.05 0.10 0.01 0.30 0.02

p

0.60 1 0.41 1 0.60 1

f

4 5 8 9 4 5

10.22 15.82 6.18 15.54 3.63 7.11 3.48

9.36

5.60

Risk taking d.f. x2 Dx2

0.04 0.00 0.63 0.07 0.46 0.21

p

0.56 1 0.68 1

13 14 8 9

16.30 22.91 9.44 14.59

5.12

6.61

0.23 0.06 0.30 0.10

Interaction with external environment f d.f. x2 Dx2 p

0.63 1

f

13 14

d.f.

13.39 25.99

12.60

Dialogue x2 Dx2

236

RISK

f

Experimentation d.f. x2 Dx2

0.42 0.02

p

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Experimentation

Total Risk taking

Total Interaction with the external environment

Total Dialogue

Total Participative decision making

Total Organisational learning capability

Firm

Mean

SD

ANOVA significance

1 2 3 4 5 6 7 8

3.54 3.00 2.86 3.05 4.88 3.63 3.73 4.14 3.69 3.33 2.37 2.61 2.73 4.18 3.78 2.93 3.39 3.26 2.56 2.00 2.19 2.88 4.56 3.18 3.73 4.35 3.20 3.56 3.45 3.80 4.16 4.05 3.59 3.62 5.03 3.87 2.37 2.25 1.98 2.55 3.19 2.82 2.58 3.72 2.69 3.06 2.66 2.76 3.18 4.11 3.37 3.34 4.24 3.36

1.15 1.08 0.97 1.12 1.08 1.09 1.03 1.03 1.54 0.94 1.10 1.16 1.04 0.79 1.14 0.84 1.01 1.47 1.16 1.00 1.02 1.06 1.05 1.09 1.04 0.92 1.49 1.17 1.00 1.09 1.07 1.14 1.05 0.99 1.01 1.48 1.06 1.01 0.82 1.05 1.11 1.13 1.07 1.10 1.64 1.07 0.96 0.67 1.05 0.93 1.04 0.89 0.84 1.32

0.000

1 2 3 4 5 6 7 8 1 2 3 4 5 6 7 8 1 2 3 4 5 6 7 8 1 2 3 4 5 6 7 8 1 2 3 4 5 6 7 8

Total Note: Calculations based on the means of each construct

Organisational learning capability 237

0.001

0.000

0.022

0.005

0.000

Table VI. Descriptive statistics and ANOVA

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Additionally, we conducted an analysis of variance (ANOVA) to test whether pertaining to a particular organisation systematically implied a specific OLC. The results of the ANOVA demonstrated significant differences between the means of the different organisations, and also showed that there is less variance between responses within firms than among firms (Table VI). Conclusion The research carried out in this study describes the development and validation of an instrument to measure organisational learning capability. The OLC measurement instrument, which aims to capture the organisational propensity to learn, is based on a comprehensive analysis of the facilitating factors for organisational learning. The facilitators were obtained from the learning organisation and organisational learning literature in an attempt to develop a comprehensive instrument. From this thorough bibliographical review, we posit five dimensions of organisational learning capability: experimentation, risk taking, interaction with the external environment, dialogue and participative decision-making. These dimensions represent an important contribution to the literature on OL, as they are based on an exhaustive literature review, and they have also been statistically validated by the research presented here. This paper also presents a review and categorisation of OL measurement instruments (Table I), which allows us to outline our contribution to the OL literature more clearly. Although other measurement scales that try to capture the organisational propensity to learn have been developed, none of them seems to have determined the conceptual dimensions through a comprehensive theoretical review. Most theoretical frameworks for these measures were based on a single perspective or literature, mainly the learning organisation literature. The OLC measurement scale may prove most useful to practitioners. Metrics must provide key business drivers for decision makers to examine the outcomes of various measured processes and strategies and track the results to guide the company. The OLC measurement scale may be used as a diagnostic tool, a device for a survey-feedback procedure towards organisational development. Our empirical findings have also shown that there is less variance between responses within firms than among them, which gives some evidence of the OLC reliability as a measure to attribute characteristics to the organisation. The diagnostic tool might be related to a dynamic trait theory approach applied to organisations, as some dimensions or traits determine the organisational behaviour concerning learning. However, the OLC measurement scale might itself be a mechanism for facilitating such learning, as OLC dimensions are accessible to change through learning and could represent a useful target for organisational change initiatives. Although the OLC measurement scale is designed to be answered by individuals within organisations, its results will lead to conclusions at organisational level. The use of individual questionnaire responses to impute attributes to the organisations, previously discussed by Bontis et al. (2002), might be considered as a shortcoming, considering the difficulties of obtaining objective assessments of the five dimensions. However, this opinion-based instrument is considered adequate as we are evaluating environmental conditions, which can only be properly assessed by people working within that context. Furthermore, prior studies have demonstrated the high correlations of perceived measures with objective measures (Gatignon et al., 2002).

We administered the questionnaire only to shopfloor workers in the context of Spanish ceramic tile manufacturers, as an employee based survey, in order to obtain a homogeneous set of respondents. This may constitute a limitation as we do not take into account other employees or other stakeholders. While the questionnaires were completed by the internal staff from a single industry, to control for potential industry effects across organisations, the instrument was designed regardless of the respondent features, the industrial sector or the country. Further research is needed to test the OLC measurement scale in other contexts. At the employee level, more research is required to analyse the relationships between OLC and issues concerning employees, such as job satisfaction, emotional intelligence or organisational commitment. At the firm level, future research could also investigate the link between OLC and performance. While linking learning to performance appears to be very important (Lyles and Easterby-Smith, 2003), the operationalisation of the co-alignment between a firm’s business strategy and its OLC is also vital (Vera and Crossan, 2003). References Alegre, J., Lapiedra, R. and Chiva, R. (2004), “Linking operations strategy and product innovation: an empirical study of Spanish ceramic tile producers”, Research Policy, Vol. 33 No. 5, pp. 829-39. Amabile, T., Conti, R., Coon, H., Lazenby, J. and Herron, M. (1996), “Assessing the work environment for creativity”, Academy of Management Journal, Vol. 39 No. 5, pp. 1154-84. Anderson, J.C. and Gerbing, D.W. (1988), “Structural equation modeling in practice: a review and recommended two-step approach”, Psychological Bulletin, Vol. 103 No. 3, pp. 411-23. Bapuji, H. and Crossan, M. (2004), “From raising questions to providing answers: reviewing organizational learning research”, Management Learning, Vol. 35 No. 4, pp. 397-417. Bapuji, H., Crossan, M. and Rouse, M.J. (2005), “Organizational learning, methodological and measurement issues”, Proceedings of the 6th International Conference on Organizational Learning and Knowledge, Trento. Bearden, W.O., Netemeyer, R.G. and Mobley, M.F. (1999), Handbook of Marketing Scales: Multi-Item Measures for Marketing and Customer Behavior Research, Sage, Newbury Park, CA. Bontis, N., Crossan, M.M. and Hulland, J. (2002), “Managing an organizational learning system by aligning stocks and flows”, Journal of Management Studies, Vol. 39 No. 4, pp. 437-69. Brown, J.S. and Duguid, P. (1991), “Organizational learning and communities-of-practice: toward a unified view of working, learning, and innovation”, Organization Science, Vol. 2 No. 1, pp. 40-57. Chamber of Commerce of Valencia (2004), Informe de la nueva economı´a global y su incidencia en los sectores tradicionales de la Comunidad Valenciana, Chamber of Commerce of Valencia, Valencia. Chiva, R. (2004), “The facilitating factors for organizational learning in the ceramic sector”, Human Resource Development International, Vol. 7 No. 2, pp. 233-49. Churchill, G.A. (1979), “A paradigm for developing better measures of marketing constructs”, Journal of Marketing Research, Vol. 17, pp. 64-73. Cotton, J.L., Vollrath, D.A., Foggat, K.L., Lengnick-Hall, M.L. and Jennings, K.R. (1988), “Employee participation: diverse forms and different outcomes”, Academy of Management Review, Vol. 13 No. 1, pp. 8-22.

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Lyles, M.A. and Easterby-Smith, M. (2003), “Organizational learning and knowledge management: agendas for future research”, in Easterby-Smith, M. and Lyles, M.A. (Eds), Handbook of Organizational Learning and Knowledge Management, Blackwell Publishing, Oxford. Nevis, E., DiBella, A.J. and Gould, J.M. (1995), “Understanding organization learning systems”, Sloan Management Review, Vol. 36 No. 2, pp. 73-85. Nunnally, J. (1978), Psychometric Theory, 2nd ed., McGraw-Hill, New York, NY. Oswick, C., Anthony, P., Keenoy, T. and Mangham, I.L. (2000), “A dialogic analysis of organizational learning”, Journal of Management Studies, Vol. 37 No. 6, pp. 887-901. Parnell, J.A. and Crandall, W. (2000), “Rethinking participative decision making: a refinement of the propensity for participative decision making scale”, Personnel Review, Vol. 30 No. 5, pp. 523-35. Pedler, M., Burgoyne, J. and Boydell, T. (1997), The Learning Company: A Strategy for Sustainable Development, McGraw-Hill, Maidenhead. Popper, M. and Lipshitz, R. (2000), “Organizational learning: mechanism, culture and feasibility”, Management Learning, Vol. 31 No. 2, pp. 181-96. Schein, E.H. (1993), “On dialogue, culture, and organizational learning”, Organizational Dynamics, Vol. 22 No. 2, pp. 40-51. Scott-Ladd, B. and Chan, C.C.A. (2004), “Emotional intelligence and participation in decision-making: strategies for promoting organizational learning and change”, Strategic Change, Vol. 13 No. 2, pp. 95-105. Senge, P. (1990), The Fifth Discipline, Doubleday, New York, NY. Sitkin, S.B. (1996), “Learning through failure”, in Cohen, M. and Sproull, L. (Eds), Organizational Learning, Sage, Thousand Oaks, CA. Spector, P.E. (1992), Summated Rating Scale Construction: An Introduction, Sage, Thousand Oaks, CA. Tannenbaum, S.I. (1997), “Enhancing continuous learning: diagnostic findings from multiple companies”, Human Resource Management, Vol. 36, pp. 437-52. Templeton, G.F., Lewis, B.R. and Snyder, C.A. (2002), “Development of a measure for the organizational learning construct”, Journal of Management Information Systems, Vol. 19 No. 2, pp. 175-218. Tippins, M.J. and Sohi, R.S. (2003), “IT competency and firm performance: is organizational learning a missing link?”, Strategic Management Journal, Vol. 24, pp. 745-61. Vera, D. and Crossan, M. (2003), “Organizational learning and knowledge management: toward an integrative framework”, in Easterby-Smith, M. and Lyles, M.A. (Eds), Handbook of Organizational Learning and Knowledge Management, Blackwell Publishing, Oxford. Watkins, K.E. and Marsick, V.J. (2003), “Make learning count! Diagnosing the learning culture in organizations”, Advances in Developing Human Resources, Vol. 5 No. 2. Weick, K.E. and Westley, F. (1996), “Organizational learning: affirming an oxymoron”, in Clegg, S.R., Hardy, C. and Nord, W.R. (Eds), Handbook of Organizational Studies, Sage, London, pp. 440-58. About the authors Ricardo Chiva is an Associate Professor in the Business Administration and Marketing Department of the Universitat Jaume I, Castello´n, Spain, where he teaches subjects related to human resources management. His Doctoral dissertation deals with organisational learning and innovation management in the Spanish ceramic tile industry. His primary areas of research cover organisational learning, innovation and design management, complexity theory and human

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resources management. Ricardo Chiva is the corresponding author and can be contacted at: [email protected] Joaquı´n Alegre is an Assistant Professor in the Department of Management “Juan Jose´ Renau Piqueras” of the University of Valencia, Spain. He has been a visiting researcher at INSEAD, Fontainebleau, France. He received his PhD in Management from the Universitat Jaume I. His research interest focuses on knowledge management and technological innovation from a strategic perspective. Dr Alegre has participated on several research projects dealing with biotechnology firms and with ceramic tile producers. Rafael Lapiedra is an Associate Professor in the Business Administration and Marketing Department of the Universitat Jaume I, Castello´n, Spain. He holds a PhD in Business Administration; his doctoral thesis focused on strategic alliances and information systems. He has worked as a visiting professor at the Universidad Tecnolo´gica Metropolitana of Santiago in Chile and at the London School of Economics and Political Science. His primary areas of research cover strategic alliances, knowledge management and inter-organisational information systems.

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The role of HR actors in knowledge networks

Human resource actors

Nada Zupan and Robert Kasˇe Faculty of Economics, University of Ljubljana, Ljubljana, Slovenia

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Abstract Purpose – The paper aims to examine structural positions of individuals, especially HR actors (line managers and HR specialists) within relational networks for creating and sharing knowledge and to explore implications for designing and implementing HR practices in knowledge-intensive firms (KIF). Design/methodology/approach – This article used exploratory research design conducting a single case study of a KIF. Social network analysis (SNA) – network centrality measures and visualization tools – was used to examine the structural position of individuals. Findings – Line managers who are HR actors are centrally positioned within examined knowledge networks, while the HR specialist is not, which implies that the decentralized approach to HRM in KIF can be effective. Results also show that the more operational (instrumental) the information or knowledge flow is, the denser the knowledge networks. Research limitations/implications – This study provides support for devolution of HRM to the line in KIF. It suggests that HRM could affect the process of knowledge creation and sharing by implementing HR practices through centrally positioned line managers. A limitation of the research is a single case study and observed intensity rather than quality of relations. Practical implications – SNA appears to be an effective tool for mapping relationships in an organization. Centrally positioned HR actors (especially line managers involved in HRM) in knowledge networks are advantageous for HRM effectiveness only if obstacles to their effectiveness are properly managed. HR specialists should relate strongly to these actors to enable successful design and implementation of HR practices. Originality/value – The paper applies SNA to the HRM field, thus expanding the traditional view of HRM into examining the position of HR actors in relational networks and exploring their role in effectively executing HR practices. Keywords Human resource management, Social networks Paper type Research paper

Introduction In a knowledge-based economy, high performing organisations acknowledge people as their most important source of competitive advantage. The resource-based view of the firm (Barney, 1991) has evolved into knowledge-based theory (Grant, 1996; Nonaka and Takeuchi, 1995) and nowadays firms are viewed as knowledge-creating entities, while their capabilities to create, transfer and utilise knowledge have become the most important source of a sustainable competitive advantage (Kogut and Zander, 1996). In order to improve our understanding of the HRM-performance link, the strategic human resource management field brings to the centre of attention the possible effects of HR practices on learning, innovation and intellectual capital (Wright et al., 2001; Boxall and Purcell, 2000). Consistent with this evolution, the notion of social capital has become an interesting concept for the HRM field because it builds particular capabilities for creating and sharing knowledge and thus facilitates the creation of intellectual capital (Nahapiet and

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Ghoshal, 1998). Further, with the social networks through which individuals build their social capital attention has shifted from observing the individual and their particular attributes within an organisation to observing actors embedded within a network of relationships with other actors (Brass, 1995). This approach becomes very useful for analysing the effectiveness of the observed devolution of HRM function to line managers (e.g. Cunningham and Hyman, 1999; Brewster and Larsen, 2000) and the increasing role of line managers in the implementation of HR practices thereby influencing a firm’s performance. However, both line managers and HR specialists have often failed to fulfil their roles in decentralised HRM systems (Hope-Hailey et al., 2005). An interesting question then emerges of whether the effectiveness of HR actors might also be related to the structural position they hold in a firm’s social network. Therefore, it is the purpose of our paper to examine the structural positions of individuals (especially HR actors) within relational networks for creating and sharing knowledge (i.e. knowledge networks) and to explore implications for their role in designing and implementing effective HR practices. The remainder of the paper is organised in four main sections. In the first one we discuss knowledge as a source of competitive advantage, establishing the link between social capital and social networks and finally explaining the role of HRM and HR actors with regard to social networks. In the second section the present study’s research design and methodology are presented in more detail because social network analysis is still relatively new in the HRM field. In the third section we present the results while the last section is devoted to a discussion of our findings, their research and practical implications, and further research possibilities.

Knowledge as a source of competitive advantage Knowledge can provide a sustainable competitive advantage because it generates increasing returns and continuing advantages. Unlike material resources, knowledge increases with its use more than it decreases, on both the sender’s and receiver’s side of sharing – it breeds new ideas and creates new knowledge. Within this line of thought, Davenport and Prusak (2000, p. 5) define knowledge as a “fluid mix of framed experience, values, contextual information, and expert insight that provides a framework for evaluating and incorporating new experiences and information. It originates from and is applied in the minds of knowers. In organizations, it often becomes embedded not only in documents and repositories but also in organisational routines, processes, practices and norms.” This definition is useful for our purpose because it encompasses both aspects of knowledge: as a stock and flow. By knowledge stock we mostly refer to the explicit knowledge encoded in documents, databases or other permanent media (Meso and Smith, 2000, p. 232) that can be viewed as a resource that a company has at its disposal and which can be managed. For building a competitive advantage, another type of knowledge becomes important: tacit (implicit) knowledge which is intangible and entails information that is difficult to express, formalise and thus also to share (Polanyi, 1966). As Haldin-Herrgard (2000, p. 358) put it: Tacit knowledge is internalized in the individual’s body and soul.

Hence, it is tacit knowledge that contributes most significantly to building a competitive advantage because it is difficult to transfer and is the key source of innovation in products, services and processes. The importance of tacit knowledge is also emphasised by Fahey and Prusak (1998) who argued that knowledge cannot only be treated as stock since it mostly exists in the minds of employees and is reflected in their day-to-day practices. As such, it can only be viewed as being in a constant state of flux (i.e. flow). Although the knowledge stock may be important it is in fact the result of past creativity. Therefore, it is crucial that firms observe the flow of knowledge, how knowledge is transferred and shared among the firm members. Especially in knowledge-intensive firms (KIFs) for which knowledge is the most important input (Starbuck, 1992) this process-based approach to knowledge creation, accumulation, dissemination, and mobilisation is becoming a must (Swart and Kinnie, 2003). Continuous enactment of tacit knowledge in novel circumstances as the basis of a KIF’s operation can only be achieved when team members meet to share their knowledge of a given area (Krogh et al., 2000, p. 7). Because knowledge becomes an asset to the organisation only when it is accessible and its value increases with the level of accessibility (Davenport and Prusak, 2000, p. 18), the relationships among organisation members become the key enablers of knowledge creation. Knowledge and social networks Following the above discussion we can infer that to build knowledge as a competitive advantage we thus need an interaction among organisational members and also between members and the environment. In this way organisations bring tacit knowledge into the process of generating ideas and solving problems creatively. It is hence important that organisations establish appropriate formal networks of relationships and support the informal ones in order to enable knowledge creation and sharing. Based on his research into high-tech industries, Bahrami (1996, pp. 64-65) claimed that informal networks are pivotal because the productivity of knowledge-based entities depends on employees’ capabilities, commitments, motivations and relationships. Due to the process dynamics these cannot be programmed around pre-determined roles and positions in a machine-like hierarchy because continuous change typically renders institutional roles and positions somewhat obsolete. Particularly in KIFs, creating an informational environment that helps employees solve increasingly complex and often ambiguous problems considerably contributes to performance (cf. Zack and McKenney, 1995). Frequently, such efforts entail initiatives focusing on the capture and sharing of codified knowledge and reusable work products (Zack, 1999). However, such initiatives often undervalue the tacit knowledge held by employees and the network of relationships that dynamically help to solve problems and create new knowledge. Therefore, improving efficiency and effectiveness in knowledge-intensive work demands more than sophisticated technologies – it requires attending to the often idiosyncratic ways in which people seek out knowledge, learn from and solve problems with other people in organisations (Cross et al., 2001b). It is thus more appropriate if we adopt a social network approach to organisations and describe each company as a set of individuals and a set of relations among them. Individuals can be seen as nodes or actors, whereas relations can be seen as the ties of a

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social or relational network (Wasserman and Faust, 1998). The likelihood of relationships between individuals in a network depends on physical and social distance, and on the opportunity to interact. Building richer, deeper and broader relationships can add social capital to the organisation and the people in it (Nahapiet and Ghoshal, 1998). Social capital can be defined as (Adler and Kwon, 2002): The goodwill available to individuals and groups. Its source lies in the structure and content of the actor’s social relations. Its effects flow from the information, influence and solidarity it makes available to the actor.

Consistently, Cross et al. (2002) claim that people rely very heavily on their network of relationships to find information and solve problems. Thus, one of the most consistent findings in the social science literature is that “who you know” often has a great deal to do with “what you come to know” (Blair-Loy, 2001). The role of HRM in developing social networks As indicated in the previous section, the ultimate success of knowledge creation, sharing and utilisations depends on how organisational members relate to each other through the different steps of the process (Krogh et al., 2000, p. 5). Hence, companies should make relationships among their members a priority in setting up and implementing activities that provide relational support. In line with this thought, we need to consider an alternative approach to the traditional view of HRM. Taking Boxall’s (1996) notion of HR advantage as a cue, we may argue that both appropriate human capital and organisational processes are needed to create a human resource advantage. As we have already pointed out, social capital has an important effect on building human capital and we thus need a shift in HRM towards relationships among individuals, also through their roles and positions within social networks. Lengnick-Hall and Lengnick-Hall (2003) warn that conventional HR practices concentrate on individual-to-individual relationships within a firm and on overcoming barriers to effective interactions across workgroups. The emphasis on traditional HRM is on four forms of relationships: formal, problematic, introductory and internal. In KIFs this is insufficient because it is the relationships among actors in knowledge networks that really contribute to creating a knowledge-based competitive advantage. Further, while organisations invest in architectural designs that promote interaction, and design policies and procedures that stimulate discussions and communication, it is important to keep in mind that social networks involving trust, social links and personal commitment cannot be engineered or mandated; they can only be encouraged by nudges in the right direction. In the workplace, HRM cannot force people to interact and establish relationships but HRM can create the conditions where those interactions are more likely to emerge (Cohen and Prusak, 2001; Cross and Parker, 2004). HRM can contribute to developing an environment conducive to building and nurturing relationships among organisation members. Further, HRM can facilitate the creation of organisational capabilities such as the ability to locate and share knowledge rapidly and respond to market changes. For instance, Collins and Clark (2003) found that a set of specific network-building HR practices was significantly related to the valuable firm resource of top management team social networks. They also found that the set of network-building HR practices led to a better firm performance (measured as both sales growth and stock

performance) through the practices’ effect on the external and internal social networks of top management teams. Their results suggest that future strategic human resource management research should continue to examine employee-based and other firm capabilities that may act as mediating links between HR practices and firm performance. HRM actors in relational networks According to the structuralistic perspective of HRM, the positions of various HR actors (HR manager or specialist, line manager and top manager) within relational networks become important for HRM effectiveness. This notion is consistent with the ongoing discussion about the roles of HR actors in strategically-oriented HRM, especially with the devolution of the responsibility for the implementation of HR practices to line managers (Thornhill and Saunders, 1998; Brewster and Larsen, 2000). While top managers with the support of HR managers (and/or HR specialists) design suitable HR strategy and practices, line managers are increasingly involved in the execution of HR strategies and practices (Larsen and Brewster, 2003). According to Brewster and Larsen (2000) the reasons for the “devolution to the line” lie in reducing HR department costs, in providing a more comprehensive approach to HRM, in placing the responsibility for human resources with those who supervise them directly, in speeding up decision-making, and as an alternative to outsourcing the HRM function. Research evidence shows that in decentralised organisations it makes more sense to assign knowledge-enabling functions (including enhancing social networks) to (line) managers (Davenport and Prusak, 2000, p. 121) because they are better positioned to execute them. Modern organisational designs, informal social structures (e.g. communities of practice) and the increasing role of information and knowledge flows in organisations put line managers in an advantageous position for implementing HR practices. They are well integrated in work processes and, as knowledge sharing facilitators (MacNeil, 2003), they are centrally positioned in relational networks supporting knowledge and information flows. Although centrality involves social liabilities such as an overload of advice activities and time allocation restrictions (Brass and Labianca, 1999), provided that line managers are appropriately supported by HR managers (Whittaker and Marchington, 2003) they can achieve greater efficiency and effectiveness in executing HR practices due to their position in the relational networks. At the same time, HR managers are usually excluded from operational work processes and, more importantly, are usually neither centrally positioned in knowledge networks nor members of communities of practice. If we follow Krogh et al.’s (2000) five steps of enabling knowledge creation, it becomes clear that the roles of the three main groups of HR actors (top management, line management and HR managers or HR specialists) are different in each step. For example, instilling a knowledge vision in the first step of knowledge creation is certainly the prime responsibility of top management. They should also serve as role models and architects of networks and builders of trust in the next step. However, the remaining three steps that include managing conversations, mobilising knowledge activists and creating the right context should be the responsibility of both top and line managers. These relational roles for managers at all levels of an organisation have already been included in many leadership models which emphasise the leader’s role in providing support and encouragement to subordinates, socialising with people to build

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relationships, providing coaching and mentoring (Yukl, 2002). Basically, every manager who takes on leadership responsibilities would automatically need to exhibit relationship-oriented behaviours in order to function effectively. More specifically, with regard to enabling knowledge creation the important task of line managers is to facilitate knowledge sharing. This can be achieved by communicating a positive learning climate, encouraging knowledge sharing among individuals and serving as a link to collective knowledge sharing (MacNeil, 2003). HR specialists should, in Ulrich’s (1997) terminology, provide the following four roles: (1) business partner – recognising and promoting what is important for building a competitive advantage through knowledge; (2) organisational expert – designing systems and tools to analyse and enhance social networks; (3) employee champion – helping employees to build and sustain effective relationships and social networks; and (4) change agent – inducing and supporting the change process stemming from new ideas generated through social relationships. When we look at traditional HR practices, they should also be relationship-oriented in a way that enables knowledge creation, such as job design that includes teamwork and co-operation, socialisation and mentorship, employee training and development activities, designing open communication channels, promoting learning and a knowledge-sharing culture (cf. Brass, 1995). Research design and methodology In order to achieve the aims of the paper our study was designed to provide information about the structural position of individuals in relational networks for creating and sharing knowledge and about the current state of the HRM function including HR strategy, HR practices and the role of various HR actors for an organisation. Due to the introductory stage of research on this topic in the HRM field, an exploratory research design combining qualitative and quantitative approaches was adopted. We decided to conduct a single case study of a medium-sized European KIF. Relying on its know-how, extensive experience and innovations the firm we selected has provided customers with comprehensive telecommunication solutions. These have included implementation of the latest communication technologies, telecommunication networks and technical solutions accompanied by high-quality services like consultancy, planning, engineering, and training, along with the maintenance and quality assurance of telecommunication networks. The rationale for selecting this company is as follows: . its success chiefly depends on knowledge creation and sharing; . it performs various HR practices and is extensively involved in knowledge management activities; and . the size of the firm is small enough for conducting a complete social network analysis.

The firm consists of two distinctive parts: operations and support. In the study, we focused on the value enhancing operations part which is based on standing process-oriented workgroups bringing together individual members from development, marketing and technical implementation working areas. In this study we are interested in finding out what are the positions (in terms of centrality and brokerage) of the various formal designers and implementers of HR practices in relational networks that facilitate the creation, accumulation, dissemination and mobilisation of knowledge. We propose that centrally positioned actors in these networks would have an opportunity to exercise HR practices better if they were appropriately supported by HR specialists and HR systems. Operationally, we refer to relational networks facilitating the creation, accumulation, dissemination and mobilisation of knowledge as knowledge networks. In order to specify these networks we have to define their basic elements – the relationship between two individuals within the network. We turn to the literature studying the dimensions of advice networks (e.g. Cross et al., 2001a) to identify three basic relationships that affect knowledge creation and sharing in an organisation. The first relationship we examine is work-related problem solving: it focuses on the sharing of solutions, advice or references among individuals when they encounter a problematic work-related situation. The item used for gathering sociometric data for building the work-related problem solving network is thus: To which of your colleagues do you usually turn for information, advice or references when you encounter a problematic work-related situation?

The second relation called work-related idea generation focuses on relationships that stimulate the generation of new ideas. The following sociometric item is used for gathering data: In discussion with which of your colleagues are you usually helped by a new idea that is useful to your work?

Finally, the third relation confirmation, validation and support of the idea deals with relationships in which a new idea is confirmed, validated and supported for implementation. The data for the network based on this relationship is gathered as follows: With which of your colleagues do you usually get the confirmation or validation of your ideas and support for their implementation?

The data were gathered in spring 2004 by means of a computer-assisted survey, a review of the firm’s internal documentation, and the semi-structured interviewing of managers and the HR manager (specialist). The interviews were used to depict the current state of the HRM function, HR practices and the firm’s context within which the knowledge networks exist. The survey was applied to gather data for the social network analysis of the knowledge networks within the firm. For consistency and clarity reasons the complete network data gathering focused on the value-enhancing operations part of the firm. Two types of data were essential to perform the analysis: network (sociometric) and grouping (individual) data. Network data were produced with the computer-assisted survey, while the grouping data (i.e. the organisational unit with a formally assigned HRM role) were obtained from the firm’s HR database. The questionnaire included

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items listed earlier in this section. We adopted a list approach for gathering network data that means that the respondents had to report their relationship with the actors listed in the questionnaire. Thus, the boundaries of the three networks (in our case the operations part of the firm, consisting of 58 actors) were defined in advance by the researcher. Respondents were asked to identify relationships with their colleagues by estimating the frequency for each type of relation separately. More specifically, they were asked to estimate how often a given type of relation occurred with a specific colleague. Choices were numerically coded (in parentheses) and limited to: it did not happen (without a numerical value); it seldom happened (1); it happened often (2); and it happened very frequently (3). All three relational networks were defined as directed networks with valued ties based on a three-point scale. Thus, not only the existence but also the direction in which the relation was pointed and its strength were identified. Differently, grouping data were determined on the basis of the firm’s organisational structure and classified every individual into one of organisational units (e.g. marketing, development, operations, and technical support). Further, we marked eight employees who had a formal responsibility for human resources, top and line managers and HR manager (specialist). Both types of data so gathered were used to construct three directed relational networks with 58 individual actors (network nodes). The descriptive social network analysis (SNA), which is used for analysing social relationships in a systematic way, was done using the Pajek (Batagelj and Mrvar, 1998) and UCINET (Borgatti et al., 2002) software packages. In analysing all three knowledge networks these tools enabled us to transform them, determine their density, and visualise and measure the structural position of the individuals (especially HR actors) within these networks. The position of actors in knowledge networks was studied with selected network visualisation techniques (using the Kamada-Kawai energy drawing algorithm in the Pajek software package) and the calculation of descriptive centrality measures. The outgoing degree and betweenness centrality measures (for more, see Freeman, 1979; Wasserman and Faust, 1998; De Nooy et al., 2005) were chosen to analyse the position of the individuals in the examined networks. To illustrate, the outgoing degree centrality of a node is the number of all outgoing ties to other nodes. In our case, an actor who has a high (valued) outgoing degree centrality would disseminate (either as a source or as a broker) specific solutions to the problem, information, support or ideas to a greater number of his colleagues at a greater frequency. In addition, the betweenness of a node measures the proportion of all shortest paths between two nodes in the network that pass through the observed node. In our case, an actor with a high betweenness centrality can control a greater proportion of the flow of solutions to the problem, information and support or ideas in the network. Results According to our semi-structured interviews and a review of the internal documentation, the examined firm perceives human resources as an important source of a competitive advantage. It operates in a highly competitive high-tech industry and its HR strategy is closely related to its market-oriented business strategy emphasising knowledge, teamwork and innovation as key success factors. The firm tries to achieve its aims through the selective employment of highly competent

employees, continuous employee development (extensive training and mentorship programmes), job design with cross-functional teams as its main feature and rewarding appropriate performance. HR practices are both vertically and horizontally integrated thus enabling the company’s relatively highly educated and young employees to achieve superior work performance. Nevertheless, according to the latest survey of internal attitudes the implementation of some practices like socialisation and mentorship, compensation, knowledge transfer and communication has to be further improved, especially because the firm has undergone rapid growth and its current size of over one hundred employees requires some formalisation of procedures. The firm has a decentralised HRM function where managers’ roles in implementing HR practices are emphasised, and the HR manager’s role is focused on providing professional and administrative support to both managers and employees. If we now turn our attention to analysing social networks, we first present the visualisation of examined networks and the structural position of the individuals within them (see Figures 1, 2 and 3). All of the visualisations are static snapshots taken simultaneously. Each of the 58 network nodes is labelled with an identification number, which is the same in all three knowledge networks. Therefore, the positions of the actors in the network for the three relations are comparable. Actors labelled v8, v48, v53, v32, v47, v41, v18 (managers) and v23 (the HR specialist) are all formal HR actors and are responsible for the execution of HR practices. In the visualisations they are coloured white so as to be differentiated from other actors, who are coloured grey. The value of ties (i.e. the strength of the relationship) can also be observed by their thickness and colour (greyscale: light, dark), where higher values are represented by a thicker and darker line. Arrows illustrate information and (tacit) knowledge flows. The

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Figure 1. Work-related problem solving network

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Figure 2. Work-related idea generation network

Figure 3. Confirmation, validation and support of an idea network

actor centrality can be clearly observed in these visualisations. Namely, the more central the actor the more centrally positioned they are in the figure. As can be observed from these visualisations the density of ties is not the same in the examined knowledge networks. Network density is defined as the proportion of the actual number of ties between all actors in a network relative to all possible ties between all actors in the network (Wasserman and Faust, 1998). For the networks presented above the network density measure has the following values: work-related problem solving network (0.14), work-related idea generation network (0.11) and confirmation, validation and support of an idea network (0.09). Based on these values, we could speculate that the more operational (instrumental) the information or knowledge flow the denser the knowledge networks. Further, according to the visualisations we can generally establish that the examined networks are quite centralised and cohesive. However, a distinctive subgraph consisting of actors v7, v4, v24, v26, v54 can be identified in all knowledge networks. Also, in the confirmation, validation and support of idea networks the three actors (v27, v29, v40) are isolated (i.e. disconnected from the network). Otherwise, a core-periphery model could be applied for the observed networks in our case. This means that a group of actors is centrally positioned in the core of the examined knowledge networks in the firm and it thus processes and brokers most of the information and knowledge flows from and to actors on the margin of these networks. A similar observation could be made for HR actors. The four line managers involved in implementing HR practices (v8, v32, v53, v41) seem to be highly centrally positioned in the examined networks, while the HR specialist (v23) can be found on the periphery of all three knowledge networks. The other three managers who hold top management positions and are formally responsible for HRM (v48, v47, v18) are also not centrally positioned and mostly have stronger links among themselves than with other actors in the networks. The exception is actor v48 (a founder of the firm) who is more centrally positioned in the two networks related to idea generation and validation. These observations are supported by calculating the centrality measures presented in Tables I and II. In Table I each column lists ten actors with the highest valued outgoing degree centrality and reports on the mean and standard deviation of the valued outgoing degree centrality for all 58 actors and network centralisation according to this centrality measure for one of three knowledge networks. Table II does the same for the betweenness centrality measure. The identification numbers of HR actors are in bold in both tables. In both Tables actor v8, who is also an HR actor, can be identified as the most centrally positioned actor in the company on both centrality measures within all predefined knowledge networks. Other actors’ centrality ranks are not so straightforward and vary according to the centrality measure and examined knowledge network. Nevertheless, we can confirm our observation from the previous section based on the network visualisation and establish that HR actors v8, v32, v53 and v41 are centrally positioned in the examined knowledge networks. In addition, it can be seen that among the top ten most centrally positioned actors in the knowledge networks there are more HR actors when applying the valued outgoing degree centrality measure than when applying the betweenness measures. This could imply that in the examined networks these HR actors show a greater capacity to build individual social capital through their direct contacts rather than through bridging (i.e.

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Table I. Centrality according to the valued outgoing degree

1 2 3 4 5 6 7 8 9 10 Mean Std. Net. cent. (%)

Work-related problem-solving network Actor Out-degree v8 v53 v32 v41 v21 v19 v42 v20 v52 v58

71.0 54.0 48.0 45.0 42.0 38.0 36.0 34.0 31.0 31.0 19.3 3.8 92.3

Network Work-related idea-generation network Actor Out-degree v8 v21 v5 v53 v32 v4 v48 v19 v41 v33

Confirmation, validation and support of an idea network Actor Out-degree

61.0 45.0 44.0 44.0 35.0 29.0 28.0 27.0 27.0 26.0 15.3 12.2 81.5

v8 v53 v21 v41 v32 v48 v5 v4 v19 v33

60.0 49.0 36.0 35.0 32.0 29.0 27.0 26.0 24.0 23.0 13.1 12.0 83.6

Source: Questionnaire

Network

Rank

Table II. Centrality according to betweenness

1 2 3 4 5 6 7 8 9 10 Mean Std. Net. cent. (%)

Work-related problem-solving network Actor Betw. % Betw. v8 v16 v41 v13 v39 v58 v22 v32 v52 v57

674.5 302.3 289.0 286.1 237.8 225.5 151.6 145.0 96.7 76.3 57.0 111.5

21.1 9.5 9.1 9.0 7.4 7.1 4.7 4.5 3.0 2.4 1.8 3.5 19.7

Work-related idea-generation network Actor Betw. % Betw. v8 v32 v4 v17 v12 v58 v2 v49 v41 v51

962.9 428.8 236.6 231.8 203.8 128.9 115.1 106.5 103.1 77.5 59.9 140.9

30.2 13.4 7.4 7.3 6.4 4.0 3.6 3.3 3.2 2.4 1.9 4.4 28.8

Confirmation, validation and support of an idea network Actor Betw. % Betw. v8 v4 v58 v20 v39 v41 v38 v17 v2 v12

1086.2 253.1 229.4 223.4 197.2 194.4 187.0 157.8 139.9 122.8 63.3 151.0

34.0 7.9 7.2 7.0 6.2 6.1 5.9 4.9 4.4 3.8 2.0 4.7 32.6

Source: Questionnaire

providing an interaction between their contacts). Nevertheless, we have to note that in all the examined networks the line managers as HR actors listed among the ten actors with the highest betweenness centrality (i.e. v8, v41 and v32) account for the majority of bridging in the network.

Discussion, implications and conclusions The purpose of our research was to explore the structural positions (in terms of centrality and brokerage) of the various formal designers and implementers of HR practices in relational networks in a KIF. The results suggest that in a KIF with a decentralised HRM function, line managers are centrally positioned within the examined knowledge networks and therefore have the opportunity to execute HR practices effectively. Namely, through their structural position in the network line managers create stronger individual social capital and their relations with other organisational members enable the better implementation of HR practices. Nevertheless, line managers’ centrality in knowledge networks may also present an obstacle because, given their intensive role in operational activities, they may lack the capacity (especially the time) to implement HR practices. A discussion with the firm’s top management when presenting the results confirmed that the heavy workload and depicted roles of line managers in the knowledge networks do indeed hinder the effective execution of HR practices. The same problem was observed by Cunningham and Hyman (1999) and Renwick (2000) in their own research where line managers were unable to allocate sufficient time for HR issues due to operational pressures. Overall, the positions of HR actors in the three different types of knowledge networks were relatively similar. The possible exception is the central position of the firm’s founder in the idea validation network which may suggest that top managers with strong professional authority are important sponsors for organisational members when they want to pursue new ideas. Therefore, opening formal channels of communication about new ideas to top management could prove to be beneficial for innovativeness, especially in larger organisations where informal access to top management is more difficult. In our study, we examined work-related problem solving and idea generation so it was expected that top managers and especially the HR manager would not be centrally positioned in these networks. However, strong relationships between the HR manager and line managers would be essential because, as discovered in previous research, line managers need support in executing HR practices (Brewster and Larsen, 2000; Harris et al., 2002; Whittaker and Marchington, 2003). In our case, the HR manager had only been employed for six months at the time of our research and it is therefore understandable that her position in the social networks was not very strong. However, evidence from other research suggests that HR managers/specialists are often unable to provide line managers with the support they need (Gennard and Kelly, 1997; Hall and Torrington, 1998). Examining the position of HR managers/specialists in social networks, especially their relations with line managers, could be useful in order to design proper HR support solutions for line managers. Based on our findings it can also be concluded that in the observed knowledge networks, a higher density of relationships in problem solving exists compared to the idea generating and validation networks. Because the last two networks are actually more relevant for knowledge creation and innovation and thus could contribute more to building a sustainable competitive advantage, this would imply that firms should target HR practices at shaping those knowledge networks. An important theoretical implication of our study for future research is the importance of not only formal but also de facto relational networks, which include informal relations and may expose the real centres of HR practices and influence as

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well as possible obstacles to effective HR implementation due to the time consuming roles in relational networks. This information would then be very important in order to design HR strategy and practices in such a way that would allow for their effective implementation. It also implies that social network analysis should be used more extensively in the strategic HRM field when trying to shed light on the HRM-firm performance link. From an HR practitioner’s viewpoint, our research confirms that social network analysis is a useful tool for mapping relationships in knowledge networks. Exploring the structural positions of HR actors provides useful information for defining an HR actor’s roles. For the firm in our study, the results clearly show that one individual actor (actor v8) has the most central position in all of the observed knowledge networks and the intensity of their relationships with others may present an obstacle to their effectiveness. Therefore, based on our study results the company implemented a new work organisation so that he was freed of certain operational duties and could focus more on relationships within networks. Also, in acknowledging the importance of centrally positioned HR actors for HRM effectiveness while maintaining their operational efficiency, a new training programme was introduced to improve the line managers’ competencies for executing HR practices. In our study we applied social network analysis to the HRM field, thus expanding the traditional view of HRM so as to examine the position of HR actors in relational networks and to explore the HRM role in shaping these relations. By using this methodology we were able to explore HR actors’ structural position in de facto knowledge networks and draw some implications for improving the effectiveness of HR implementation. The limitations of our research findings are mostly associated with it being a single case study of a knowledge-intensive firm and thus generalisations are impossible. Also, SNA was performed using frequency measures that provide limited information on the quality of the relationships. As we focused on individual HR actors’ structural positions within networks, we adopted an egocentric rather than a truly relational approach to SNA. Although the unit of analysis was not a relationship but an individual embedded within a network, we believe this approach was more appropriate for our research purposes. Although in its early stages, this exploratory research suggests several interesting propositions to be tested in the future, such as how does the network centrality of HR actors affect the execution of HR practices, which roles should HR actors take with regard to knowledge creation, transfer and utilisation and, finally, how can a firm shape relational networks and affect knowledge creation and transfer through HR practices. The research topic of relational networks and HRM introduced in this study remains challenging and calls for further, more rigorous and larger-scale research. Apart from verifying the observations in this case study it will have to determine which relational networks are crucial for a company’s performance and which HR practices shape them most effectively. References Adler, P.S. and Kwon, S.W. (2002), “Social capital: prospects for a new concept”, Academy of Management Review, Vol. 27 No. 1, pp. 17-40.

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De Nooy, W., Mrvar, A. and Batagelj, V. (2005), Exploratory Social Network Analysis with Pajek, Cambridge University Press, Cambridge, MA. Fahey, L. and Prusak, L. (1998), “The eleven deadliest sins of knowledge management”, California Management Review, Vol. 40 No. 3, pp. 265-75. Freeman, L.C. (1979), “Centrality in social networks: conceptual clarification”, Social Networks, Vol. 1, pp. 215-39. Gennard, J. and Kelly, J. (1997), “The unimportance of labels: the diffusion of the personnel/HRM function”, Industrial Relations Journal, Vol. 28 No. 1, pp. 27-44. Grant, R.M. (1996), “Toward a knowledge-based theory of the firm”, Strategic Management Journal, Vol. 17, pp. 109-22. Haldin-Herrgard, T. (2000), “Difficulties in diffusion of tacit knowledge in organizations”, Journal of Intellectual Capital, Vol. 1 No. 4, pp. 357-65. Hall, L. and Torrington, D. (1998), “Letting go or holding on – the devolution of operational personnel activities”, Human Resource Management Journal, Vol. 8 No. 1, pp. 41-55. Harris, L., Doughty, D. and Kirk, S. (2002), “The devolution of HR responsibilities: perspectives from the UK’s public sector”, Journal of European Industrial Training, Vol. 26 No. 5, pp. 218-29. Hope-Hailey, V., Farndale, E. and Truss, C. (2005), “The HR department’s role in organizational performance”, Human Resource Management Journal, Vol. 15 No. 3, pp. 49-66. Kogut, B. and Zander, U. (1996), “What firms do? Coordination, identity, and learning”, Organization Science, Vol. 7 No. 5, pp. 502-18. Krogh, G., Ichijo, K. and Nonaka, I. (2000), Enabling Knowledge Creation, Oxford University Press, Oxford. Larsen, H.H. and Brewster, C. (2003), “Line management responsibility for HRM: what’s happening in Europe?”, Employee Relations, Vol. 25 No. 3, pp. 228-44. Lengnick-Hall, M.L. and Lengnick-Hall, C.A. (2003), “HR’s role in building relationship networks”, Academy of Management Executive, Vol. 17 No. 4, pp. 53-66. MacNeil, N. (2003), “Line managers: facilitators of knowledge-sharing in teams”, Employee Relations, Vol. 25 No. 3, pp. 294-307. Meso, P. and Smith, R. (2000), “A resourced-based view of organizational knowledge management systems”, Journal of Knowledge Management, Vol. 4 No. 3, pp. 224-34. Nahapiet, J. and Ghoshal, S. (1998), “Social capital, intellectual capital, and the organizational advantage”, Academy of Management, Vol. 23 No. 2, pp. 242-66. Nonaka, I. and Takeuchi, H. (1995), The Knowledge-Creating Company: How Japanese Companies Create the Dynamics of Innovation, Oxford University Press, Oxford. Polanyi, M. (1966), The Tacit Dimension, Routledge & Paul Kegan, London. Renwick, D. (2000), “HR – line work relations: a review, pilot case and research agenda”, Employee Relations, Vol. 22 No. 2, pp. 179-205. Starbuck, W.H. (1992), “Learning by knowledge-intensive firms”, Journal of Management Studies, Vol. 3 No. 4, pp. 262-75. Swart, J. and Kinnie, N. (2003), “Sharing knowledge in knowledge-intensive firms”, Human Resource Management Journal, Vol. 13 No. 2, pp. 60-75. Thornhill, A. and Saunders, M. (1998), “What if line managers don’t realize they’re responsible for HR?”, Personnel Review, Vol. 27 No. 6, pp. 460-71.

Ulrich, D. (1997), Human Resource Champions: The Next Agenda for Adding Value and Delivering Results, Harvard Business School Press, Boston, MA. Wasserman, S. and Faust, K. (1998), Social Network Analysis: Methods and Applications, Cambridge University Press, Cambridge, MA. Whittaker, S. and Marchington, M. (2003), “Devolving HR responsibility to the line – threat, opportunity or partnership?”, Employee Relations, Vol. 25 No. 3, pp. 245-61. Wright, P.M., Dunford, B.B. and Snell, S.A. (2001), “Human resources and the resource based view of the firm”, Journal of Management, Vol. 27 No. 6, pp. 701-21. Yukl, G. (2002), Leadership in Organizations, 5th ed., Prentice-Hall, Upper Saddle River, NJ. Zack, M.H. (1999), “Managing codified knowledge”, Sloan Management Review, Vol. 40 No. 4, pp. 45-58. Zack, M.H. and McKenney, J.L. (1995), “Social context and interaction in ongoing computer-supported management groups”, Organization Science, Vol. 6 No. 4, pp. 394-422. Corresponding authors Nada Zupan can be contacted at: [email protected]

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Competency management in support of organisational change Maria Vakola, Klas Eric Soderquist and Gregory P. Prastacos

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Athens University of Economics and Business, Athens, Greece Abstract Purpose – Competitive advantage depends largely on the ability to activate and use organisational resources. As a result, the focus in the strategic management, organisational behaviour and human resource management literature has turned to the internal capabilities of organisations including a particular focus on employees’ competencies. This paper seeks to analyse and discuss a forward-looking, dynamic and proactive approach to competency modelling explicitly aligned with strategic business needs and oriented to long-term future success. Design/methodology/approach – This paper is based on a longitudinal research project sponsored by a leading Greek bank, currently undergoing fundamental corporate restructuring. This paper describes how the competency model was developed and how it facilitated strategy implementation and change by supporting communication, employee understanding of business goals, and the incorporation of new behaviours, roles and competencies in operations. Findings – A forward-looking and proactive approach to competency modelling is presented and discussed in the context of a large-scale organisational change. The organisational core competencies required for a business to compete successfully in the banking sector are defined and discussed. The right mix of skills and behaviours that the individuals would need to possess in order to produce and support those core competencies is also analysed and discussed. Originality/value – Traditional approach to competency management, which is analogous to job analysis, focuses on competencies of successful individuals, rather than on competencies that are needed to support an organisation to meet its short- or long-term objectives. It is important to realise that there is a need to shift toward a forward-looking and proactive approach to competency modelling and present a competency methodology that supports this need. Keywords Competences, Change management, Banking Paper type Research paper

International Journal of Manpower Vol. 28 No. 3/4, 2007 pp. 260-275 q Emerald Group Publishing Limited 0143-7720 DOI 10.1108/01437720710755245

Introduction The competency approach to human resource management is based on identifying, defining and measuring individual differences in terms of specific work-related constructs, especially the abilities that are critical to successful job performance. The concept of competency lies at the heart of human resource management, providing a basis for integrating key HR activities such as selection and assessment, performance management, training, development and reward management, thus developing a coherent approach to the management of people in organisations (Lucia and Lepsinger, 1999). The use of competencies in human resource management is not something new, although the approach is still characterised by a certain confusion related to what competencies are and how they should be measured (Shippmann et al., 2000). Difficulties with the operation and implementation of competency management systems are mostly related to the complex and lengthy process required for identifying the appropriate competencies for an organisation and for building the appropriate competency model Athey and Orth (1999). Another issue of concern is that the

competencies defined most often end up as being backward-looking rather than future-oriented with respect to strategy and organisational change (Torrington et al., 2002). Competency models tend to focus on what managers currently do rather than what is needed to perform effectively in the future (Antonacopoulou and Fitzgerald, 1996), something that jeopardises the potential of competencies to act as levers for implementing change (Martone, 2003). The need for a forward-looking and proactive approach to competency modelling, i.e. to the process of identifying and describing job competencies in narrative form for an identifiable group of jobs (Rothwell and Lindholm, 1999), is driven by the increasing pace in strategy development and implementation (Athey and Orth, 1999). In this context, competencies can be used for translating strategy into job-related and individual skills and behaviours that people can understand and therefore implement in support for change. The challenge here is not only to be able to define the organisational core competencies required for a business to compete successfully, but also define the right mix of skills and behaviours that the individuals would need to possess in order to produce and support those core competencies. We present and analyse a novel approach to competency modelling that allows us to explicitly align competencies to goals and strategy, thus actively supporting those changes necessary for implementing change and achieving competitive advantage. Based on a case study from the banking sector, we illustrate the rollout of such future oriented competency framework and discuss how it was used in order to facilitate strategy implementation and support required change. The need for a new perspective on competency modelling Boyatzis (1982) suggested that a job competency is an underlying characteristic of an employee (i.e. motive, trait, skill, aspects of one’s self-image, social role, or a body of knowledge), which results in effective and/or superior performance in a job. More recently, Sparrow (1997), in his review of the use of organisational competencies in personnel selection and assessment, defined competencies as people’s behavioural repertoires, i.e. their sets of behavioural patterns, which are related to work performance and distinguish excellent from average performers. Further, according to Athey and Orth (1999) a job-related competency is a set of observable performance dimensions, including individual knowledge, skills, attitudes, and behaviours, as well as collective team, process, and organisational capabilities that are linked to high performance, and that provide the organisation with sustainable competitive advantage. Thus, we retain from the above that an individual job-related competency is the underlying set of behavioural patterns of an employee related to effective and/or superior work performance, acting both at an individual and collective level (effective/superior performance both in solitary and inter-personal work), and that provide the organisation in which they are implemented and applied with sustainable competitive advantage. Employees’ competencies and the integration of HR policies and practices with business strategies play a central role for sustained competitive advantage (Hendry and Pettigrew, 1986; Barney, 1991; Lado and Wilson, 1994; Kamoche, 1996). The culture of the lifetime employment no longer exists. Rather, we are witnessing a shift from “people as workforce to people as competitive force” (Prastacos et al., 2002, p. 67)

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that identify strategic thinking, innovation, creativity, and business sense as critical requirements for succeeding in almost any kind of job, thus driving the need for defining and developing new competencies (Ulrich, 1997). In this context, it is particularly important to grasp the dynamic nature of individual job-related competencies and recognising the need for connecting competencies with changing business needs (Athey and Orth, 1999). In spite of the abundant concepts, frameworks and management publishing dealing with strategy, recent research shows that one of the most difficult managerial and leadership issues remains the translation of business strategy into the individual competencies needed for implementing and supporting that strategy at the operational level in organisations (Kaplan and Norton, 2005). Most often, employees must be provided with quite prescriptive job descriptions in order to be able to behave in a manner aligned with strategic objectives (Sparrow, 1997). As a result of this prescriptive approach the competencies identified in many competency management projects are oriented toward the skills needed to continue doing what the organisation already does. In that sense, few competency models differ from the traditional approach of selecting and retaining employees who can perform a set of well-defined tasks, usually focusing on technical knowledge and skills (Sappey and Sappey, 1999). In times of frequent change, or in project-oriented environments, however, such a view of competencies seriously limits the organisation’s potential for dynamically adapting to an evolving strategy. From a methodology point-of-view, the most common approach to competency modelling involves images of what job holders do based on static job descriptions and identification of behaviours that distinguish outstanding from adequate performers (Cockerill et al., 1995). Then, the attributes, e.g. high performance competencies, which distinguish outstanding from average job performance, are identified and measured. Finally, statistical analysis of the frequency of these competencies leads to a “model” of competencies demonstrated by outstanding performers. This approach to competency management, which is analogous to job analysis, focuses on competencies of successful individuals rather than on competencies that are needed to support an organisation to meet its short- or long-term objectives (Ledford, 1995). Moreover, the laborious procedures required in order to dig out, analyse, validate and then elaborate on job descriptions and other descriptive data related to the tasks and activities that compose jobs are time consuming and costly, especially in larger organisations (Athey and Orth, 1999). In view of the above, there are a number of issues that need to be addressed in order to advance the approach to competency management if the objective is to find support in competencies for implementing strategy-driven change initiatives. First, there is a need to shift toward a forward-looking and proactive approach to competency modelling. If competency modelling focuses on the analysis of gaps between current high and average performance, it ignores the skills required for long-term future success. As a result, the organisation compensates and rewards behaviours that already from the outset may be obsolete and constitute obstacles to strategy implementation (Antonacopoulou and Fitzgerald, 1996). As business needs are changing, business leaders are recognising the value of employees who are not only highly skilled but, more importantly, can adapt to changes, learn quickly, commit themselves to continuous professional and personal development and communicate

effectively (Rodriguez et al., 2002). Second, the laborious and time-consuming procedures of traditional approaches to competency modelling will be of little use for organisations with rapidly changing structures, processes and performance requirements. Finally, for companies that operate in continuously changing business environments there is a critical need to implement new business strategies quickly and effectively (Hitt et al., 1998; Hamel, 1994). Competencies, if generated from strategy, can be used as powerful communication tools in order to translate business strategy and changes in structure and processes into behavioural terms that people can understand and therefore, implement. Competency management and integration of competencies into HR functions provide HR management with a toolkit for capturing and communicating strategic vision and objectives in clear behavioural terms that can be easily understood and applied (Athey and Orth, 1999). The case study company and its corporate restructuring program The bank has a network of more than 370 branches, located mainly in South Europe as well as in Central and Western Europe, and it employs 6,800 people. Apart from retail and corporate banking the bank has developed, right from its founding more than 100 years ago, a range of other activities such as asset management, leasing, insurance, factoring etc. The bank operates in a market characterised by major changes, such as an impressive increase in mergers, acquisitions and strategic alliances, a significant privatisation of public banks, with corresponding growth of private ones, and the entry of new financial services organisations, characterised by flexibility, and targeting specific market segments with customised products and services. However, despite a brand name that is associated with high competence, reliability and tradition, the bank’s processes and culture have been suffering from inefficiencies, lack of customer focus, “job for life” attitude, resistance to change, inertia and bureaucracy. Needing to better exploit its strengths and break new ground in product innovation and customer service, the bank has launched a corporate-wide restructuring program called “Pegasus”, aiming at major improvements at the strategic and operational levels. The strategic objective of the restructuring program consisted of meeting its customers’ and employees’ expectations through a new corporate identity, improved operations, service orientation, and better HRM systems. The program aimed primarily to change the bank’s culture and corporate identity by adopting a more customer centric organisation. To achieve this, the bank reengineered its customer relationship management programmes, developed a clearer profiling towards well defined market segments, streamlined its organisational structure, and invested in technology in order to improve processes efficiency and productivity. How the competency model was developed Within the context described above, the top management of the bank decided to redefine their HR methods and systems starting from a focus on individual job-related competencies for the bank’s retail network. The competency model should focus not only on behaviours, knowledge and skills necessary, but should also facilitate communication about strategy and articulate how people could expect to be selected, trained, evaluated, and rewarded after implementation of the new strategy. Moreover,

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competencies related to the ability to change, learn and take initiatives should explicitly be integrated. At this point the research team was called in to accompany the competency project using multiple sources of evidence such as annual reports, internal documentation, interviews, archival records and personal observation. Following extensive discussions with the bank representatives, an in-depth literature review of competency management in general and of competency models in particular, and a careful scoping of the project, a number of methodological principles for the development of the competency model were defined. First, the competency framework should take into consideration not only job descriptions, but also best practices and recent trends in the industry (banking), as well as the organisation’s own strategy (Martone, 2003), so as to guarantee a direct and dynamic link between strategy and competencies and the relevance and “survival” of the framework throughout and beyond the implementation of the change program. Second, the competency framework should consist of a set of “generic” competency areas, with each competency area to be composed of a limited number of competencies that would be relevant, to different degrees, for every concerned job position (Lucia and Lepsinger, 1999). The competency areas would guarantee some continuity and account for the path-dependency in the relation between strategy, organisation and competencies, while the detailed competencies would allow for more rapid adaptation and flexibility in the model. Third, for every position in the branch network, the set of required competencies would then develop into a competency profile (Boyatzis, 1982), indicating the detailed job-related competency characteristics, specific for that position in the network. Fourth, in formulating the individual, job-related competency profile, functional as well as behavioural characteristics of the job should be taken into account, referring to both knowledge- and skill-based competencies, as well as behavioural ones that should characterise the job-holder (Woodall and Winstanley, 1998). As a result, the above actions led to the development of the competency framework which unfolded through three distinctive but parallel processes of identifying and elaborating competencies based on: (1) Banking core capabilities. (2) Banking industry trends. (3) The bank’s own competitive strategy. In terms of banking core capabilities Leichfuss and Mattern (1996) present a comprehensive study identifying five capabilities that differentiate the best banks from average or low performers in the sector: a strong corporate leadership; a highly professional marketing approach; a differentiated and efficient distribution system; lean, efficient, automated processes; and, finally, a credit policy that covers risk and ensures adequate decision criteria and rating capability. As a starting point for the process of defining competency areas for the model, the research team held two focus groups consisting of human resources experts and banking experts in order to generate individual job-related competencies from these five core competencies of leading banks. In terms of banking industry trends, we reviewed relevant annual reports, research papers, industry and consulting reports[1], as well as selected press and journal articles

in order to identify important current and future trends in the international and local banking sector. Supplementary to this documentary analysis, interviews were conducted with sector experts and bank executives, and new focus groups were held with the same participants as in the first phase. Finally, in order to develop more specific competencies for the particular case study bank, we conducted an in-depth analysis of the bank’s strategy, particularly in view of the corporate restructuring program. This analysis relied on extensive interviews with the president and CEO, the vice presidents of the bank, the directors of retail and corporate operations, and other top managers, as well as an analysis of the bank’s internal documents. Having generated an important number of competencies from these three independent but parallel phases of analysis, the research team synthesised and grouped these competencies as illustrated in Figure 1. There was a certain overlap that had to be eliminated, and before formulating the final propositional competency framework a final workshop was held with the bank’s HRM team. Using the methodology of Tett et al. (2000), this prepositional framework was further examined and refined by a panel of experts on human resource management and banking. We asked the panel to review, specify if necessary further synthesise the initially selected competencies, and finally validate the formulated competencies from an implementation perspective. Leaning heavily on the input from the panel we elaborated a competency model with five major distinctive competency areas (Table I). The explicit requirement that the competency framework should be used transversally for all jobholders in the bank’s branches was also integrated in the final elaboration of the framework. Each competency area contained between three and four competencies adding up to a total of 17 competencies in the five areas that, in turn, were specified in a total of 65

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Figure 1. The process of grouping synthesising the competencies generated

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Competency area

Definition

Interpersonal excellence

General definition: develops/maintains networks both within the organisation and with clients; focuses on providing excellent customer service; demonstrates communication co-operation abilities Some examples: identifies/makes use of events for developing external networks; maintains regular two-way communication; maintains a problem solving attitude General definition: demonstrates competencies of planning, organising, co-ordinating monitoring of bank resources, processes operations Some examples: pays attention to details; accurately estimates resources to achieve plans; avoids negative impact of own actions on others; accurately prioritises key tasks; regularly reviews progress of tasks General definition: ability/willingness to make decisions, based on profound knowledge of business environment, strategic needs, bank’s processes, products services Some examples: stays up to date on products service information; has extensive knowledge of product or service features internally and externally; identifies new opportunities for business General definition: demonstrates competencies in organising promoting sales based on market awareness, customer relationship management Some examples: relates benefits product features to the customer; sales objections, is skilled in closing sales; exceeds customer’s expectations; has a history of repeat business General definition: demonstrates competencies of planning, guiding developing human resources Some examples: uses knowledge of individual’s strengths, interests, development needs to delegate tasks; provides accurate regular feedback; identifies where support is needed, provides it

266 Project operations management

Business sense decision making

Sales management

People management

Table I. Final competency areas

profiles, defining what is understood by each competency, both from a responsibility and a behavioural perspective. After the elaboration of the full competency model it was of utmost importance to validate it with data coming from jobholders. Thus, the research team conducted 60 interviews with branch employees representing all jobs covered by the competency model. The analysis of these interviews confirmed the above full model (five areas, 17 competencies and 65 profiles), and, most importantly, provided important data in order to fine-tune the behavioural repertoires for each competency. This data allowed the match of each task with functional and behavioural competencies that ensure effective performance in each job at all levels. Two examples are illustrated in Table II.

Competency area: interpersonal excellence Competency 1: Interpersonal communication Competency profile for branch manager Functional

Behavioural

Competency area: sales management Competency 3: sales negotiating competencies Competency profile for account officer

Use of knowledge experience in order to attract new customers, maintain existing ones Use all the available customer information, fully understands customers’ profile needs Answers all the questions to the best of his/her knowledge, facilitates an informed decision making Takes care of all the issues involved in the selling process Creates a climate of trust; can cope with the Creates a climate of trust; shows unexpected; deals with conflicts; appreciates patience, effectively deals with difficult customers; continuously updates diversity his/her knowledge in order to negotiate effectively Development of public relations; Responsibility of bank’s promotion in the local market Develops collaboration with colleagues, subordinates, ensures timely accurate information knowledge sharing Uses appropriate communication channels

At this stage, the final validated model was successively introduced in order to define, communicate and implement new jobs in the pilot branches where the change programme was successively rolled out. This allowed us to focus our study on how the competency system could support strategy implementation and change. The use of competencies for translating strategy into action and for supporting organisational change The competency management project was time-paced with the rollout of the corporate-wide restructuring project. This gave us a unique opportunity to observe and analyse, through participant observation and interviews with executives and branch employees, how the competency framework actively could translate the strategy behind the transformation into actions at the level of individual job holders, and how it supported change. We identified six particular areas where this happened, as detailed below. Communication of strategic changes The reality of the transformation program was that strategic initiatives were deployed in a top-down manner. In this process of implementing the new strategy, the central HR department used competencies in order to communicate strategic changes and their implications for operations. More specifically, in their effort of translating the emphasis on customer service, the development of new distribution channels, and the new values, behaviours and roles attached to these changes, the HR managers expressed these desired objectives in the language of both functional and behavioural competency profiles. This process was facilitated through workshops including all levels of employees where specific methods such as role-play and team-building

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Table II. Examples of competency areas, competencies competency profiles for two jobs

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exercises were employed in order to explain how competencies will help the organisation meet its goals. Improvement of employees’ understanding of how to reach goals One of the major causes of failure of large-scale organisational change efforts is poor communication (Kotter, 1996). As a result, employees often have difficulties in making sense of the necessity for change, in comprehending how their own operational reality will be affected, and, above all, in understanding their own critical role as contributors to the desired change. Competencies proved to be a powerful tool that enabled HR managers and change agents to communicate change objectives and the management expectations regarding new ways of working, leading to a significantly improved employee understanding of the desirable actions and behaviours for reaching the goals set, compared to previous experiences with other major changes in the bank. A large majority of the interviewed branch employees pointed out that competencies clarify where the bank wants to go and how it should operate in order to successfully reach these strategic destinations. An account officer with long experience from the branch network said: Competencies will help people understand the bank’s vision. Many times in the past we heard about changes in what the bank will do in the future but until now, it was never very clear how such changes would affect us or what we would need to do in order to follow these plans.

Through the competencies framework, with well defined and expressed competencies anchored in strategy, jobholders felt they gained a better understanding of what was expected, what was rewarded, what was desirable and what behaviours and abilities would be needed in the short-, mid- and more long-run. Improvement of feedback from branches to headquarters Competencies contributed to improving the feedback from the branches to the headquarters of the bank. A series of assessment centres building on the competency framework were run with the objective of preparing employees for new duties related to the corporate transformation program. Through these assessment centres and through the interviews conducted by the research team in parallel, senior management became aware of gaps in competencies that could transform into serious obstacles in the process of changing the organisation to implement the new strategy. Hence, the matching of the competency framework to the reality of existing competency profiles directly assessed triggered a need for immediate action in order to ensure training and development aligned with the change objectives and the new strategic orientation. Results from assessment centres showed that in general branch employees needed significant development of behavioural competencies related to interpersonal collaboration and adaptability, in other words defined competencies such as communication, flexibility, dealing with the unexpected, driving initiatives, and handling of conflicts. A senior branch officer expressed the following opinion: This bank employs people with profound knowledge of the banking system and very capable of using the bank’s processes in an effective way. The problem is that some of us don’t know how to communicate with the customer and are not very good at finding out their real needs.

The competencies related to interpersonal excellence are essential for the customer-focused orientation that the bank aims at developing. Through the implementation of the competency model, management got feedback early enough in the change process, allowing for corrective action towards the reinforcement and development of these competencies.

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Integration of new behaviours in operations At the level of day-to-day activities, the success of any transformation program depends on the extent to which new behaviours and practices are incorporated into employees’ daily routines, i.e. integrated in operational routines. One means for achieving this is training of employees combined with employee assessment focused on the achievement of those new behaviours and practices. After the identification of gaps in competencies discussed above, the HR department launched training sessions focusing not only on technical skills, as they used to do in the past, but also on competencies such as communication, teamwork and sales management and procedures. The identified competencies gap guided the development of new training seminars devoted to change management, team building and communication. These training sessions supported the change process not only in terms of acquisition of new knowledge and skills directly applicable in daily activities, but also in terms of allowing for the exchange of opinions and ideas to take place. “This has opened up a new dimension in my job”, said a branch sales officer. “I have been able to share both new ideas and some worries with colleagues doing the same job in other branches” she continued. The competency framework was used in a number of training-related activities including training needs analysis, training content update, selection and preparation of trainers, definition of pedagogical approaches, training evaluation and link of training results with other HR processes, in particular performance appraisal and evaluations. Also, it was decided to incorporate competencies into all HRM processes, starting from training processes and then linking assessment, hiring and ultimately career development to the competency system. The initial experiences from competency-based training and assessment turned out positively. A branch employee in the pilot group expressed her experience in the following way:

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To me, the competency framework provides a fixed point that is essential in order to develop new routines and practices required in our new roles.

The notion of fixed points was perceived as key to successful incorporation of competencies into daily routines. Many interviewees said that they found in the competency model a reference framework that could serve as a focal point and rulebook for initiatives and decision making in the frontline of customer interaction and day-to-day decision making. Although this re-engineering phase where all the tools and processes were re-designed was considered as a success, it will take time to root out the inefficiencies that were identified in the HR processes before training processes stop suffering from inefficiencies. As the personnel director noted: There is a long way to go before fully integrating competencies into all the HRM processes. We have just started to realise how powerful competencies can be, especially in times of change.

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Enhancement of employee participation in change implementation The revamping of the training schemes, driven by the competency management system being adopted, led to an enhancement of the participation and involvement of branch officers in the change implementation. The central HR department identified and formed a pool of high performing and high potential branch managers to receive particular briefings and then take on the role of coaches in the training of their colleagues. One bank manager commented: Having participated in training sessions about competency-based performance management systems, I feel more confident to explain what is expected from now on to my people. I feel also more confident to convince people to follow this change.

The outcome of this “outsourcing” of training was that branch employees achieved a better and quicker understanding of why and how their roles needed to change compared to earlier change programs. In this way, the competency management project helped HR management to come to terms with dissatisfaction and complaints among newly recruited personnel, personnel that had higher formal qualifications compared to the already existing staff (e.g. in terms of postgraduate studies in business or economics and more specific competencies in sales management and customer care). However, inefficiencies in the existing HRM systems, such as unclear roles and responsibilities, unclear goal setting, poor performance management and lack of career planning had led to a turnover rate as high as 30 per cent among these employees. By involving them in the change planning process from day one, a completely different climate conductive to commitment could be created: the bank believed in their potential of acting as change agents raising not only productivity and employee qualifications, but also their active participation in the reshaping of the bank’s branch office profile. Potentially and in the longer term, competency-based performance management and career planning can further contribute to retain the most qualified employees. Institutionalising changes The competencies that were identified and specified in the competency model defined new standards for performance of all employees in the branches. Therefore, the model urgently needed to be integrated in the performance management system so as to ensure that the competencies would not remain just an ideal but be truly assessed in practice. Through an explicit goal setting procedure, a clarification of expectations, and a transparent evaluation procedure of employee competencies, a strong and comparatively rapid institutionalisation of the desired changes was achieved in the bank. The new performance management system was perceived as a significant improvement compared to the appraisal frameworks and procedures previously in place. One interviewee commented: The previous system was considered as a typical procedure without any real impact on us. Therefore, we didn’t pay much attention to “numbers” coming in every year. In any case, the average performance score was 8.5 out of 10 which means that we didn’t really have to try harder since we were all considered outstanding.

By explicitly identifying competency gaps, the new system provides direct input for defining individualised training initiatives and the system also enables HR and line managers to provide feedback to the employees on how successful they are in meeting the new requirements. A branch office director stated:

Initial results from performance appraisals showed poor customer orientation and poor people management skills. It was about time to realise that being a good banker is not enough. We need to change also in those softer issues in order to stay in business.

The adoption of a competency-based performance management system, although it was still in its early stages of development and implementation at the time of our study, has built the foundation of a more positive climate and expectations for the future, and, as an HR officer commented, has provided a “strong motor” in order to keep up the momentum in the transition phase. A road-map for implementation and support in strategy implementation and change Competency management has been around for a number of years. Experiences from extensive use of it in a variety of business environments show that it can provide substantial benefits in terms of aligning HR policies with strategy and rejuvenating HRM processes such as selection, training, assessment and development. However, competency management needs to be used widely: a tendency of focusing on what the organisation already does might lead companies to get stuck with outdated processes and visions of what is strategically important. An inclination towards looking at successful individuals when defining competencies, without an in-depth analysis of which competencies are really valuable for implementing strategy, might lead to poor focus and an over-emphasis on generic behaviours compared to job-related skills. Finally, a tendency of developing too detailed competency frameworks with a long lead-time between competency definition and actual implementation might result in inflexible competency frameworks that end-up in closets instead of being used actively to integrate strategy, change and operations. The competency framework developed within this study was designed to cope with those shortcomings and it proved particularly successful in our case study through the following: First, it anchored the competencies in the new strategy of the bank as well as in core competencies and industry trends, thus integrating a dynamic and forward-looking approach in the competency modelling. Second, it helped validate the emerging model with management and job-holders, as well as with HR and Banking experts, thus ensuring focus on job-related skills integrating strategy and operations. Third, it enabled areas and competencies to remain generic while at the same time allowing for significant variation to take place at the level of the competency profiles. This substantially improved the flexibility of the model. Besides providing a roadmap and design imperatives for developing a competency framework, our study also identified a number of impacts on strategy implementation and change given the competency approach. As we were able to study how the stepwise implementation of the competency model in the bank’s branch network unfolded, we could observe how it played a central role for supporting strategy implementation and reinforcing the roll-out of the change program of which the competency project itself was part. These potentially positive impacts require a set of managerial levers in order to be optimally exploited. In Table III we indicate a number of such levers observed in our study, providing a roadmap for tapping into this potential. The key to use the levers indicated successfully is the development of a management agenda that focuses on the integration between strategic management,

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Potential positive impacts for supporting strategic change Communication of strategic changes

272 Improvement of employees’ understanding of how to reach goals

Improvement of feedback from branches to headquarters

Incorporation of new behaviours

Enhancement of employee participation in change implementation

Table III. Potential positive impacts from competency management on the implementation of strategic change

Institutionalising changes

Levers/actions for tapping into this potential Translate specific defined objectives into competencies, functional behavioural competency profiles Use specific efforts, such as role-play, in order to illustrate how competencies will make the organisation operate in order to meet its goals Accompany the competency framework with clear guidelines methodologies for linking competencies to goals, for clarifying what is desirable, for conducting competency-based evaluation Follow up frequently on employees’ understanding of what is expected Clarify what behaviours/abilities will be needed in the short-, mid-more long-term future Use the competency framework as a basis for conducting assessment centres interviews already at the definition stage of change efforts Search for gaps in competencies that could transform into serious obstacles to change processes Take actions to initiate a bridging of these gaps before rolling out the change effort Develop training programs to follow up on the reinforcement of competencies identified as missing in the gap analysis. As change unfolds, training needs will change Facilitate the exchange of opinions ideas through training. This will support the change process not only in terms of acquisition of new knowledge skills directly applicable in operations, but also in terms of changing behaviours roles Give to a pool of high performing high potential managers the role of coaches in the development of the competencies of their colleagues Tap into this resource of change agents in order to reinforce the communication about the need for change about the true meaning of competencies required in new roles The shift to a competency-based performance management system allows HR management to start institutionalising change through better goal setting, clarification of expectations, reward of behaviours that support new strategic objectives

change management and human resource management. Optimised competency-based management consists of moving in a continuous loop of strategy formulation – competency model adaptation – change implementation – application of competencies for optimised business results – strategy reformulation, and so on. A competency model should never be seen as developed once and forever. If HR managers fail to adapt continuously and reengineer periodically their organisation’s competency

framework, it will at best become outdated and fall into oblivion, and at worst drag the organisation into stagnation and loss of competitive momentum. Despite its contributions discussed above, this study has several limitations related to the timing of the data collection and the project itself. Although, the change project has just finished, some key performance results have not yet been possible to measure. We were involved in the change project from its planning phase and as a result, observations and interviews took place during all project phases. However, the overall evaluation of the project is still going on and more findings are expected to come out from this process. Further study of the tangible effects of the new strategy and organisation is carried out on a continuous basis and will allow us to close the loop between strategy formulation, competency modelling, application of competencies and business results. A natural suite to our research would be to try to model and then quantify, through survey-based research, the exploratory links between job-related competencies, strategy implementation and change that have been developed here. A striking gap exists in the literature on organisational capabilities concerning the contribution of human resource management and job performance to core capabilities that would be important to bridge in order to pursue the quest for optimised competency management in practice. Note 1. EIU and Andersen Consulting, Deloitte Research, Ernst & Young, Group of Ten, Meridien Research. References Antonacopoulou, E. and Fitzgerald, M. (1996), “Reframing competency in management development”, Human Resource Management, Vol. 6, pp. 27-48. Athey, T. and Orth, M. (1999), “Emerging competency methods for the future human resource management”, Human Resource Management, Vol. 38, pp. 215-26. Barney, J. (1991), “Firm resources sustained competitive advantage”, Journal of Management, Vol. 17, pp. 99-121. Boyatzis, R.E. (1982), The Competent Manager: A Model for Effective Performance, John Wiley & Sons, New York, NY. Cockerill, T., Hunt, J. and Schroder, H. (1995), “Managerial competencies: fact or fiction?”, Business Strategy Review, Vol. 6, pp. 1-13. Hamel, G. (1994), “The concept of core competence”, in Hamel, G. and Heene, A. (Eds), Competence-based Competition, Wiley, New York, NY. Hendry, C. and Pettigrew, A. (1986), “The practice of strategic human resource management”, Personnel Review, Vol. 15, pp. 2-8. Hitt, M.A., Keats, B. and DeMarie, S.M. (1998), “Navigating in the new competitive landscape: building strategic flexibility in competitive advantage in the 21st century”, Academy of Management Executive, Vol. 12, pp. 22-42. Kamoche, K. (1996), “Strategic human resource management within a resource capability view of the firm”, Journal of Management Studies, Vol. 33, pp. 213-33. Kaplan, R.S. and Norton, D.P. (2005), “The office of strategy management”, Harvard Business Review, Vol. 83 No. 10, pp. 72-81.

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Kotter, J.P. (1996), Leading Change, Harvard Business School Press, Boston, MA. Lado, A.A. and Wilson, M.C. (1994), “Human resource systems sustained competitive advantage: a competency-based perspective”, Academy of Management Review, Vol. 19, pp. 699-727. Ledford, G. (1995), “Paying for the skills knowledge, competencies of knowledge workers”, Compensation Benefits Review, Vol. 27, pp. 55-62. Leichfuss, R. and Mattern, F. (1996), “Can retail banks learn from each other?”, McKinsey Quarterly Anthology of Personal Financial Services. Lucia, A. and Lepsinger, R. (1999), The Art Science of Competency Models, Jossey-Bass, San Francisco, CA. Martone, D. (2003), “A guide to developing a competency-based performance-management system”, Employee Relations Today, Vol. 30 No. 3, pp. 23-32. Prastacos, G.P., Soderquist, K.E., Spanos, Y. and van Wassenhove, L. (2002), “An integrated framework for managing change in the new competitive landscape”, European Management Journal, Vol. 20, pp. 55-71. Rodriguez, D., Patel, R., Bright, A., Gregory, D. and Gowing, M.K. (2002), “Developing competency models to promote integrated human resource practices”, Human Resource Management, Vol. 41, pp. 309-24. Rothwell, W.J. and Lindholm, J.E. (1999), “Competency identification, modelling assessment in the USA”, International Journal of Training Development, Vol. 3, pp. 90-105. Sappey, R. and Sappey, J. (1999), “Different skills knowledge for different times: training in an Australian retail bank”, Employee Relations, Vol. 27, pp. 577-89. Shippmann, J., Ash, R., Battista, M., Carr, L., Eyde, L., Hesketh, B., Kehoe, J., Pearlman, K., Prien, E. and Sanchez, J.I. (2000), “The practice of competency modelling”, Personnel Psychology, Vol. 53, pp. 703-40. Sparrow, P. (1997), “Organisational competencies: creating a strategic behavioural framework for selection assessment”, in Anderson, N. and Herriot, P. (Eds), International Handbook of Selection and Assessment, Wiley, London. Tett, R., Guterman, H. and Bleier, A. (2000), “Development content validation of a ‘hyperdimensional’ taxonomy of managerial competence”, Human Performance, Vol. 13, pp. 205-51. Torrington, D., Hall, L. and Taylor, S. (2002), Human Resource Management, Prentice Hall, London. Ulrich, D. (1997), Human Resource Champions, Harvard Business School Press, Cambridge, MA. Woodall, J. and Winstanley, D. (1998), Management Development: Strategy Practice, Blackwell, Oxford. Further reading Deloitte Research (2001), Re-inventing Financial Services Business Models, Deloitte Research, New York, NY. Ernst & Young (2000), Small Business Banking, Ernst & Young, London. Group of Ten (2001), “Report on consolidation in the financial sector”, available at: www.imf.org. About the authors Maria Vakola is an organisational psychologist and she is currently working as an assistant professor at the Athens University of Economics and Business. She received her PhD in organisational behaviour/change management from the University of Salford, UK. Her research

interests concentrates on change management, individual and organisational readiness to change. Maria Vakola is the corresponding author and can be contacted at: [email protected] Klas Eric So¨derquist is an Assistant Professor and head of the Management Science Laboratory’s Innovation and Knowledge Management Unit at the Department of Management Science and Technology at Athens University of Economics and Business (AUEB). He holds a DBA from Brunel University, UK. His research concentrates on R&D, innovation management, and knowledge management. Gregory P. Prastacos has more than 20 years of research and consulting experience in strategy and operations, decision-making, and business transformation. Prior to joining the Athens University of Economics and Business he was on the faculty of the Wharton School. He has been a senior partner and Chairman of Deloitte & Touche Conslulting (Greece), and has received the INFORMS first prize on the practice of Management Science (Edelman Award). He has published seven books and numerous articles in journals.

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The effects of joint reward system in new product development

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Tsun Jin Chang Department of Marketing Management, Shih Chien University, Kaohsiung, Taiwan

Shang Pao Yeh Department of International Business Administration, Leader University, Tainan, Taiwan, and

I-Jan Yeh Department of Public Policy and Management, Shih Hsin University, Taipei, Taiwan Abstract Purpose – This study purports to examine the effects of a joint reward system (JRS) under a new product development (NPD) setting by identifying four neglected aspects of JRS that contains a procedural view (participation of reward decision and reward contingent on NPD phases) and a monetary view (risk-free to participate and over-reward incentive) in a conceptual model, and then to empirically test their effects on knowledge sharing and NPD performance. Design/methodology/approach – Using regression analysis, the proposed model was tested on 233 valid respondents (112 in R&D, 50 in marketing, and 71 in manufacturing), including 92 from electronics firms, 87 from semiconductor firms, 29 from biotechnology firms, and 25 from pharmaceutical firms in Taiwan. Findings – The results indicated that risk-free to NPD project members is the most salient aspect of JRS on knowledge sharing and NPD performance. Joint determination of reward allocation was found to be a favorable JRS for only marketing and NPD performance. Rewards contingent on NPD phases have shown conflicting results between R&D and marketing. No relationship was found for over-reward incentive on knowledge sharing and NPD performance. Despite the mixed effects of JRS, knowledge sharing is a strong predictor of NPD performance. Originality/value – This study extends understanding of the complexities of rewards on knowledge sharing and NPD success by decomposing and testing four unique aspects of JRS, which sheds a new light on NPD researches. Keywords Performance related pay, Knowledge sharing, Product development Paper type Research paper

International Journal of Manpower Vol. 28 No. 3/4, 2007 pp. 276-297 q Emerald Group Publishing Limited 0143-7720 DOI 10.1108/01437720710755254

Introduction Over the past years, organizations are scrambling for sustaining efforts to stimulate, facilitate, and utilize their organization-wide knowledge to gain competitive advantages. This trend has especially extended to the new product development (NPD) process and cross-functional NPD teams. To accommodate such trend, a strong role in establishing the foundation of knowledge creation in NPD process is required (Song et al., 2000). While studies (e.g. Nonaka, 1994; Shih et al., 2006) have suggested that knowledge sharing among individuals strengthens knowledge creation and recent

empirical evidence also indicates that knowledge sharing among NPD members can facilitates NPD performance (Chang et al., 2006), identifying effective mechanisms for stimulating knowledge sharing among NPD members across different functional areas has largely remained an untapped source of competitive edge. Organizational rewards can be viewed as one of the mechanisms (Milne, 2001). Although organizational behavior school generally recognizes that organizational reward programs are designed to attract ideal candidates, to retain employees at work, and to motivate employees for higher performance in general (e.g. Ivancevich and Matteson, 2002, p. 197), the complexities of rewards have been elevated as the formation of works are going toward collective efforts in which team-based rewards become salient (e.g. Bartol and Srivastava, 2002; Johnson et al., 2006). The dynamisms of work have been shifting from structural driven to task driven (Milne, 2001, p. 326) under team-based and/or cross-functional management. The importance of team-based rewards are attributed not only to the critical role in determining cross-functional integration among employees and units (Coombs and Gomez-Mejia, 1991; Sarin and Mahajan, 2001) and thus driving group and team performance (Griffin and Hauser, 1996), but also to the significant effects on knowledge sharing (Bartol and Srivastava, 2002; Milne, 2001; Shih et al., 2006) and knowledge exchange (Cabrera et al., 2006). Under such circumstance, the conventional reward mechanisms based on individuals or individuals within unit may not be as effective as in NPD context where cross-functional team efforts are involved and valued (Barclay, 1991). Hence, a reward system that values collective efforts across functions and cooperative behaviors, like joint reward system (JRS), in NPD may be a more effective mechanism (Crittenden, 1992). Prior studies have highlighted the contributions of JRS in NPD setting in many aspects (i.e. Cho and Hahn, 2004; Chimhanzi, 2004; Griffin and Hauser, 1996; Gupta et al., 1986; Sarin and Mahajan, 2001; Xie et al., 2003). Given the critical role that organizational reward program plays in facilitating knowledge sharing among individuals (Bartol and Srivastava, 2002), the effects of JRS on knowledge sharing among cross-functional NPD members and NPD performance has yet to be tested. However, studies incorporating JRS (e.g. Xie et al., 2003; Chimhanzi, 2004) primarily focus on the degree to which project members are joint evaluated and are equally rewarded for joint involvement rather than individual performance, which may not reflect general phenomenon of reward practices in NPD where functional project members are not necessarily rewarded equally in practices (Feldman, 1996). Unlike machines, an individual’s tendency in conducting knowledge sharing is affected not only by managerially controlled variables (e.g. reward and incentive programs), but also by the psychological state (e.g. motivation) of the individual to determine whether to share or to hoard knowledge. Whereas Lawler (1977) had suggested that organizational rewards may affect the attitudes and behavior of employees, the perceived fairness of the rewards may alter the employees’ attitudes and behavior toward contributing their efforts to the organizations (Allen et al., 2003; Milne, 2001). To further understand the effects of JRS under NPD setting, this study identifies four aspects of JRS that contains reward procedure view (participation of reward decision and reward contingent on NPD phases) and monetary view (risk-free to participate and over-reward incentive) in the conceptual model and then empirically tests their effects on knowledge sharing and NPD performance.

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Conceptual model and research hypotheses In light of the above discussion, we proposed and tested a conceptual model (depicted in Figure 1) linking four unique features of JRS as measured by joint determination of reward allocation, reward contingent on NPD phases, risk-free to participants, and over-reward incentives to knowledge sharing and NPD performance. We conceptualize that JRS affects knowledge sharing among NPD functional project members and, as a result, affects NPD performance. In order to acknowledge the assertion that behavior intention is an example of mediator concept (Baron and Kenny, 1986, p. 1180), we also conceptualized that knowledge sharing mediates the relationships between JRS and NPD performance. Joint reward system (JRS) and knowledge sharing Joint reward system is a mechanism designed to reward joint efforts across functions, specifically R&D and marketing that are jointly responsible for the success and failure of a new product, in the NPD process (Gupta et al., 1986). Prior studies illuminating the contributions of JRS suggest that, for example, it integrates cross-functional efforts (Griffin and Hauser, 1996; Sarin and Mahajan, 2001), reduces goal incongruity (Xie et al., 2003) and conflicting behavior (Gupta et al., 1986) among functional members, facilitates interpersonal communication, alleviates conflict levels between marketing and HR (Chimhanzi, 2004), and bridges gaps among sociocultural differences (Cho and Hahn, 2004) in NPD. The conventional practices in measuring JRS are primarily based on the level of collaboration across functions, like R&D and marketing, which assumes all departments are evaluated and are rewarded accordingly in successful commercialization of new product. In practice, however, this may not be a general case in NPD practice (Griffin and Hauser, 1992). Evidence suggests that reward systems based on functional expertise driven, rather than on the integrated efforts of functional collaboration driven, are common practices among firms (Feldman, 1996). Questions then arise as to: (1) Do the NPD members’ participation in reward allocation affect NPD success? (2) How (process-based or outcome-based reward) and when (initiation or implementation phase under NPD) to reward for NPD success?

Figure 1. Conceptual model

(3) Will the associated risk of reward affect NPD success? (4) Is higher reward able to motivate project teams for NPD success? To tackle the above questions, we incorporate four features in reward that are posited to influence NPD: (1) Joint determination of reward allocation. (2) Rewards contingent on NPD phases. (3) Risk-free to participants. (4) Over-reward incentives. The first two variables are extended from the arguments made by Pascarella (1997) and Sarin and Mahajan (2001) regarding how to reward NPD team, whereas the later two variables are focused on the monetary reward practices in NPD. Joint determination of the reward allocation. Joint determination of the reward allocation is the extent to which functional members in a NPD team jointly determine the allocation of rewards from successful launch of a new product. While previous studies have shown that firms employ JRS as an integrative mechanism to gain NPD design-to-market performance (Griffin and Hauser, 1996; Sarin and Mahajan, 2001), researches done by Coombs and Gomez-Mejia (1991) have noted that perhaps the single most important factor contributing to the cross-functional integration is how rewards are allocated across these different functions. Although members in R&D function are recognized for their critical role to the success of high-tech companies, they may not be rewarded properly and thus specially designed reward program is demanded to ease the rapid turnover in many US companies. In light of this, JRS is purported to achieve collaborative efforts rather than individual performance (as discussed earlier). This leads to the fact that some functional members may perceive such practice as inequitable reward and, as such, harmful effect on the overall NPD success may surface (Coombs and Gomez-Mejia, 1991; Griffin and Hauser, 1992, 1996). It is therefore impracticable to reward cross-functional members equally. However, the equity can be achieved if project members can gain voices in the JRS decision by participating jointly to determine the allocation of rewards. Organizational behavior theory has asserted the contributions of employee participation (e.g. Newstrom and Davis, 2002, pp. 187-201). In addition, recent evidences have indicated that participation in the decision-making leads to positive attitudes such as commitment and job satisfaction (Allen et al., 2003), to encourage greater level of efforts in collecting information for the sales force (Judson et al., 2006), and to enhance procedural interaction for information exchange in NPD. While rewards based on team performance are likely to enhance knowledge sharing within teams (Bartol and Srivastava, 2002) and facilitate idea capture schemes for innovation, NPD project members’ commitment and job satisfaction gained by participating in the reward process leads to voluntary or prosocial behavior, i.e. providing information to coworkers (LePine et al., 2002), a form of sharing knowledge, and generates positive job attitudes like integration and involvement. We thus proposed H1:

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H1. The greater the level of JRS as measured by joint determination of the reward allocation, the greater the degree of knowledge sharing among R&D, marketing, and manufacturing of NPD project members. Reward contingent on NPD phases. Sarin and Mahajan (2001) have proposed two reward approaches – process-based rewards and outcome-based rewards. Process-based reward is the degree to which team rewards are tied to procedures, behaviors, or other means of achieving desired outcomes, e.g. completion of certain phases in the development process (Deschamps and Nayak, 1995). Outcome-based reward is the degree to which team rewards are tied to match the bottom-line of the end results. Sarin and Mahajan (2001) suggested that for long and complex projects, outcome-based rewards have a positive effect, as opposed to the negative effect of process-based rewards, on performance. Moreover, Johne (1984) suggests that the NPD process may be simplified into two main phases: initiation and implementation. The distinction between the two phases is that the initiation phase emphasizes the conceptualization of the product, whereas the implementation phase centers on fulfilling that concept (Nakata and Sivakumar, 1996). Process-based reward at initiation phase. Although technological breakthroughs are stimulated by circumstances that encourage risk taking even when rewards are failed to deliver such promise (Sasaki, 1991), the outcome of risk taking may be contingent upon the phases of the NPD process (Johne, 1984; Nakata and Sivakumar, 1996). At the initiation phase, risk taking and intensive knowledge sharing are necessary for generating product ideas before entering the implementation phase where higher costs are incurred. Therefore, considerable concerns for rewarding workers in the initiation phase are placed on sharing diverse knowledge and maximizing the number and range of product development approaches, so that strong and viable ideas can be generated. However, time consumption between the decisions made at the initiation phase and the market outcomes (Hauser et al., 1996) may elevate NPD members’ risk exposure. If any NPD project team is to move organizations forward by taking risk, the result-driven mechanism may not be perceived as a fair approach (Pascarella, 1997), because their willingness of knowledge sharing may be discouraged by the unknown outcome such as losing individual value or raising possible costs (O’Dell and Grayson, 1998). Consequently, the initiation phase is often characterized by loose structure to encourage the free flow of thoughts and actions (Johne, 1984) – activities that are less result driven, namely, the interchange of valuable personal expertise and the incentives for encouraging risk taking to take place. We identify, based on the forgoing discussion, that a process-based reward system is an appropriate mechanism for rewarding NPD teams at the initiation phase, hereby stating H2a as follows: H2a. The greater the level of JRS as measured by process-based rewards at initiation phase of NPD process, the greater the degree of knowledge sharing among R&D, marketing, and manufacturing of NPD project members. Outcome-based reward at implementation phase. Rewards designated for collaboration and cohesion are effective means at the implementation phase where pursuing desired market performance becomes the desired end by encouraging knowledge interchanges during the close scrutiny of decisions and execution of only those that minimally affect schedules and budgets (Nakata and Sivakumar, 1996). The use of outcome control and

social control may unify joint efforts to effectively and quickly achieve the desired goals because individuals would expect that their knowledge sharing behaviors helps others to improve their performance on one hand, and a sense of cooperation and reciprocity is developed particularly when knowledge is shared on the other hand (Bartol and Srivastava, 2002). In addition, social control also directs teams toward common goals, e.g. market performance, by the internalization of values and mutual commitment (Jaworski, 1988). As a consequence, outcome-based rewards are more effective in aligning the project team with goals that are set at the initiation phase. We thus expect that outcome-based reward systems may stimulate knowledge sharing at the implementation phase. We hypothesize that: H2b. The greater the level of JRS as measured by outcome-based rewards at implementation phase of NPD process, the greater the degree of knowledge sharing among R&D, marketing, and manufacturing of NPD project members. Risk-free to participants. Management’s attitudes, such as encouraging risk-taking and/or entrepreneurial character, and tolerating for failures in the NPD process, were found to have positive effects on NPD success (Gupta et al., 1986; Song and Parry, 1993; Coombs and Gomez-Mejia, 1991; Pascarella, 1997; Bartol and Srivastava, 2002) in general and on higher level of cross-functional integration and innovation performance (Song and Parry, 1993) in particular. While risk-taking or being adventurous are positive drives for NPD success, NPD project members are also capable of weighting risks between those of their organization and those of their own (Sarin and Mahajan, 2001). Although they may be willing to share risk with their organizations, they are, however, more eager to minimize their risk exposure or to secure a risk-free position (Sasaki, 1991; Sarin and Mahajan, 2001). Innovation is intrinsically an adventure in which considerable time lag between the efforts made by the NPD teams and the market outcomes. If NPD teams are put into positions to move organizations forward by taking risk, it’s unfair to reward them solely on the basis of results (Pascarella, 1997). Robbins and Finley (1995) had noted that NPD teams would not invest their best efforts to carry out business objectives if they are placed at risk. The agency theory (Bloom and Milkovich, 1998) also suggests that an optimal compensation system is contingent on the need to balance an agent’s (NPD team’s) effort and risk aversion. As a result, we argue that a JRS that facilitates team integration and knowledge sharing should be characterized by minimal risks or risk-free to the NPD project members. H3. The greater the level of JRS as measured by risk-free to participants, the greater the degree of knowledge sharing among R&D, marketing, and manufacturing of NPD project members. Over-reward incentives. Equity theory suggests that individuals are more likely to attain higher performance and team members are more cooperative when they are over-rewarded, whereas under-rewarded team members behave less cooperatively and more selfishly (Harder, 1992). An over-reward incentive program may facilitate cross-functional integration and knowledge sharing in NPD teams due to their reciprocal interdependencies while achieving common goals. Keidel (1985) has characterized basketball as exhibiting

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reciprocal interdependence because it requires more teamwork than do other team sports. When the equity for players who perceived themselves as been under-rewarded are restored, they are more likely to exhibit positive behavior linked to their future rewards and vice versa. Under NPD process, where reciprocal interdependence is needed, over-reward approach may be a mechanism that suppresses selfish behavior and encourage cooperative behavior. Thus, a JRS characterized by over-reward is a positive force that facilitates NPD project members’ knowledge sharing. We propose the following hypothesis accordingly: H4. The greater the level of JRS as measured by over-reward incentives, the greater the degree of knowledge sharing among R&D, marketing, and manufacturing of NPD project members . . . Knowledge sharing and NPD performance The introduction of knowledge creation into NPD generates innovative values for new products (i.e. time to market, innovative features, and creative functions) and gains competitive advantage for the organization (Nonaka, 1994). Unfortunately, the knowledge creation process is ineffective without knowledge sharing via the socialization in the knowledge creation process whereby tacit knowledge can be transformed into explicit knowledge that is valuable to organizations. The repeated interaction and discussions with one another in the knowledge sharing activities could enhance market feedback in the NPD through information flow and synergistic coordination (Nonaka, 1994; Nonaka and Takeuchi, 1995). Evidence also suggests that project teams complete their projects faster when they can access to units that possessed related knowledge (Hansen, 2002), and therefore, a NPD that is typically organized by members across functional units may achieve desired goal more effectively. In addition to the discussions in previous sections indicating that knowledge sharing among workers across various functions in NPD may secure successful outcomes, substantial evidences from literature and field studies in both new product development and knowledge management have lend strong support for a significant and positive association between knowledge sharing and innovation performance (Griffin and Hauser, 1992, 1996; Nonaka, 1994; Song et al., 2000), and more specifically, NPD performance (Chang et al., 2006). Therefore, we advance the following hypothesis: H5. The greater the degree of knowledge sharing among NPD project members, the greater the level of NPD performance among R&D, marketing, and manufacturing of NPD project members. JRS and NPD performance Prior researches have provided sufficient evidence to the effect that JRS affects NPD performance significantly and directly (Song and Parry, 1993; Griffin and Hauser, 1996). JRS was found to contribute to information exchange, as well as cross-functional harmony relationship and involvement, and reduce goal incongruity under NPD context (Xie et al., 2003). While conventional wisdom is inclined to assert that innovation creates added-value and therefore enhance market compatibilities for products, we tend to believe that JRS will strengthen NPD performance:

H6. The greater the level of JRS as measured by joint determination of reward allocation, rewards contingent on NPD phases, risk-free to participants, and over-reward incentives, the greater the level of NPD performance. .

Research design and data collection Sample and procedure We collected data from NPD members who were working in high-tech industries covering the electronics semiconductor, biotechnology, and pharmaceutical industries in southern Taiwan. The survey design follows the procedure employed by Song et al. (2000). Sample firms were contacted through telephone calls to confirm a contact person in each firm, followed by surveys questionnaires that were distributed to employees whose functional expertise was in R&D, marketing, and manufacturing and had been actively engaging in the NPD process during the past three years. A cover letter that explained the purpose and scope of the study with the assurance of confidentiality was attached with each questionnaire. Four hundred questionnaires were distributed and 233 valid questionnaires (112 in R&D personnel, 50 in marketing, and 71 in manufacturing) were collected, generating a response rate of 58.25 per cent. Of the 233 valid questionnaires, 92 were from electronics firms, 87 were from semiconductor firms, 29 were from biotechnology firms, and 25 were from pharmaceutical firms.

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Questionnaire and measures Appendix 1 illustrates the survey items and their associated construct reliabilities. All measures were obtained from a “self-report” questionnaire. Unless otherwise specified, a response scale that was anchored by 1, “strongly disagree,” and 7, “strongly agree.” Because of the exploratory nature of this study and the importance of the individual in knowledge management (Nonaka, 1994; Nonaka and Takeuchi, 1995; O’Dell and Grayson, 1998), we focus on the individual as the unit of analysis. The mean, standard deviation and correlations for each variable are shown in Table I.

1 1. Joint determination of reward allocation 2. Process-based reward at initiation phase 3. Outcome-based reward at implementation phase 4. Risk-free to participants 5. Over-reward incentives 6. Knowledge sharing 7. NPD performance Mean Standard deviation Number of items Notes: *p , 0.05;

p , 0.01; n ¼ 233

**

2

1.00 0.76 *

1.00

0.61 * * 0.63 * * 0.71 * * 0.55 * * 0.66 * * 4.17 1.37 4

0.62 * * 0.58 * * 0.65 * * 0.51 * * 0.56 * * 3.82 1.48 3

3

4

5

6

7

1.00 0.49 * * 0.60 * * 0.47 * * 0.46 * * 4.40 1.41 2

1.00 0.66 * * 0.68 * * 0.71 * * 4.90 1.12 3

1.00 0.52 * * 0.61 * * 4.65 1.05 3

1.00 0.69 * * 4.97 0.99 7

1.00 4.68 1.12 4

Table I. Correlations and descriptive statistics

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Measurement development for JRS and knowledge sharing Because of the lack of valid instruments, self-administered items were undertaken (see appendix A) for JRS and knowledge sharing. The items were carefully examined and justified to determine the theoretical definition that corresponds to the operationalization in NPD practices by six academic professors and two industry experts in NPD. The revised items were then reviewed and revised by ten NPD project members across various industries where sample firms located and were followed by the verification and confirmation that were made by the six academic and industry professionals for final approval. Joint determination of reward allocation was measure by using a four-item scale that assesses the degree to which the allocation of rewards is determined jointly by all the NPD project members as suggested for various elements of JRS (e.g. Coombs and Gomez-Mejia, 1991). The five-item scale of rewards contingent on NPD phase assesses the degree to which process-based rewards and outcome-based rewards were employed at the initiation and the implementation phase of NPD respectively. The items were revised based on Sarin and Mahajan’s (2001) work. The three-item scale of risk-free to participants assessing senior management’s attitude in encouraging risk-taking, supporting entrepreneurial spirits, and tolerating of initial failures, was modified based on the works of Gupta et al. (1986) and Song and Parry (1993). Based on the arguments of Song and Parry (1993), the three-item over-reward incentives scale assesses the degree to which the firms provide proven incentives for innovation regardless of the uncertainty of their outcomes. Knowledge sharing was measured by using a seven-item scale of intent-mechanism matrix that was designed to heed the arguments made by Nonaka (1994) and Bartol and Srivastava (2002). The two by four matrix that contains two knowledge types (explicit and tacit knowledge) and four mechanisms (sharing to contribute to organizational database, sharing through formal interactions within or across teams or units, informal interactions among individuals, and sharing within communities of practice, which are voluntary forums of employees around a topic of interest) for sharing knowledge (Bartol and Srivastava, 2002) were constructed. One cell may not be practical as tacit knowledge is difficult to input into a database. NPD performance. A four-item scale that measured respondents’ perceptions of innovation performance was based on Song et al. (2000). Similar items were significantly correlated with objective financial performance (Joshi and Sharma, 2004). Factor analysis To assess discriminant validity and dimensionality of the JRS construct (i.e. 15 items measuring five dimensions of JRS), a principal-components factor analysis with five-factor varimax rotation was performed. The results of factor analysis were shown, as in Appendix 2, with proving reliabilities (a . 0.89, see Appendix 1). As depicted in Appendix 2, all 15 items were loaded onto the factors as expected for measurement. These five factors accounted for more than 80 per cent of the total variance, indicating the discrininant validity and dimensionality of JRS construct. The same approach was applied to the measurement items of knowledge sharing, and NPD performance. Results of factor analysis were shown in Appendix 3. As expected, all items were loaded onto the factors as designed with 76 percent of the total variance explained with proving reliabilities (a . 0.90, see Appendix 1).

Test of common method/source bias Because we collected the data from the same respondent using the same format of questionnaire in measuring both dependent and independent variables, the contamination of data caused by common method/source variance may be of concern. We used Harman’s single-factor test on the items as suggested by Podsakoff and Organ (1986). Common method/source variance may not be a serious concern as generally expected, yet results from the single-factor analysis indicated that more than one factor emerged with the first factor accounted for 37 percent of total variance, which suggests that common method/source variance is not an issue in this study. Test of mediation Not proposed in the hypotheses, our conceptual model nevertheless suggests that knowledge sharing mediates the relationships between JRS and NPD performance. Given that prior studies have shown the direct effects between JRS and knowledge sharing (Song and Parry, 1993; Griffin and Hauser, 1996) and between knowledge sharing and NPD performance (Chang et al., 2006), we thus tested the mediation effect of knowledge sharing to ensure the appropriation of the proposed model before the hypotheses were tested. Following the mediating test steps by using regression as Baron and Kenny (1986) have suggested, the regression results were shown in Table II. Model 1 shows significant and positive effects of JRS on NPD performance for total sample and for all three functional areas. Model 2 indicates significant and positive effects of knowledge sharing on NPD performance for total sample and for all three functional areas. Model 3 illustrates that while knowledge sharing was controlled, the positive effects of JRS has declined for the total sample (from b ¼ 0:7291, p , 0:001 to b ¼ 0:4652, p , 0:001), as well as R&D (from b ¼ 0:8295, p , 0:001 to b ¼ 0:5832, p , 0:001), marketing (from b ¼ 0:7238, p , 0:05 to b ¼ 0:3028, p , 0.05), and manufacturing (from b ¼ 0:5824, p , 0.001 to b ¼ 0:3462, p , 0.001). The results suggest that partial mediation effects were found for knowledge sharing in the model. Furthermore, the regression results in Table II have also shown strong and significant relationships between JRS and knowledge sharing, JRS and NPD performance, and knowledge sharing and NPD performance across NPD project members from R&D, marketing, and manufacturing functions. Analysis and results The effects of JRS on knowledge sharing (H1-H4) To test hypotheses H1 through H4, multiple regression was used in which all features of JRS were entered into one equation. As shown in Table III, mixed results emerged from regression analysis. Joint determination of reward allocation was found to have no significant relationship with knowledge sharing for the total sample, but significant and positive effect was found in marketing function (b ¼ 0:4190, p , 0.001), suggesting that project members from marketing prefer to participate in joint determination of reward allocation than members form R&D and manufacturing. Hence, H1 was partially supported. No significant relationship was found for process-based reward at initiation phase in predicting knowledge sharing for the total sample. Rather, we found significant and positive effect in R&D function (b ¼ 0:1859, p , 0.01) and significant but unexpected negative effect in marketing function (b ¼ 20:2396, p , 0.05), indicating that project members from R&D, contrary to their

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Table II. Regression results of mediation effects of knowledge sharing Total

0.8784 * * *

**

p , 0.01;

***

Fð2; 109Þ ¼ 88:77 , 0.0001 0.6196 0.6126

MKT

0.7238 *

Fð2; 47Þ ¼ 40:94 , 0.0001 0.6353 0.6198

0.3028 * 0.6066 * * *

Fð1; 48Þ ¼ 70:52 , 0.0001 0.5950 0.5866

0.8372 * * *

Fð1; 48Þ ¼ 45:75 , 0.0001 0.4880 0.4773

p , 0.001

0.5832 * * * 0.4380 * * *

Fð1; 110Þ ¼ 94:26 , 0.0001 0.4615 0.4566

Notes: Values for independent variables are beta coefficients; *p , 0.05;

Dependent variable: NPD performance Model 3 JRS (H6) 0.4652 * * * Knowledge sharing (H5) 0.4507 * * * Model fit F-statistics Fð2; 230Þ ¼ 165:53 Significance , 0.0001 R2 0.5901 Adjusted R 2 0.5865

0.8295 * * * Fð1; 110Þ ¼ 135:89 , 0.0001 0.5526 0.5486

R&D

MFG

0.5824 * * *

Fð2; 68Þ ¼ 40:89 , 0.0001 0.5460 0.5326

0.3462 * * * 0.4249 * * *

Fð1; 69Þ ¼ 56:41 , 0.0001 0.4498 0.4418

0.6413 * * *

Fð1; 69Þ ¼ 49:38 , 0.0001 0.4171 0.4087

286

Dependent variable: NPD performance Model 2 Knowledge sharing 0.7771 * * * Model fit F-statistics Fð1; 231Þ ¼ 205:63 Significance , 0.0001 R2 0.4709 2 Adjusted R 0.4687

Dependent variable: NPD performance Model 1 JRS 0.7291 * * * Model fit F-statistics Fð1; 231Þ ¼ 228:03 Significance , 0.0001 R2 0.4968 Adjusted R 2 0.4946

Independent variables

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Independent variables Joint determination of reward allocation Process-based reward in initiation phase Outcome-based reward in implementation phase Risk-free to participants Over-reward incentives Knowledge sharing 0.3754 * * *

0.4624 * * *

F(5,227) ¼ 44.15 ,0.0001 0.4930 0.4819 **

2 0.0471

0.5039 * * *

0.1362

2 0.2396 *

0.4190 * * *

***

p , 0.001

F(5,44) ¼ 24.33 , 0.0001 0.7171 0.6850

p , 0.01;

F(5,106) ¼ 20.61 ,0.0001 0.4929 0.4690

0.0496

0.0521

0.0810

20.0255

0.1859 * *

20.0623

Knowledge sharing R&D MKT

0.0299

0.0980

Total

Notes: Values for independent variables are beta coefficients; *p , 0.05;

H6

H4

H3

H2b

H2a

H1

Dependent variables

F(5,65) ¼ 10.94 ,0.0001 0.4569 0.4151

20.1306

0.5040 * * *

0.1829

0.0027

0.0623

MFG

F(6,226) ¼ 64.64 , 0.0001 0.6318 0.6221

0.1057 0.3626 * * *

0.2881 * * *

2 0.0377

0.0010

0.2086 * * *

NPD performance Total (H6)

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Table III. Regression results of JRS, knowledge sharing, and NPD performance

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counterparts from marketing function, favor process-based reward at initiation phase to stimulate knowledge sharing. This result lent a partial support for H2a. Risk-free to participants has exhibited strong and positive predictability more than any other features of JRS. Risk-free to participants was significantly and positively associated with knowledge sharing not only in total sample (b ¼ 0:4624, p , 0.001), but also across R&D (b ¼ 0:3754, p , 0.001), marketing (b ¼ 0:5039, p , 0.001), and manufacturing (b ¼ 0:5040, p , 0.001). H3 was supported as a result, showing that NPD project members, regardless of their functional background, favor the utilization of risk-free to participants of JRS in driving knowledge sharing. Outcome-based reward at implementation phase and over-reward incentives did not yield significant results in predicting knowledge sharing neither in the total sample nor in the three functional areas. H2b and H4 were not supported as a consequence. The effects of knowledge sharing and JRS on NPD performance (H5 and H6) Because the interdependence among variables in our conceptual model in which knowledge sharing appears to be both the dependent variable of JRS and the independent variable in predicting NPD performance, we controlled JRS while testing H5 and controlled knowledge sharing while testing H6. As expected, when JRS was properly controlled, the regression results in Table II (model 3) yielded significant and positive relationship between knowledge sharing and NPD performance among project members in total sample (b ¼ 0:4507, p , 0.001) and across R&D (b ¼ 0:4380, p , 0.001), marketing (b ¼ 0:6066, p , 0.001), and manufacturing (b ¼ 0:4249, p , 0.001). H5 was supported accordingly. Also as our prediction, the overall JRS was positively and significantly related to NPD performance in total sample and across all three functional areas (b ¼ 0:4652, p , 0.001 for total sample, b ¼ 0:5832, p , 0.001 for R&D, b ¼ 0:3028, p , 0.05 for marketing, and b ¼ 0:3462, p , 0.001 for manufacturing), when knowledge sharing was properly controlled (see model 3 in Table II). As shown in Table III, the significant effects were mainly contributed by joint determination of reward allocation (b ¼ 0:2086, p , 0.001) and risk-free to participants (b ¼ 0:2881, p , 0.001). H6 was therefore partially supported. Discussions and suggestions This study is purported to develop and examine the effects of five features of joint reward system – joint determination of reward allocation, process-based reward at initiation phase, result-based reward at implementation phase, risk-free to participants, and over-reward incentives – as the antecedents of knowledge sharing that leads to NPD performance among NPD project members across R&D, marketing, and manufacturing functions. The overall regression results produce significant and positive effects for the relationships between JRS and knowledge sharing, JRS and NPD performance, as well as knowledge sharing and NPD performance. Yet mixed results emerged when we broke down the JRS into five features and divided the sample into NPD project members’ functional specialties. JRS as measured by risk-free to participants yielded consistent significant and positive results in predicting not only knowledge sharing among NPD project members across R&D, marketing, and manufacturing, but also NPD performance. Such findings are highly consistent with recent researches on how reward structure influencing the

performance of cross-functional NPD teams (Sarin and Mahajan, 2001) and on the importance of offering reward to invite knowledge sharing without concerning the immediate success or failure to achieve NPD success (Joshi and Sharma, 2004). While NPD members’ attitudes toward risk are a critical issue in NPD (Song and Parry, 1993), the results also support the assertion that NPD project members tend to be risk-aversion (Sasaki, 1991; Sarin and Mahajan, 2001). Therefore, designing JRS to minimize project members’ risk in NPD is likely to stimulate project members’ efforts for NPD performance (Robbins and Finley, 1995). In addition, the sample firms were resided in Taiwan where uncertainty avoidance is relatively higher than the Western’s (Hofstede, 1980) and employees are predisposed to a common practice of knowledge hoarding (Hsu, 2006). Higher level of uncertainly avoidance may lead NPD project members to show higher level of risk aversion. NPD project members’ hording knowledge may be a common practice for protecting themselves in the competitive hi-tech environment. Under such social and cultural exigencies, NPD project members are likely to weight their costs and benefits for contributing their knowledge unless the risk and costs can be minimized and/or waived. Our findings indeed suggest that high-tech firms tend to use the risk-free approach in rewarding NPD project teams plainly because its risk-free and non-cost driven nature. Furthermore, unique aspects of organizational cultures for sharing tacit knowledge in Taiwan may also affect the NPD performance through: . their integrated relationship with organizations; . their openness to the external environment; and . their special approaches to knowledge sharing (Yiu and Lin, 2002). Although we had found positive and significant effect of joint determination of reward allocation only in NPD project members from marketing, rather than from R&D and manufacturing, we also found significant and positive relationship with NPD performance. This finding may reflect a traditional managerial practice, namely, R&D is rewarded for innovation, whereas marketing is rewarded for creating and maintaining markets and satisfied customers. The high-tech firms in Taiwan are renowned for their competitive edges in OEM and ODM instead of product and brand promotions. Such industrial competitive advantages foster a culture that values R&D more than marketing and manufacturing. As such, NPD project members from marketing show higher appreciation than their counterparts from R&D in the participation of reward allocation. However, as discussed in previous sections, for a NPD program to be successful, collective efforts appeared to be a prerequisite. Thus, it is important for the HR practitioners in hi-tech industries to become actively involved in NPD and to promote interpersonal communication across NPD functional members regarding contents and operationalizations of JRS in order to reduce conflicts among them (Chimhanzi, 2004). Reward contingent on NPD phase yielded some surprising and interesting results. The results indicated as expected that process-based reward at initiation phase significantly and positively associated with knowledge sharing in R&D, but a surprising negative and significant result emerged in marketing, whereas outcome-based reward at implementation phase has no any significant effect. A plausible explanation may be that the R&D personnel constantly encounter uncertainties in the process of generating new ideas and developing new

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technologies, a time consuming practice during the design-to-market (Hauser et al., 1996). A key player at initiation phase notwithstanding, R&D personnel are likely to sense stronger feelings of uncertainties than marketing or manufacturing because they possess more knowledge in the technical feasibilities of the project. As a result, a process-based reward is more favorable for R&D because it offers risk aversion. Marketing personnel, on the other hand, may be more willing to be rewarded for the products’ market performance. It is quite practical to expect that marketing is the sole prize claimer for a product’s market success. After all, a successful market performance will ultimately be translated into the bottom line, i.e. financial statements, or vice versa, which give immediate pressure to the marketing function. Therefore, rewards that tie to procedures, behaviors, and other means of achieving desired outcomes (i.e. process-based reward) may be counterproductive for marketing. To our surprise, no significance relationships were found for outcome-based reward at implementation phase of JRS in predicting knowledge sharing and NPD performance. One plausible explanation is that a foreseeable reward is viewed as more secured at implementation phase because its outcome is more certain. The opposite and inconsistent patterns among R&D, marketing, and manufacturing functions regarding process-based and outcome-based reward signal potential conflicts, because each function seem to have varied focal points in the reward programs. The inconsistencies of their focus may be the causes of no significant relationships found in association with NPD performance. Hence, our findings suggest that managers should strike a balance between emphasis on process-based reward for R&D and on outcome-based reward for manufacturing. For example, other mechanisms for successful NPD results, e.g. performance management systems promoting knowledge sharing (Hsu, 2006), enhance communication (Song and Parry, 1993), and reward systems designed to build co-operative knowledge sharing environment (Shih et al., 2006) should be considered because the benefits brought by extrinsic rewards would diminish over time. Rewards contingent on various phases of NPD appear to be more complicated than were expected. More dimensions and contexts need to be explored to further clarity the concepts. Future studies may incorporate extrinsic and intrinsic reward mechanisms to test the interaction effects of JRS to clarify the complementary effects in NPD under diversified contexts as suggested by Sarin and Mahajan (2001). Contrary to the prior studies claiming that the use of over-reward incentives stimulates knowledge sharing among organizational members (Keidel, 1985; Harder, 1992), our empirical results suggest that over-reward incentives has no significant effects on knowledge sharing. HR practitioners value rewards that facilitate knowledge sharing at the initiation stage (c.f. Bock and Kim, 2002, p. 19), but acknowledge that such reward may not elevate organizational participants’ level of commitment. The significant and positive relationship between knowledge sharing and NPD performance is also consistent with prior studies (Song and Parry, 1993; Griffin and Hauser, 1996). Therefore, fostering knowledge sharing among NPD members is the essential tasks for management. In addition, formal reward systems based on knowledge sharing behaviors, collective efforts, team-based and company-wide incentives, and informal reward systems based on procedural fairness and trust, are all conducive to knowledge sharing.

Limitations While this study has yielded major findings that possess significant implications for both theory and practice, several limitations need to be addressed as well. First, more objective NPD performance measurement (Godener and So¨derquist, 2004), such as finance, customer satisfaction, strategic management, process management, technology management, innovation, and knowledge management should be employed in future studies to ensure that quantitative outcomes of NPD performance is consistent with qualitative measures. Second, because data for this study were collected from high-tech organizations across electronics, semiconductor, biotechnology, and pharmaceutical industries, it would be helpful for future studies to replicate our findings in non-high tech settings to enhance the generalizability of our results in other settings. Third, this study ignored the importance of individuals’ variance in terms of knowledge, skill, and ability (KSA) for teamwork and knowledge sharing (Stevens and Campion, 1994). Future studies may need to incorporate the necessary KSA of NPD members into related studies for further clarification. Finally, while within-team and interunit networks had different effects on the outcomes of three knowledge-sharing phases (Hansen et al., 2005), future studies on knowledge sharing under NPD setting should examine how multiple networks affect different phases of knowledge sharing.

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Appendix 1

Construct and response formats

Measurement items

Jointly determine the allocation: (alpha ¼ 0:89) Please indicate the degree to which you agree or disagree with the following statements (1 ( “strongly disagree”, 7 ( “strongly agree”)

1. Participative management was used commonly in our company 2. Our company offers a joint reward to cross-functional NPD teams for their performance 3. A policy of jointly determine the allocation of rewards in our company is emphasized as a means for motivating NPD members 4. All NPD-involved functions are invited to determine how to properly allocate their rewards jointly in our company 1. In initiation phase of NPD, team members are rewarded for completing major milestones/stages 2. In initiation phase of NPD, team members are rewarded for meeting certain prescribed conditions 3. In initiation phase of NPD, teamwork behavior is taken into account when evaluating/rewarding the team 4. In implementation phase of NPD, the rewards received by team members are related entirely to the profit contribution attributed to the team 5. In implementation phase of NPD, the rewards received by team members are deferred until bottom-line results of the team (e.g. product performance, market share, profitability, and sales) are available 1. In NPD process, if team members encounter failure, management encourages them to keep trying 2. Management encourages team members undertake entrepreneurial behavior by supporting new ideas and risk-taking 3. Initial failures in NPD process do not reflect on your competence 1. Our company provides over-reward incentives to team members for a successful NPD project 2. In our company, even though the teams failed in their mandate, they remained a base pay 3. In our company, rewards accrue to those who perform superbly on a failing project 1. Contributes explicit knowledge to organizational database 2. Sharing explicit knowledge in formal interactions within or across teams or units 3. Sharing tacit knowledge in formal interactions within or across teams or units 4. Sharing explicit knowledge in informal interactions among individuals 5. Sharing tacit knowledge in informal interactions among individuals 6. Sharing explicit knowledge within communities of practice, which are voluntary forums of employees around a topic of interest 7. Sharing tacit knowledge within communities of practice, which are voluntary forums of employees around a topic of interest (continued)

Rewards contingent on NPD phase: (alpha ¼ 0:91) Please indicate the degree to which you agree or disagree with the following statements (1 ( “strongly disagree”, 7 ( “strongly agree”)

Risk-free to participants: (alpha ¼ 0:90) Please indicate the degree to which you agree or disagree with the following statements (1 ( “strongly disagree”, 7 ( “strongly agree”) Over-reward incentives: (alpha ¼ 0:90) Please indicate the degree to which you agree or disagree with the following statements (1 ( “strongly disagree”, 7 ( “strongly agree”) Knowledge sharing: (alpha ¼ 0:91) Please indicate the degree to which you agree or disagree with the following statements (1 ( “strongly disagree”, 7 ( “strongly agree”)

Table AI. Construct, construct reliability, and measurement items

Construct and response formats

Measurement items

Innovation performance: (alpha ¼ 0:90) Please indicate the degree to which you agree or disagree with the following statements (1 ( “strongly disagree”, 7 ( “strongly agree”)

1. The innovation performance of our team or program in terms of profits, sales, and market share has met our firm’s objectives 2. Compared with our major competitors, our innovation performance in terms of profits, sales, and market share is far more successful 3. Compared with our other teams in our firm, the innovation performance of our team is far more successful 4. From an overall profitability standpoint in the industry, the innovation performance of our team has been very successful

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Scale items

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Table AII. Results of factor analysis of JRS items

1. Participative management was used commonly in our company 2. Our company offers a joint reward to cross-functional NPD teams for their performance 3. A policy of jointly determine the allocation of rewards our company is emphasized as a means for motivating NPD-involved members 4. All NPD-involved functions are invited for determining how to allocate their rewards jointly in our company 5. In initiation phase of NPD, team members are rewarded for completing major milestones/stages 6. In initiation phase of NPD, team members are rewarded for meeting certain prescribed conditions 7. In initiation phase of NPD, teamwork behavior is taken into account when evaluating/rewarding the team 8. In implementation phase of NPD, the rewards received by team members are related entirely to the profit contribution attributed to the team 9. In implementation phase of NPD, the rewards received by team members are deferred until bottom-line results of the team (e.g. product performance, market share, profitability, and sales) are available 10. In NPD process, if team members encounter failure, management encourages them to keep trying 11. Management encourages team members undertake entrepreneurial behavior by supporting new ideas and risk-taking 12. Initial failures in NPD process do not reflect your competence 13. Our company provides over-reward incentives to team members for a successful NPD project 14. In our company, even though the teams failed in their mandate, they remained on base pay 15. In our company, rewards accrue to those who perform superbly on a failing project

Factor 1 Factor 2 Factor 3 Factor 4 Factor 5 0.73 0.75 0.70 0.67 0.86 0.83 0.65 0.88

0.79 0.78 0.71 0.83 0.47 0.98 0.46

Notes: Factor 1 corresponds to joint determination of reward allocation, Factor 2 corresponds to process-based reward at initiation phase, Factor 3 corresponds to outcome-based reward at implementation phase, Factor 4 corresponds to risk-free to participants, and Factor 5 corresponds to over-reward incentives; n ¼ 233

Joint reward system

Appendix 3

Scale items 1. Contributes explicit knowledge to organizational database 2. Sharing explicit knowledge in formal interactions within or across teams or units 3. Sharing tacit knowledge in formal interactions within or across teams or units 4. Sharing explicit knowledge in informal interactions among individuals 5. Sharing tacit knowledge in informal interactions among individuals 6. Sharing explicit knowledge within communities of practice, which are voluntary forums of employees around a topic of interest 7. Sharing tacit knowledge within communities of practice, which are voluntary forums of employees around a topic of interest 8. The innovation performance of our team or program in terms of profits, sales, and market share has met our firm’s objectives 9. Compared with our major competitors, our innovation performance in terms of profits, sales, and market share is far more successful 10. Compared with our firm’s other teams, the innovation performance of our team or program is far more successful 11. From an overall profitability standpoint in the industry, the innovation performance of our team or program has been successful

Factor 1

Factor 2

0.74

297

0.80 0.77 0.79 0.81 0.77 0.76 0.82 0.85 0.80 0.81

Notes: Factor 1 corresponds to knowledge sharing, Factor 2 corresponds to NPD performance; All items loaded on the factors as designed and are accounted for 73 percent of the total variance; n ¼ 233

About the authors Tsun-Jin Chang is an Associate Professor in the Department of Business Administration at Shih Chien University, Kaohsiung Campus. He received a BS in Business Administration from Tam Kang University, and an MBA and a PhD in Management from the Institute of Business Management of National Sun Yat-Sen University. His current research interests include the management of R&D, internal customer satisfaction incentives for NPD, and improving the process of NPD. Tsun-Jin Chang is the corresponding author and can be contacted at: [email protected] Shang-Pao Yeh is an Associate Professor of the Department of International Business at Leader University. He received a Master’s degree in Public Administration from the University of Southern California (USC), an MBA from Northrop University, and a Doctorate in Management from Webster University. His recent research interests are job insecurity, knowledge sharing, and OCB under organizational reform and change. I-Jan Yeh is an Assistant Professor of the Department of Public Policy and Management at Shih Hsin University. He received a Master’s degree in Public Administration and a PhD in Public Policy from the University of Southern California (USC). His recent areas of research interest are in knowledge management in regulatory process and digital government.

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Table AIII. Results of factor analysis of all constructs except for JRS items

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Juan G. Cegarra-Navarro and Eusebio Angel Martı´nez-Conesa Polytechnic University of Cartagena, Cartagena, Spain Abstract Purpose – E-business requires small and medium-sized enterprises (SMEs) to seek both external and internal knowledge and to establish external and internal relationships with partners, such as customers and suppliers. This paper aims to describe a model that examines how knowledge management has an impact on the adoption of e-business, particularly in SMEs. Design/methodology/approach – This paper reviews literature to identify relevant measures through a structural equation model, which is validated through an empirical investigation of 107 SMEs in the Spanish telecommunications sector. Findings – The results show that, in order to implement e-business systems, companies need to provide and support the acquisition, sharing and application of knowledge as prior steps. Research limitations/implications – Other factors that have not been included in this study are also likely to affect knowledge acquisition. Practical implications – Organisations that engage in learning from their customers and suppliers not only test the effectiveness of a new direction of e-business, but also have the potential to design their e-business around what customers truly need and want, and as such gain a sustainable competitive advantage. Originality/value – These results have implications for e-business managers in formulating policies and targeting appropriate organisational capabilities to ensure the effective adoption of e-business systems. Keywords Customer orientation, Electronic commerce, Knowledge management, Small to medium-sized enterprises, Spain Paper type Research paper

Broadly speaking, e-business can be defined as any business carried out over an electronic network (exchanging data files, having a website, using other companies’ websites or buying and selling goods and services online). Some of the major benefits of e-business are providing more timely and accurate information for decision-making, enabling improved coordination and communication with business partners, facilitating improved customer service, and helping reduce administrative costs (Zhuang and Lederer, 2003). Small and medium-size enterprises (SMEs) have been recognised as being fundamental players within the European e-business economy (Howard, 1990). In Spain, SMEs represent more than 99.8 per cent of all businesses registered, generate about 70 per cent of the employment and contribute to 65 per cent of the gross domestic product International Journal of Manpower Vol. 28 No. 3/4, 2007 pp. 298-314 q Emerald Group Publishing Limited 0143-7720 DOI 10.1108/01437720710755263

The data of this research originated from a research program supported by the European Regional Development Fund entitled: “Knowledge management, intellectual capital, technology systems and other management alternatives” Reference: EX-300-503.

(Cegarra and Sabater, 2005). Even though e-business provides many opportunities for SMEs, a number of SMEs have not capitalised on this new method (Fillis et al., 2004). The barriers to change are no longer technological – they are now barriers of competence and will. This “resistance” to implement e-business can be related to issues of uncertainty, trust and lack of knowledge, which impede the rate at which SMEs’ adopt e-business (Fillis et al., 2003). This is especially true if managers in SMEs have never previously used any electronic means of communication (Nath et al., 1998). Denning (2000) suggests that the growth of this new electronic world requires innovation and the generation of new businesses with developing and leveraging knowledge assets, including many different types of new skills, new forms of integrated and intensive relationships with external entities, new sets of perceptions held by customers, channels and suppliers, and, of course, significant new knowledge. Therefore, e-business must be structured around the knowledge and context needed for the integration of technology systems (TS). Knowledge management (KM) is the process of collecting, organising, storing and exploiting the information and data within organisations (Nonaka and Takeuchi, 1995), particularly tacit and explicit knowledge. The key benefits of using KM among SMEs have been identified as: the provision of environmental information (Birley, 1985); support and confirmation in decision-making (Carson et al., 1995); the generation of new contacts (Birley, 1985) and the development of ideas for new product offerings (Carson et al., 1995). This study aims to examine the impact of KM on the adoption of e-business systems. In the following section, we introduce the key concepts of KM phases (knowledge acquisition, knowledge sharing and knowledge application) and e-business for SMEs. Contextual framework SMEs have been using TS applications for many years (Maguire and Magrys, 2001). It is however important to realise that knowledge creation cannot be accomplished solely using technology tools (Ackerman, 2000). Decision support systems, executive information systems, data warehousing and mining systems along with a host of other technologies have all been evaluated by Davenport and Prusak (1998), and more recently by Smith and Farquhar (2000) who have discussed “artificial intelligence”, alluding to how all these solutions fall short in the process of knowledge creation. Malhotra (2000) emphasised that “it is not the computers but what people do with them that matters”, suggesting the role of the users’ motivation and commitment in TS performance. In this regard, Ackerman (2000) asserts that creating and using knowledge is a human endeavour in that it requires individuals to think and to reason; in short, to make sense of the current and emerging context around them. KM applications provide a novel architecture for enterprises that contributes significantly to understanding and facilitating the e-business transformation of operational processes (Fahey et al., 2001). Johannessen et al. (1999) for instance, argued that knowledge integration and related applications have been developed as strategic competitive factors in modern organisations, such as the managing of intellectual and social capital, the promotion of organisational innovation and the support of new forms of collaboration. From the perspective of technological innovation, knowledge acquisition, knowledge sharing and the practical application of knowledge are the main elements for developing technological capabilities (Gilbert and Cordey-Hayes, 1996; Johannessen et al., 1999).

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Knowledge acquisition Knowledge acquisition (KA) is defined as the business processes that capture knowledge (Lin and Lee, 2005). Resources are scarce in SMEs and a chief knowledge officer cannot be justified, so knowledge is likely to result from secondary data (e.g. trade journals, sector research, conferences and professional magazines) or from personal contacts (Langerak, 2003). In this regard, Dewhurst and Cegarra (2004) suggest that since resources are scarce in SMEs and since any practice to acquire knowledge will generally be more expensive than encouraging meetings with suppliers or customers, it is likely that these will be favoured by SMEs. From the point-of-view of this paper, the context of KA results from companies working cooperatively with other organisations to support new products, satisfy customers and create new market innovations. Customer orientation (CO) and supplier orientation (SO) focus on determining the relevant customers or suppliers, processes and domain knowledge needed to carry out business activities successfully and acquiring or generating this knowledge by monitoring suppliers’ and customers’ activities within the e-business system. Under this framework, sellers or front-line contact people acquire knowledge based on their direct experiences and observations, which are stored in their memories as cognition, belief and values (Selnes and Sallis, 2003). Davenport et al. (2001) call this knowledge “human data or knowledge”, because it is captured and used mainly by employees interacting with customers or observing and interpreting the behaviour of colleagues.

Knowledge sharing There is a problem with previous arguments in that the information given by the customer or the supplier is one thing, and the knowledge used by the company is another. For example, the knowledge created by sales is not formulated or controlled directly by the management, but it is being continuously created through new customers and lost as employees move, groups dissolve and application wanes, thus, the “sharing of knowledge” commences. Knowledge sharing (KS) is defined as the transmission of knowledge from individuals who have been related with customers and suppliers to the rest of the people that form part of the organisation. Very often, this process takes place by members sharing stories or anecdotes of actual work practices, as opposed to what is mentioned in formal job descriptions or procedure manuals (Brown and Duguid, 1991). According to Sinkula et al. (1997), open-mindedness (i.e. the willingness to consider ideas and opinions that are new or different) is associated with the concept of learning, through which managers encourage the distribution of knowledge by social processes between groups and individuals. The result of these externalisation and combination processes will be “shared explicit knowledge” stored in the organisational memory. The goal of this social knowledge is that all members of the organisation are aware (Cohen, 1991) of where the useful complementary abilities reside (e.g. who knows what? Who can help with that? Who can exploit new information?). The maintenance of the “organisational memory” supposes in each case, the reactivation and development of new information, which fosters learning and the integration of new knowledge in members of the organisation, thus, “knowledge application” begins.

Knowledge application Knowledge application (KAP) includes the absorption of the knowledge generated in the acquisition and sharing phases (i.e. the internalisation of the knowledge within an organisation), so it could be applied to what has already been learnt in those phases to businesses and its own activities. For example, when information on customers and suppliers is assimilated by decision-makers and it changes their mental models of the market environment, it has been applied to make a decision (Dickson, 1994, p. 46). Therefore, before the organisation can use the shared knowledge, it must first be assimilated. In this regard, Kim (1998) conceptualises absorptive capacity as learning capability and problem solving skills that enable individuals to assimilate knowledge and create new knowledge. Kim (1993) argues that individual learning can be classified as conceptual or operational. On the one hand, conceptual learning concerns thinking about why things are or why they are done in the first place, sometimes challenging the very nature or existence of the prevailing conditions, procedures, or conceptions and potentially leading to new mental models and new ways of understanding. Through conceptual learning, individuals develop cognitive maps (Huff, 1990) of the different domains in which they operate. Differently, operational learning basically refers to learning how to do something. It relates to learning how to complete the steps necessary to perform a particular task. Operational learning is this nexus between what individuals can do (capability), what they want to do (motivation), and what they need to do (focus) which enhances the application of knowledge. Research model and hypotheses Knowledge acquisition is greater when more assorted interpretations are developed by the individuals that form part of the organisation. Huber (1991) asserts that one of the principal factors that influence the success of getting multiple interpretations is collaboration with other organisations. Taking into account Huber’s contributions, CO and SO are ideal platforms to learn and explore new possibilities, because two or more individuals are working together with different resources and complementary capacities, which is a learning facilitator factor. Communication and collaboration with customers and suppliers provide a “face-to-face” interaction so that the desired exchange of knowledge can occur. However, at those stages, knowledge is individual rather than social (Cohen, 1991), and tacit rather than explicit (Nonaka, 1994). Therefore, it is necessary that this knowledge becomes embedded within organisational memory-structures in order that it becomes a component of the “dominant design”. New knowledge may be further “consolidated” through the emergent understandings that are created by group members when they interact (Schein, 1992). Cegarra and Sabater (2005) suggest that “knowledge sharing” supports knowledge application because it reduces uncertainty. It tells employees about their learning – what is working (do more of this) and what is not (do less of this). Considering this, we suggest that “KS” helps learners adjust what they are doing so they are more successful in their tasks. In this regard, Akgu¨n et al. (2005) suggest that when an individual considers the alternatives and shows curiosity about understanding related issues, their ability to discover new and novel practices increases, which in turn may affect the implementation of new routines.

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Even though e-business provides all employees with the necessary tools to make the right decisions, very little is achieved if they do not link these tools with their previous cognitive maps (Hsiu-Fen and Gwo-Guang, 2005). This means that for users of e-business to realise the full potential of the technology, they must be willing to use the technology and become an effective user (Bontis and Fitz-enz, 2002). In this aim, the set of elements that contribute having employee sentiment should be considered. By facilitating a high level of KAP, organisations will have members who will assume responsibilities in using TS (Venkatesh and Speier, 2000; Robey et al., 2002). According to Koh and Maguire (2004), one of the main drivers of the emerging trend of SMEs implementing e-business systems is the pressure from the big players (their business customers). In turn, Kotler (2000) suggests that when information or knowledge is fragmented within a company, customer feedback is hard to obtain and, as a result, performance suffers. E-business systems enable order processing to be computerised and performance to be monitored in real-time. Therefore, SMEs equipped with e-business can provide a better and competitive service to their customers, which could enhance organisational performance. Given the aforementioned discussion, we propose the structural model shown in Figure 1 and the following hypotheses: H1. Supplier orientation leads to knowledge sharing. H2. Customer orientation leads to knowledge sharing. H3. Knowledge sharing leads to knowledge application. H4. Knowledge application leads to e-business systems. H5. E-business systems lead to better business performance. Method The Spanish telecommunications industry was the subject of our data collection. The total market in Spain for “telecommunications” (including fixed and mobile and data communications with broadband internet access as a key means of transmission), represents 18 percent of the total European telecommunications market and nearly 4.7 percent of the Spanish gross domestic product. SME’s that comprise the Spanish Telecommunications industry are highly motivated to introduce processes for KM and

Figure 1. E-business process via KM

e-business as they have to face up to a highly dynamic environment, strong competition and rapid advances in technology. Considering this, we suggest that the telecommunications industry in Spain is an appropriate setting for an investigation of KM and its impact on e-business adoption. The Spanish National Classification of Economic Activities (CNAE) was used to define the sector to which the SMEs belonged. Based on the Statistical Year 2002, we used a list of 665 SMEs provided by the Sistema de Ana´lisis de Balances Ibe´ricos[1] (SABI) database as an initial sampling frame. All companies were included in the CNAE-642 and were classified according to the European Union classification (COM, 1996) as SMEs (with fewer than 250 employees, an annual turnover not exceeding e50 million, and an annual balance sheet total not exceeding of e43 million). In order to develop appropriate measures for the constructs that were of interest in this study, we combined scales from several other relevant empirical studies with some additions to make an initial list of 49 items; 24 measuring the extent of e-business; 5 £ 2 ¼ 10 measuring the two factors of customer and supplier orientation (CO and SO); 5 £ 2 ¼ 10 measuring the two factors of sharing and application of knowledge (KS and KAP) and FIVE measuring business performance (BP). We eliminated several redundant items through interviews with businesspeople and colleagues, and we then tested a first draft of the questionnaire using three leading Spanish telecommunications businesses. The questionnaire constructs comprised the following: (1) The initial measures relating to the existence of SO and CO scales consisted of eight items (4 £ 2 ¼ 8 measuring CO and SO factors) adapted from a scale designed by Dewhurst and Cegarra (2004) to measure the construct of external communities of practice. Consistent with Dewhurst and Cegarra (2004), items that tapped the SO and CO were interwoven with issues related to encouraging individuals in the organisation to track changing markets and share market intelligence with customers and suppliers. (2) The measures relating to the existence of the knowledge sharing (KS) scale consisted of four items adapted from a scale designed by Baker and Sinkula (1999) to measure the construct of “open-mindedness”. These items describe the way management faced up to change, introduced it actively into the company through projects, collaborated with other members of the organisation, and recognised the value of new information or taking risks. (3) The existence of conditions necessary to support “knowledge application” (KAP) were measured using an adapted version of a scale designed by Bontis et al. (2000). This construct focuses on the generation of new insights, engaging in actions that are experimental in nature, breaking out of traditional mind-sets to see things in new and different ways, developing the competencies necessary for doing one’s job, having a sense of pride and ownership in one’s work, and being aware of the critical issues that affect one’s work. (4) Cegarra and Sabater (2005) classify TS into three categories: the internet enables customers and employees to have access to instant available information about products and services across time and distance; groupware provides collaborative groups formed by employees, managers and sometimes customers with the ability to link large amounts of information in a dynamic manner; and collective systems facilitate flows of information that may be controlled by users. Table I presents

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Table I. Summary of survey e-business items

1. Internet connection 2. Web page or homepage 3. Website, e.g. catalogue on internet 4. Banners or links with other sites 5. Counters and trackers 6. Site map 7. Search engine 8. Bulletin board systems 9. E-mail 10. Open discussion forums 11. Open voting systems 12. Open distribution lists 13. Online calendars or agendas 14. Repository of documents 15. Newsgroup (USENET) 16. Access to share database 17. Tools to provide vendor recommendations 18. Tools to provide estimated costs 19. Tools to provide timeframes 20. Affiliate programs with tracking (e.g. cookies) 21. Creation of customised billing systems 22. Customer service management solutions 23. Complete shopping cart solutions 24. Payment and verification systems

A Yes A Yes A Yes A Yes A Yes A Yes A Yes A Yes A Yes A Yes A Yes A Yes A Yes A Yes A Yes A Yes A Yes A Yes A Yes A Yes A Yes A Yes A Yes A Yes

A No A No A No A No A No A No A No A No A No A No A No A No A No A No A No A No A No A No A No A No A No A No A No A No

these measurement constructs, where items one to eight measured the internet; items nine to 16 measured groupware; and items 17 to 24 measured collective systems. (5) The initial measures relating to the existence of a BP scale consisted of three items. Several measures of business performance have appeared in the literature and we adopt the growth-based measures proposed by McDougall et al. (1994), Roth and Ricks (1994), and Bontis et al. (2000) for sales, profits and profitability on total assets. Before conducting the surveys, Spanish telecommunication businesses were contacted and asked by our team to participate in the study. They were informed by telephone of the work objectives and they were assured of its strictly scientific and confidential nature, as well as the global and anonymous treatment of the data. In total, 665 companies were solicited for participation in the study by telephone, and only 195 agreed. The information-collecting period lasted for about two months, from early May to early July 2005. Three data collection sources have been mainly used for the part of the study reported here. First, the information about CO, SO, KS and KAP was collected by sending letters and e-mails to the manager or general director of the SMEs. Table II shows the 12 items used to measure the “KS” (Y1-3), “KAP” (Y4-6), “SO” (X1-3), and “CO”, (X4-6). In these questions, the individual responding had to indicate his degree of agreement or disagreement on a seven point Likert scale (where 1 ¼ strong disagreement and 7 ¼ strong agreement). From the sample size of 195, a total of 107 valid responses were received giving a response rate of 54.87 per cent with a factor of error of 6.35 per cent for p ¼ q ¼ 50 per cent and a level of reliability of 95.5 per cent.

Item description (KS): Knowledge sharing Y1: The management has ability to work in team Y2: Meetings in which employees from different departments participate, are organised Y3: The management accepts the change introducing it actively in the company

Standardised loading

t-value

Reliability SCR *

0.83

9.61

SCR = 0.833

0.75

8.48

0.79

8.99

(KAP): Knowledge application Y4: Employees provide creative solutions before unforeseen Y5: Employees put at the disposal of the company all the information they possess Y6: Employees do not conceal their mistakes

0.85

10.41

0.85 0.63

10.43 6.86

E-business Y7: Internet technology Y8: Groupware Y9: Collective systems

0.90 0.89 0.81

11.52 11.31 9.76

(BP): Business performance Y10: Growth rate of sales 2002 Y11: Growth rate of profits 2002 Y12: Profitability rate on total assets 2002

0.73 0.71 0.94

8.28 7.99 11.64

0.89

11.54

0.70

8.22

0.83

10.41

(SO): Supplier orientation X1: Novelties are introduce thanks to your suppliers X2: There is a frequent contact (three times per month) with suppliers X3: From time to time suppliers inform about new technologies and products of the sector (CO): Customer orientation X4: Informal activities (dinners, lunches, and travels), in which customers and employees participate, are organised X5: Customers give your company information about the offers that receive from competitors X6: After a product has been delivered, we contact with clients to ask them the degree of satisfaction

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SCR = 0.901

SCR = 0.842

SCR = 0.876

SCR = 0.77

8.95

0.89

10.93

0.70

0.833

7.76 x2ð80Þ

Notes: The fit statistics for the 18 measurement constructs were: ¼ 163:86; GFI ¼ 0:89; RMSA ¼ 0:077; CFI ¼ 0:90; IFI ¼ 0:90; *With scale composite reliability (SCR) of (pc ¼ ðSli Þ2 var (j)/[(Sli)2 var (j)+Suii] Source: Bagozzy and Yi (1988)

Then, every web page of each SME was examined to identify the presence of specific e-business applications (1) or otherwise (0). As a result, three variables with a minimum value of zero and a maximum value of eight were identified and the confirming factorial model, shown in Table II (Y7-Y9), indicated that they could be represented by

Table II. Construct summary, confirmatory factor analysis and scale reliability

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a single factor e-business. Finally, the continuous measures of business performance (BP) in Table II (items Y10-Y12) were collected using data from the SABI database. Measurement model To assess the uni-dimensionality of each construct, a confirmatory factor analysis of the five constructs was conducted. The application of this technique requires that an initial model be proposed, so that if data do not fit well, then the model can be modified until a good fit is achieved. We transitioned from one model to another, removing those items that did not converge substantially with their respective latent variable. To perform this analysis, modification indices, path coefficients and change statistics were examined (the enhanced or attenuated reliability given by the removal of the item) (Anderson and Gerbing, 1988). Table III provides means, standard deviations and correlations for the resulting 18 items (three for each construct). As shown in Table II, the results suggest a good fit for the 18 measurement constructs since: x2 ¼ 163:86, df ¼ 80, p ¼ 0:00; goodness-of-fit index ½GFI
¼ 0:89; comparative fit index ½CFI
¼ 0:90; incremental-fit index ½IFI
¼ 0:90; root mean square error of approximation ½RMSEA
¼ 0:07. Tables II and IV summarise the reliabilities and convergent and discriminant validities for all the constructs. The reliability of the measures is calculated using Bagozzi and Yi’s (1988) composite reliability index and with Fornell and Larker’s (1981) average variance extracted index. For all the measures, both indices are higher than the evaluation criteria of 0.6 for the composite reliability and 0.5 for the average variance extracted (Bagozzi and Yi, 1988). As shown in Table II, all those path coefficients, from the six constructs to the 18 measures, are statistically significant with the lowest t-value for the items measuring KAP effectiveness being 6.86. The fact that all the t-values considerably exceed the standard of 2.00 and the standardised parameters (. 0.5), indicates a satisfactory convergent validity for the five constructs at a level of reliability of 99 per cent. Discriminant validity was assessed in two ways (Baker et al., 2002): first, the confidence interval for each pair-wise correlation estimate (i.e. ^ two standard errors) should not include 1 (Anderson and Gerbing, 1988). Table IV shows that this condition was satisfied for all pair-wise correlations in the measurement model. Second, for each construct, the percentage of variance extracted should exceed the construct’s shared variance with every other construct (i.e. the square of the correlation) (Fornell and Larcker, 1981). This condition is satisfied for all the constructs, as shown in Table IV. For example, the extracted variance for KS rcAVE ¼ 0:624, which exceeds its shared variances with KAP (0.25), e-business (0.02), BP (0.14), SO (0.38) and CO (0.28). Results The proposed structural model is specified from the hypothesised relationships H1-H5 depicted graphically in Figure 2. In terms of our hypotheses (Table V), the findings for H1 and H2 show that SO and CO had a positive and significant influence on KS. By testing the third hypothesis (H3), Table V shows that the effect of KS on KAP had a significant influence at a level of ( p , 0.01). In testing the fourth hypothesis (H4) Table V, again, shows that “the existence of conditions necessary to stimulate knowledge application” has a positive and significant effect on e-business. The finding for H5 shows that e-business is associated with BP at a level of ( p , 0.01). Our data further indicates that e-business mediates the effects of KS and KAP on BP.

5.32 1.22 1.00 0.27 0.40 0.43 0.12 0.16 0.09 0.28 0.24 0.27 0.49 0.34 0.43 0.56 0.09 0.23

5.36 1.20

1.00 0.61 0.06 0.24 0.30 2 0.09 0.01 2 0.10 0.10 0.07 0.17 0.48 0.41 0.34 0.40 0.43 0.17

5.14 1.58 1.00 0.61 0.65 0.41 0.44 0.35 0.16 0.17 0.12 0.26 0.24 0.35 0.50 0.16 0.22 0.52 0.38 0.41 1.00 0.71 0.67 0.28 0.18 0.21 0.34 0.32 0.42 0.54 0.33 0.47 0.39 0.49 0.43

5.03 1.53

Y4

1.00 0.58 0.35 0.33 0.34 0.35 0.34 0.46 0.56 0.43 0.40 0.32 0.49 0.46

4.96 1.52

Y5

1.00 0.19 0.22 0.11 0.32 0.19 0.42 0.35 0.41 0.28 0.29 0.21 0.13

4.33 1.34

Y6

1.00 0.81 0.72 0.28 0.09 0.30 0.10 0.03 0.11 0.02 0.00 0.10

3.98 2.48

Y7

1.00 0.73 0.20 0.04 0.21 0.11 0.02 0.07 0.06 0.03 0.07

2.13 1.78

Y8

1.00 0.10 0.02 0.15 0.06 0.03 20.01 20.05 20.01 0.13

1.96 1.93

Y9

1.00 0.55 0.69 0.21 0.22 0.20 0.18 0.13 0.11

2 0.07 0.68

Y10

1.00 0.67 0.25 0.21 0.20 0.26 0.18 0.13

20.07 0.29

Y11

1.00 0.26 0.30 0.12 0.29 0.29 0.16

2 0.41 0.86

Y12

1.00 0.69 0.83 0.52 0.64 0.42

5.33 1.68

X1

1.00 0.78 0.31 0.49 0.17

4.80 1.89

X2

1.00 0.36 0.45 0.27

5.23 1.61

X3

X5 5.47 1.05

1.00 0.63

X4 5.77 1.17

1.00 0.68 0.55

1.00

5.56 1.01

X6

Notes: Mean (m); Standard deviation (s); Knowledge sharing (Y1-3); Knowledge application (Y4-6); Business performance (Y7-9); Supplier orientation (X1-3); Customer orientation (X4-6)

h O’ Y1 Y2 Y3 Y4 Y5 Y6 Y7 Y8 Y9 Y10 Y11 Y12 X1 X2 X3 X4 X5 X6

Y3

Y2

Y1

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Table III. Correlation matrix analysed

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Table IV. Cross loading and discriminant validity for each pairwise of constructs

KS ! KAP KS- ! E-business KS ! BP KS ! SO KS ! CO KAP ! KS KAP ! E-business KAP ! BP KAP ! SO KAP ! CO E-business ! KAP E-business ! KS E-business ! BP E-business ! SO E-business ! CO BP ! KS BP ! KAP BP ! E-business BP ! SO BP ! CO SO ! KS SO ! KAP SO ! E-business SO ! BP SO ! CO CO ! KS CO ! KAP CO ! E-business CO ! BP CO ! SO

wi

1

(wi þ 2 *1)

Shared variance

0.50 * 0.13 0.37 * 0.62 * 0.53 * 0.50 * 0.38 * 0.56 * 0.70 * 0.61 * 0.38 * 0.13 0.27 * 0.11 0.04 0.37 * 0.56 * 0.27 * 0.29 * 0.33 0.62 * 0.70 * 0.11 0.29 * 0.69 * 0.53 * 0.61 * 0.04 0.33 * 0.69 *

0.09 0.11 0.10 0.07 0.09 0.09 0.10 0.08 0.06 0.08 0.10 0.11 0.10 0.10 0.11 0.10 0.08 0.10 0.09 0.10 0.07 0.06 0.10 0.09 0.06 0.09 0.08 0.11 0.10 0.06

0.68 0.35 0.57 0.76 0.71 0.68 0.58 0.72 0.82 0.77 0.58 0.35 0.47 0.31 0.26 0.57 0.72 0.47 0.47 0.53 0.76 0.82 0.31 0.47 0.81 0.71 0.77 0.26 0.53 0.81

0.25 0.02 0.14 0.38 0.28 0.25 0.14 0.31 0.49 0.37 0.14 0.02 0.07 0.01 0.00 0.14 0.31 0.07 0.08 0.11 0.38 0.49 0.01 0.08 0.48 0.28 0.37 0.00 0.11 0.48

Extracted variance KS rcAVE = 0.624 KAP rcAVE = 0.618 E-business rcAVE = 0.756 BP rcAVE = 0.644 SO rcAVE = 0.704 CO rcAVE = 0.626

Note: * , 0.01; Average variance extracted (pc = Sli 2 var (j)/[Sli 2 var (j)+Suii] Source: Fornell and Larcker (1981)

Figure 2. The theoretical structural model

In order to provide greater confidence in our model specification with KS and KAP treated as intermediate variables between CO, SO and BP, omitting the direct relationship between CO, SO, KS and e-business, we tested our theoretical model (TM) against alternative model specifications (AM). This procedure is recommended by

Standardised parameter Hypotheses estimates Number Sign Parameter Estimate t-value

Linkages in the model Hypotheses Supplier orientation ! knowledge sharing Customer orientation ! knowledge sharing Knowledge sharing ! knowledge application Knowledge application ! e-business systems E-business systems ! business performance

H1 H2 H3 H4 H5

Indirect effect Supplier orientation ! knowledge application Supplier orientation ! e-business systems Supplier orientation ! business performance Customer orientation ! knowledge application Customer orientation ! e-business systems Customer orientation ! business performance Knowledge sharing ! e-business systems Knowledge sharing ! business performance Knowledge application ! business performance

þ þ þ þ þ

g11 g12 b21 b32 b43

0.37 0.24 0.56 0.46 0.24

3.28 * 2.13 * * 5.01 * 3.69 * 2.65 *

þ þ þ þ þ – þ þ þ

k21 k31 k41 k22 k32 k42 i31 i41 i42

0.21 0.09 0.02 0.13 0.06 0.01 0.26 0.06 0.11

2.89 * 2.37 * * 1.78 * * * 2.01 * * 1.81 * * * 1.50 3.19 * 2.06 * * 2.18 * *b

Notes: *p , 0.01; * *p , 0.05; * * *p , 0.1; Fit statistics for measurement model of 18 indicators for six constructs: GFI ¼ 0:84; CFI ¼ 0:82; IFI ¼ 0:82

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Table V. Construct structural model

Anderson and Gerbing (1988) using the chi-square difference test (CDT) to test the null hypothesis; MT – MA ¼ 0. Compared with a less parsimonious AM, a non-significant CDT would lead to the acceptance of the more parsimonious TM. Table VI reports a significant change in the chi-square between our model and AM. The CDT presents a p , 0.01 level, which leads to the consideration of the alternative model’s fit as significantly worse.

Discussion The adoption of e-business is a complex process that is influenced by numerous factors such as what customers want and need, subjective norms, stages of adoption, user competence, implementation processes and organisational factors (Chiasson and Lovato, 2001). In turn, although knowledge from sellers or front-line contact people is the most important success factor in the implementation of e-business, it is also its biggest risk factor, as employees are afraid of giving away their expertise with

Model Theoretical model (TM) Alternative model (AM)

Chi-square

Degrees of freedom

Chi-square difference

Degrees of freedom difference

Probability

222.87 232.28

86 88

9.41

2

p ¼ 0:009 *

Note: *Compared with the proposal model (TM), the alternative models (AM) present a significant worse fit and a less parsimonious specification. Therefore, TM is preferred as a better alternative

Table VI. Sequential chi-square tests

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customers and suppliers to colleagues who would use this knowledge to get promoted at their expense. This paper examines the relative importance and significance of KM on e-business within SMES. The results suggest that in order to implement e-business systems, companies need to provide and support the acquisition, sharing and application of knowledge as prior steps. Note that the results also indicate that e-business contributes to the creation of BP. The theoretical and managerial implications of the bi-directional relationships observed across those constructs are discussed in further detail in the following paragraphs. With regard to H1 and H2, the analytical results reveal significant associations between two knowledge acquisition factors (CO, and SO) and the level of knowledge sharing. This addresses the concerns expressed by authors such as Sinkula et al. (1997) when they refer to market information processing as a necessary condition for KM, as it is the process by which information is converted into knowledge. Organisations which engage in learning from their customers and suppliers not only test the effectiveness of a new e-business direction, but also have the potential to design their e-business around what customers truly need and want, and as such gain sustainable competitive advantage. Therefore, SMEs must be more aware of the benefits in their surrounding environment to implement e-business (Gossain and Kandiah, 1998). Understanding how customers and suppliers form perceptions of a firm innovation would help designers, implementers and users in their evaluation, selection, implementation and on-going use of an e-business system. Regarding H3, our findings suggest that KAP is driven by sharing what has already been learned. These results also support that KS has an indirect effect on e-business through KAP. This supports what authors such as Lin and Lee (2005, p. 176) express, when they suggest “knowledge sharing is important in innovation processes in the e-business context”. By knowledge sharing, organisations may provide outcomes and benefits in two main ways: (1) Sharing solutions provided by customers and suppliers. (2) Redefining or adapting organisational goals or ways of doing things. Some solutions adapted by businesses may include new TS. Therefore, e-business cannot occur without a KS context. In fact, when somebody represents knowledge, they are influenced by the context in which the subject performs articulation. Through KS, organisations foster a dynamic capacity where teams and their members are continuously able to increase their abilities to articulate knowledge (Fahey et al., 2001). Regarding H4, the analytical results of this study support that firms that stimulate and improve organisational application of knowledge (KAP) are more likely to adopt e-business systems. This finding is consistent with Gilbert and Cordey-Hayes’ (1996) conceptualisation of KAP as the facilitator of successful technological innovation. People usually take advantage of databases after colleagues direct them to a specific location in a database for lessons or tools (Gold et al., 2001). For example, rather than engaging in an extensive search through an organisation’s information technology-based repository of knowledge (e.g. databases), employees turn first to friends and peers to learn where to find relevant knowledge. Furthermore, the results support that KA has an indirect effect on KAP through KS. These findings support the views of Hsiu-Fen and Gwo-Guang(2005), which draw attention to the fact that

knowledge application enables employees to both use existing knowledge and create new knowledge, both of which are crucial for e-business systems adoption. With regard to H5, our results support the importance of e-business to enhance organisational performance. Our data further indicates that e-business systems are significant, but not enough to achieve higher levels of business performance, in this way, only if they are supported for KM drivers (i.e. SO, CO, KS and KAP) can they become powerful tools for success. An effective e-business application is expected to improve performance, but if poorly planned, developed or implemented without due recognition to increase human resource effectiveness, it can breed disaster and hold back individual and/or group performance (Templer, 1989). That is, if organisational e-business systems are focused on making knowledge useful, firms are more likely to achieve increased levels of performance. Conclusions The findings of this study stress that companies may be over-investing in the adoption of most hyped technologies, and under-investing on mechanisms to facilitate the flow of knowledge creation. Furthermore, the firms that consider KM as a lineal process (i.e. KA ! KS ! KAP) can expect to achieve higher levels of e-business adoption. Consequently, in the context of e-business systems adoption, it is important to note that managers should encourage employees to create and use knowledge rapidly and effectively as a prior step. The study has some limitations. First, although the telecommunication industry falls clearly within the category of SMEs, they might not be representative of all SMEs because of the types of products and services they sell. Second, we are able to provide only a snapshot of ongoing processes and not measurements of the same process over time. Moreover, other factors that have not been included in this study are also likely to affect KM processes. Taking into account its limitations, this study points to the need for further avenues of research, including more precise measurement constructs; the effects of other learning facilitators and life-cycles on e-business systems. A longitudinal study is needed to examine the relationships between knowledge application and e-business and the ways in which they affect customer relationships. Finally, future studies including large companies may help improve the rigor of the results. Note 1. Sistema de ana´lisis de balances ibe´ricos or SABI is a database containing information on over 555,000 Spanish companies and over 67,000 Portuguese companies. References Ackerman, M.S. (2000), “The intellectual challenge of CSCW: the gap between social requirements and technical feasibility”, Human-Computer Interaction, Vol. 15, pp. 179-203. Akgu¨n, A.E., Lynn, G.S. and Yilmaz, C. (2005), “Learning process in new product development teams and effects on product success: a socio-cognitive perspective”, Industrial Marketing Management, Vol. 16 No. 3, pp. 215-23. Anderson, J.C. and Gerbing, D. (1988), “Structural modelling in practice: a review and recommended two-steps approach”, Psychological Bulletin, Vol. 103 No. 3, pp. 411-23.

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Nonaka, I. (1994), “A dynamic theory of organizational knowledge creation”, Organization Science, Vol. 5 No. 1, pp. 14-37. Nonaka, I. and Takeuchi, H. (1995), The Knowledge-creating Company: How Japanese Companies Create the Dynamics of Innovation, Oxford University Press, New York, NY. Robey, D., Ross, J.W. and Boudreau, M.C. (2002), “Learning to implement enterprise systems: an exploratory study of the dialectics of change”, Journal of Management Information Systems, Vol. 19 No. 1, pp. 17-46. Roth, K. and Ricks, D.A. (1994), “Goal configuration in a global industry context”, Strategic Management Journal, Vol. 15 No. 2, pp. 103-20. Schein, E. (1992), Organizational Culture and Leadership, 2nd ed., Jossey-Bass, San Francisco, CA. Selnes, F. and Sallis, J. (2003), “Promoting relationship learning”, Journal of Marketing, Vol. 67 No. 3, pp. 80-95. Sinkula, J.M., Baker, W.E. and Noordewier, T. (1997), “A framework for market-based organizational learning: linking values, knowledge, and behavior”, Journal of the Academy of Marketing Science, Vol. 25 No. 4, pp. 305-18. Smith, R.G. and Farquhar, A. (2000), “The road ahead for knowledge management”, AI Magazine, Vol. 21 No. 4, pp. 17-40. Templer, A. (1989), “Human resource managers and the new technology agenda”, Journal of General Management, Vol. 15 No. 2, pp. 73-80. Venkatesh, V. and Speier, C. (2000), “Creating an effective training environment for enhancing telework”, International Journal of Human Computer Studies, Vol. 52 No. 6, pp. 991-1005. Zhuang, Y. and Lederer, A.L. (2003), “An instrument for measuring the business benefits of e-commerce retailing”, International Journal of Electronic Commerce, Vol. 7 No. 3, pp. 65-99. About the authors Juan G. Cegarra-Navarro is a Doctor in Business Administration, and Master in marketing and communications. Currently, he is an assistant professor at the Polytechnic University of Cartagena, Cartagena, Spain. Juan G. Cegarra-Navarro is the corresponding author and can be contacted at: [email protected] ´ ngel Martı´nez-Conesa is lecturer and PhD student at the Polytechnic University of Eusebio A Cartagena. His investigation line is focused in knowledge management and organisational learning.

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Knowledge sharing and firm innovation capability: an empirical study Hsiu-Fen Lin

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Department of Shipping and Transportation Management, National Taiwan Ocean University, Keelung, Taiwan Abstract Purpose – The study sets out to examine the influence of individual factors (enjoyment in helping others and knowledge self-efficacy), organizational factors (top management support and organizational rewards) and technology factors (information and communication technology use) on knowledge sharing processes and whether more leads to superior firm innovation capability. Design/methodology/approach – Based on a survey of 172 employees from 50 large organizations in Taiwan, this study applies the structural equation modeling (SEM) to investigate the research model. Findings – The results show that two individual factors (enjoyment in helping others and knowledge self-efficacy) and one of the organizational factors (top management support) significantly influence knowledge-sharing processes. The results also indicate that employee willingness to both donate and collect knowledge enable the firm to improve innovation capability. Research limitations/implications – Future research can examine how personal traits (such as age, level of education, and working experiences) and organizational characteristics (such as firm size and industry type) may moderate the relationships between knowledge enablers and processes. Practical implications – From a practical perspective, the relationships among knowledge-sharing enablers, processes, and firm innovation capability may provide a clue regarding how firms can promote knowledge-sharing culture to sustain their innovation performance. Originality/value – The findings of this study provide a theoretical basis, and simultaneously can be used to analyze relationships among knowledge-sharing factors, including enablers, processes, and firm innovation capability. From a managerial perspective, this study identified several factors essential to successful knowledge sharing, and discussed the implications of these factors for developing organizational strategies that encourage and foster knowledge sharing. Keywords Knowledge sharing, Organizational innovation Paper type Research paper

Introduction Knowledge sharing creates opportunities to maximize organization ability to meet those needs and generates solutions and efficiencies that provide a business with a competitive advantage (Reid, 2003). Knowledge sharing can define as a social interaction culture, involving the exchange of employee knowledge, experiences, and skills through the whole department or organization. Knowledge sharing comprises a set of shared understandings related to providing employees access to relevant information and building and using knowledge networks within organizations (Hogel The author would like to thank the National Science Council (NSC) of the Republic of China, Taiwan for financially supporting this research.

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et al., 2003). Moreover, knowledge sharing occurs at the individual and organizational levels. For individual employees, knowledge sharing is talking to colleagues to help them get something done better, more quickly, or more efficiently. For an organization, knowledge sharing is capturing, organizing, reusing, and transferring experience-based knowledge that resides within the organization and making that knowledge available to others in the business. A number of studies have demonstrated that knowledge sharing is essential because it enables organizations to enhance innovation performance and reduce redundant learning efforts (Calantone et al., 2002; Scarbrough, 2003). A firm can successfully promote a knowledge sharing culture not only by directly incorporating knowledge in its business strategy, but also by changing employee attitudes and behaviors to promote willing and consistent knowledge sharing (Connelly and Kelloway, 2003; Lin and Lee, 2004). Moreover, various studies focused on the relationship between knowledge sharing enablers and processes (Van den Hooff and Van Weenen, 2004a; Van den Hooff and Van Weenen, 2004b; Bock et al., 2005; Yeh et al., 2006), while others have focused on the relationship between knowledge sharing enablers and innovation performance (Calantone et al., 2002; Syed-Ikhsan and Rowland, 2004). However, researchers and practitioners have not tried an integrative model that explores the effectiveness of knowledge sharing from a holistic perspective, and little empirical research has examined the relationships among knowledge sharing enablers, processes, and firm innovation capability. To fill this gap, this study develops a research model that links knowledge sharing enablers, processes, and firm innovation capability. The study examines the influence of individual factors (enjoyment in helping others and knowledge self-efficacy), organizational factors (top management support and organizational rewards) and technology factors (information and communication technology use) on knowledge sharing processes and whether leads to superior firm innovation capability. Based on a survey of 172 employees from 50 large organizations in Taiwan, this study applies the structural equation modeling (SEM) to investigate the research model. Additionally, the current study contributes to knowledge sharing research by further clarifying which factors are essential for knowledge sharing effectively. At a minimum, the findings of this study provide a theoretical basis, and simultaneously can be used to analyze relationships among knowledge sharing enablers, processes, and firm innovation capability. From a managerial perspective, the findings of this study can improve understanding and practice of organizational management of knowledge sharing. Specifically, this study identified several factors essential to successful knowledge sharing, and discussed the implications of these factors for developing organizational strategies that encourage and foster knowledge sharing. Analysis model and hypotheses Figure 1 illustrates the general framework of strategic decision processes that are contrasted below. Following the approach proposed by Rajagopalan et al. (1993), the analytical framework of this study comprises three aspects: enablers, processes and outcomes. “Enablers” are the mechanism for fostering individual and organizational learning and also facilitate employee knowledge sharing within or across teams or work units. In related research, knowledge sharing enablers include the effects caused by employee motivators, organizational contexts, and information and communication

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Figure 1. A general framework for studying knowledge sharing

technology (ICT) applications (Taylor and Wright, 2004; Bock et al., 2005; Wasko and Faraj, 2005; Lin and Lee, 2006). The “knowledge sharing processes” dimension refers to how organization’s employees share their work-related experience, expertise, know-how, and contextual information with other colleagues. Knowledge sharing processes consist of both employee willingness to actively communicate with colleagues (i.e. knowledge donating) and actively consult with colleagues to learn from them (i.e. knowledge collecting). Finally, the organizational promotion of knowledge sharing is changing traditional ideas about managing intellectual resources and employee work styles by providing new processes, disciplines and cultures, thus constituting an organizational innovation (Darroch and McNaughton, 2002). The “outcomes” dimension reveals the effects of the degree of knowledge sharing effectively achieved on firm innovation capability. The literature recognizes the existence of different influences on employee knowledge sharing activities, such as individual, organizational, and technology factors (Lee and Choi, 2003; Connelly and Kelloway, 2003; Taylor and Wright, 2004). Referring to the individual dimension, most authors agree that knowledge sharing depends on individual characteristics, including experience, values, motivation, and beliefs. Wasko and Faraj (2005) suggested that individual motivators may enable employee willingness to share knowledge. Employees are motivated when they think that knowledge sharing behaviors will be worth the effort and able to help others. Therefore, the expectation of individual benefits can promote employees to share knowledge with colleagues. Furthermore, referring to the organizational dimension, organizational climate is usually made to capture efficiently the benefits of innovation-supportive culture (Saleh and Wang, 1993). In the context of knowledge sharing, the different aspects of organizational climate are critical drivers of knowledge sharing, such as reward systems linked to knowledge sharing (Bartol and Srivastava, 2002), open leadership climate (Taylor and Wright, 2004), and top management support (MacNeil, 2003; MacNeil, 2004). Finally, referring the technology dimension, ICT can be effectively used to facilitate the codification, integration, and dissemination of organizational knowledge (Song, 2002). For example, using ICT, such as groupware,

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online databases, intranet, and virtual communities, for communicating and sharing knowledge has been the focus of several previous researches (Koh and Kim, 2004). Knowledge sharing processes can be conceived as the processes through which employees mutually exchange knowledge and jointly create new knowledge (Van den Hooff and Van Weenen, 2004a). Ardichvill et al. (2003) discussed knowledge sharing as involving both the supply and the demand for new knowledge. Van den Hooff and Van Weenen (2004b) identified a two-dimension of knowledge sharing process that consists of knowledge donating and knowledge collecting. Knowledge donating can be defined as the process of individuals communicating their personal intellectual capital to others, while knowledge collecting can be defined as the process of consulting colleagues to encourage them to share their intellectual capital. Additionally, an important challenge for organizations is which motivations influence both knowledge donating and knowledge collecting and lead to superior firm innovation capability (Jantunen, 2005). Therefore, this study focuses on the relationships between knowledge sharing enablers (i.e. individual, organizational, and technology factors) and firm innovation capability by elaborating on the significance of knowledge sharing processes (i.e. knowledge donating and knowledge collecting). Figure 2 illustrates the set of hypotheses considered in the research model that is discussed below. Individual factors as determinants of knowledge-sharing processes The research considered here has focused on individual factors that promote or inhibit organizational knowledge sharing activities. The two factors that may be proximal determinants of knowledge sharing are identified: enjoyment in helping others and knowledge self-efficacy. Enjoyment in helping others is derived from the concept of altruism. Organ (1988) defined altruism includes discretionary behaviors that help specific others with organizationally relevant tasks or problems. Knowledge workers may be motivated by relative altruism owning to their desire to help others (Constant et al., 1994; Davenport and Prusak, 1998). Previous research shows that employees are intrinsically motivated to contribute knowledge because engaging in intellectual pursuits and solving problems is challenging or pleasurable, and because they enjoy

Figure 2. The research model

helping others (Wasko and Faraj, 2000; Wasko and Faraj, 2005). Knowledge workers who derive enjoyment from helping others may be more favorable oriented toward knowledge sharing and more inclined to share knowledge – in terms of both donation and collecting. The following hypothesis thus is proposed: H1. Enjoyment in helping others positively influences employee willingness to both (a) donate and (b) collect knowledge. Self-efficacy is defined as the judgments of individuals regarding their capabilities to organize and execute courses of action required to achieve specific levels of performance (Bandura, 1986). Self-efficacy can help motivate employees to share knowledge with colleagues (Wasko and Faraj, 2005). Researchers have also found that employees with high confidence in their ability to provide valuable knowledge are more likely to accomplish specific tasks (Constant et al., 1994). Knowledge self-efficacy typically manifests in people believing that their knowledge can help to solve job-related problems and improve work efficacy (Luthans, 2003). Employees who believe that they can contribute organizational performance by sharing knowledge will develop greater positive willingness to both contribute and receive knowledge. Hence, the following hypothesis is proposed: H2. Knowledge self-efficacy positively influences employee willingness to both (a) donate and (b) collect knowledge. Organizational factors as determinants of knowledge-sharing processes Top management support is considered one of the important potential influences on organizational knowledge (Connelly and Kelloway, 2003). Numerous studies have found top management support essential to creating a supportive climate and providing sufficient resources (Lin, 2006). MacNeil (2004) emphasized the importance of the visible top management’s support to organizational knowledge sharing climate. Moreover, Lin and Lee (2004) proposed that the perception of top management encouragement of knowledge sharing intentions is necessary for creating and maintaining a positive knowledge sharing culture in an organization. Consequently, this study expects that top management support positively influences employee willingness to share knowledge with colleagues – both in terms of donating and collecting. The following hypothesis is therefore formulated: H3. Top management support positively influences employee willingness to both (a) donate and (b) collect knowledge. Organizational rewards indicate what the organization values shape employee behaviors (Cabrera and Bonache, 1999). Organizational rewards can range from monetary incentives such as increased salary and bonuses to non-monetary awards such as promotions and job security (Davenport and Prusak, 1998; Hargadon, 1998). Several organizations have introduced reward systems to encourage employees to share their knowledge. For example, Buckman Laboratories recognizes its 100 top knowledge contributors through an annual conference at a resort. Moreover, Lotus Development, a division of IBM, bases 25 per cent of the total performance evaluation of its customer support workers on the extent of their knowledge sharing activities (Bartol and Srivastava, 2002). This study thus expects that if employees believe they can receive organizational rewards by offering their knowledge, they would develop

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greater positive willingness to both donate and receive knowledge. The following hypothesis is proposed: H4. Organizational rewards positively influence employee willingness to both (a) donate and (b) collect knowledge.

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Technology factors as determinants of knowledge-sharing processes Information and communication technology (ICT) use and knowledge sharing are closely linked, because ICT can enable rapid search, access and retrieval of information, and can support communication and collaboration among organizational employees (Huysman and Wulf, 2006). Within knowledge sharing, the use of ICT development facilitate new methods and applications (such as groupware, online databases, intranet, virtual communities, etc.), and allow firms to expand available social networks by overcoming geographical boundaries and thus achieving more effective collaborative activities (Pan and Leidner, 2003). Moreover, Zack (1999) believes that ICT plays the following three different roles in knowledge management activities: (1) Obtaining knowledge. (2) Defining, storing, categorizing, indexing, and linking knowledge-related digital items. (3) Seeking and identifying related content. Moreover, according to Yeh et al. (2006), effective knowledge management requires employees sharing their knowledge through ICT facilities, because ICT can provide communication channels for obtaining knowledge, correcting flow processes, and identifying the location of knowledge carriers and requesters. Hence, the following hypothesis is proposed: H5. ICT support positively influences employee willingness to both (a) donate and (b) collect knowledge. Knowledge-sharing processes and firm innovation capability It is obvious that a firm’s ability to transform and exploit knowledge may determine its level of organizational innovation, such as faster problem-solving capability and enhanced rapid reaction to new information. Many scholars stress the importance of knowledge sharing to enhancing innovation capability (Liebowitz, 2002; Lin, 2006). The definition of Davenport and Prusak (1998) indicates that knowledge is personal. Organizations can only begin to effectively manage knowledge resources when employees are willing to cooperate with colleagues to contribute knowledge to the firm. Knowledge donating aims to see individual knowledge become group and organizational knowledge over time, which in turn improves the stock of knowledge available to the firm. A firm that promotes employees to contribute knowledge within groups and organizations is likely to generate new ideas and develop new business opportunities, thus facilitating innovation activities (Darroch and McNaughton, 2002). Knowledge collecting consists of processes and mechanisms for gathering information and knowledge from internal and external sources. The process of knowledge collecting in which organizational knowledge becomes group and individual knowledge, involves the internalization and socialization of knowledge.

Hansen (1999) suggested that knowledge collecting represents a key aspect of successful project completion, especially for organizations heavily involved in innovation projects. The generation of new ideas and the improvement of firm products, because of a better absorptive capacity, could improve innovation performance (Jantunen, 2005). Specifically, a firm with proficiency in gathering and integrating knowledge is more likely to be unique, rare, and difficult for rivals to replicate, and hence has the potential to sustain high levels of firm innovation capability. This study expects that employee willingness to both donate and collect knowledge with colleagues is likely to sustain innovativeness and thus better position the firm in terms of long-term competitive advantage. The following hypotheses thus are formulated: H6. Employee willingness to donate knowledge positively influences firm innovation capability. H7. Employee willingness to collect knowledge positively influences firm innovation capability. Methods Sample and data collection A draft questionnaire was pilot tested by five MIS professors to ensure that the content and wording were free of problems. A total of 30 participants from ten organizations in five industries in Taiwan then examined the revised questionnaire. These participants were given the questionnaire and asked to examine it for meaningfulness, relevance, and clarity. A total of 50 organizations were randomly selected from the top 1,000 firms list published by Common Wealth magazine in 2004, which listed the 1,000 largest firms in Taiwan. Ten survey packets were mailed to each of these 50 organizations in the summer of 2005. A cover letter explaining the study objectives and a stamped return envelope were enclosed. Follow-up letters also were sent about three weeks after the initial mailings. Of the 500 questionnaires distributed, 172 completed and usable questionnaires were returned, representing a response rate of 34.4 percent. Table I lists the respondent company characteristics, including industry type, gender, age, education level, working experience, and position. Measures In this study, items used to operationalize the constructs were mainly adapted from previous studies and modified for use in the knowledge-sharing context. All constructs were measured using multiple items. All items were measured using a seven-point Likert-type scale (ranging from 1 ¼ strongly disagree to 7 ¼ strongly agree). A list of items for each scale is presented in the appendix. The measurement approach for each theoretical construct in the model is described briefly below. Enjoyment in helping others was measured using four items derived from Wasko and Faraj (2000), which focused on employee perceptions of pleasure obtained through sharing knowledge. A four-item scale measuring knowledge self-efficacy was adapted from a measure developed by Spreitzer (1995). It assesses employee judgments of their capability to share knowledge that is valuable to the organization. Top management support was measured using four items adapted from studies by Tan and Zhao (2003).

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Frequency Demographic characteristics Industry type Manufacturing Banking/insurance Computers/communication Transportation Retail/wholesale Real estate/construction Health/foods Utility Others

13 6 10 4 6 5 3 1 2

No. of response 51 27 32 17 21 11 5 2 6

Gender Male Female

126 46

73.3 26.7

Age 21-25 26-30 31-35 36-40 Over 40 Missing

13 70 41 24 21 3

7.6 40.7 23.8 13.9 12.2 1.8

11 102 59

6.4 59.3 34.3

Working experience 0-3 years 3-5 years 5-10 years 10-15 years Over 15 years Missing

18 57 43 30 21 3

10.5 33.1 25.0 17.4 12.2 1.8

Position Director Manager Chief employee Employee Others

13 31 72 51 5

7.6 18.0 41.8 29.7 2.9

Education level High school Bachelor Graduate

Table I. Profile of respondents (n ¼ 172)

No. of company

% 29.7 15.7 18.6 9.9 12.2 6.4 2.9 1.1 3.5

These measurements assess the extent to which employees perceive support and encouragement of knowledge-sharing from top management. Organizational rewards were measured using four items derived from Hargadon (1998) and Davenport and Prusak (1998), which were defined as the extent to which employees believe that they will receive extrinsic incentives (such as salary, bonus, promotion, or job security) for sharing knowledge with colleagues. Additionally, ICT use was measured based on four

items taken from Lee and Choi (2003), which referred to the degree of technological usability and capability regarding knowledge sharing. Knowledge donating was measured using three items adapted from an investigation by Van den Hooff and Van Weenen (2004a), which assess the degree of employee willingness to contribute knowledge to colleagues. Knowledge collecting was measured using four items derived from Van den Hooff and Van Weenen (2004a), which referred to collective beliefs or behavioral routines related to the spread of learning among colleagues. Finally, firm innovation capability was measured using six items derived from Calantone et al. (2002), which focused on firm rate of innovation adoption. Data analysis and results Data analysis in this study was performed using structural equation modeling (SEM) to validate the research model. This approach was chosen because of its ability to test casual relationships between constructs with multiple measurement items (Joreskog and Sorbom, 1996). Numerous researchers have proposed a two-stage model-building process for applying SEM (Joreskog and Sorbom, 1996). The measurement model was first examined for instrument validation, followed by an analysis of the structural model for testing associations hypothesized in the research model. These results are described next. Measurement model The measurement model with all eight constructs was assessed using confirmatory factor analysis (CFA) (Anderson and Gerbing, 1992). The appendix presents factor loadings of indicators in the measurement model. All factor loadings exceed 0.5 and each indicator was significant at 0.01 levels. Moreover, from the appendix, the observed normed x2 for measurement model was 1.99 (x2 =df ¼ 1:99; df ¼ 201) which was smaller than 3 recommended by Bagozzi and Yi (1988). Other fit indexes included the goodness-of-fit index (GFI) and comparative fit index (CFI), they exceeded the recommended cut-off level of 0.9 (Bagozzi and Yi, 1988). The adjusted goodness-of-fit index (AGFI) also exceeded the recommended cut-off level of 0.8 (Chau and Hu, 2001). The root mean square error of approximation (RMSEA) was below the cut-off level of 0.08 recommended by Browne and Cudeck (1993). The combination of these results suggested that measurement model exhibited a good level of model fit. The psychometric properties of eight constructs and indicators (dimensional scales) were assessed with respect to convergent validity and discriminant validity (Joreskog and Sorbom, 1996). The reliability of the constructs (composite reliability) and the average variance extracted were used as the measures for convergent validity (Fornell and Larcker, 1981; Bagozzi and Yi, 1988). From the appendix, the composite reliability of all constructs exceeded the benchmark of 0.7 recommended by Nunnally and Bernstein (1994). In terms of average variance extracted, all constructors exceed the suggested value of 0.5 (Bagozzi and Yi, 1988), indicating the measure has adequately convergent validity. Discriminant validity is demonstrated when the respective average variance extracted is larger than the squared correlation between two constructs (Fornell and Larcker, 1981). Table II shows the comparison between squared correlations of two constructs (off-diagonal elements) and the average variance extracted for each construct (diagonal elements). Overall, all of the eight constructs show evidence of high discriminant validity. In summary, the measurement

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1. 2. 3. 4. 5. 6. 7. 8.

Enjoyment in helping others Knowledge self-efficacy Top management support Organizational rewards ICT use Knowledge donating Knowledge collecting Firm innovation capability

1

2

3

4

5

6

7

8

0.59 * 0.21 0.32 0.24 0.45 0.25 0.32 0.50

0.67 * 0.37 0.31 0.13 0.41 0.21 0.39

0.51 * 0.19 0.27 0.38 0.31 0.25

0.58 * 0.23 0.10 0.26 0.26

0.64 * 0.08 0.37 0.17

0.66 * 0.50 0.39

0.61 * 0.51

0.59 *

Notes: *Diagonal elements are the average variance extracted for each of the eight constructs. Table II. Test of discriminant validity

Off-diagonal elements are the squared correlations between constructs. For discriminant validity, diagonal elements should be larger than off-diagonal; All of the correlations are significant at the p , 0.01 level

model demonstrated adequate reliability, convergent validity and discriminant validity. Structural model The first step in model estimation was to examine the goodness-of-fit of the hypothesized model in Figure 3. The observed normed x 2 was 2.24 (x2 =df ¼ 477:25=213). The GFI is 0.88, AGFI is 0.84, NFI is 0.87, CFI is 0.92, and RMSEA is 0.06. The results of goodness-of-fit indices exhibited a moderate but acceptance level of overall model fit and, therefore, provided support to the overall validity of the structural model. The second step in model estimation was to examine the significance of each hypothesized path in the research model. The results of the analysis are depicted in Figure 3 (significant paths depicted by bold lines and insignificant paths by dash lines) and summarized in Table III. In hypotheses H1a, H1b, H2a, and H2b, this study examined the effects of individual factors on knowledge sharing processes. The results found that both

Figure 3. Results of structural model

Hypothesis

Hypothesized path

H1a

Enjoyment in helping others ! knowledge donating Enjoyment in helping others ! knowledge collecting Knowledge self-efficacy ! knowledge donating Knowledge self-efficacy ! knowledge collecting Top management support ! knowledge donating Top management support ! knowledge collecting Organizational rewards ! knowledge donating Organizational rewards ! knowledge collecting ICT use ! knowledge donating ICT use ! knowledge collecting Knowledge donating ! Firm innovation capability Knowledge collecting ! Firm innovation capability

H1b H2a H2b H3a H3b H4a H4b H5a H5b H6 H7

Path coefficient

Results

0.31 *

Supported

0.27 * 0.45 * 0.38 * 0.23 * 0.19 * 0.12 0.07 0.04 0.28 * 0.29 *

Supported Supported Supported Supported Supported Not supported Not supported Not supported Supported Supported

0.41 *

Supported

Note: *p , 0.01

enjoyment in helping others and knowledge self-efficacy were found to positively influence knowledge donating and knowledge collecting. Furthermore, the top management support variable was found to be influential in knowledge sharing processes, supporting H3a and H3b. However, hypotheses H4a and H4b were not supported, the results show that organizational rewards had no significant relationship with employee willingness to donate and collect knowledge. Moreover, ICT use was found to positively influence knowledge collecting (H5b), but the linking ICT use and knowledge donating was not supported (H5a). Finally, the impact of firm innovation capability was found to be strongly positively associated with employee willingness to donate and collect knowledge, supporting hypotheses H6 and H7.

Discussion and implications This study is interesting from both theoretical and practical perspectives. Theoretically, this study proposed a research model for empirical studies to link knowledge sharing enablers and processes with firm innovation capability. The results from a structural equation modeling approach provide quite a strong support for the hypothesized relations. The results show that two individual factors (enjoyment in helping others and knowledge self-efficacy) and one of the organizational factor (top management support) significantly influence knowledge sharing processes. The results also indicate that employee willingness to both donate and collect knowledge enable the firm to improve innovation capability. From a practical perspective, the relationships among knowledge sharing enablers, processes, and firm innovation capability may provide a clue regarding how firms can promote knowledge sharing culture to sustain their innovation performance. Discussion of the findings, implications for practitioners and limitations and directions for future research are presented below.

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Table III. Results of structural model

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Discussion of findings First, the findings of this study indicate that both enjoyment in helping others and knowledge self-efficacy were strongly associated with employee willingness to share knowledge. This result implies that employees who feel pleasure in sharing knowledge and thus helping others tend to be more motivated to donate and collect knowledge with colleagues. Additionally, a sense of the competence and confidence of employees may be requirement for employees to engage in knowledge sharing. That is, employees who believe in their ability to share organizationally useful knowledge tend to have stronger motivation to share knowledge with their colleagues. Related to organizational factors, top management support was effective for employee willingness to both donate and collect knowledge with colleagues, but organizational rewards was not. The findings indicate that perceptions of top management encouragement of knowledge sharing influence employee willingness to share knowledge. Therefore, management should recognize that organizational rewards only secure temporary compliance. To promote knowledge sharing activities, top management facilitation of social interaction culture is more important than extrinsically motivated employees (such as those motivated by monetary compensation). Moreover, the results show a positive significant relationship between ICT use and knowledge collecting, but no significant relationship with knowledge donating. Although analytical results show that most respondents agreed that the use of various ICT tools help employees in receiving knowledge, the results reveal no significant relationship between ICT use and knowledge donating. This phenomenon may be explained by the fact that organizations exhibit a tendency for employees to use knowledge as their source of power for personal advantage rather than as an organizational resource (Syed-Ikhsan and Rowland, 2004). Knowledge thus cannot be distributed simply via online database or intranet. This finding might also be caused by the fact that investing in ICT alone is not enough to facilitate knowledge donating, because ICT can provide access to knowledge, but access is not the same as using or applying knowledge. That is, knowledge sharing involves social and human interaction, not simply ICT usage. Finally, the results indicate that employee willingness to both donate and collect knowledge is significantly related to firm innovation capability. The findings suggest that innovation involves a broad process of knowledge sharing that enables the implementation of new ideas, processes, products, or services. As Jantunen (2005) noted, a positive knowledge sharing culture helps firms improve innovation capability. Therefore, the change introduced by the companies involves a broad incorporation of knowledge sharing mechanisms which attempt to foster innovation, such as the allocation of a budget for providing adequate training for knowledge transfer, the linking of staff-turnover to the generation of new ideas, or the creation of teams systematically devoted to new initiatives generation. Implications for practitioners This study proposes the following implications for helping managers establish a successful knowledge sharing strategy. First, the findings of this study confirm that individual factors are associated with knowledge sharing processes. Since enjoyment in helping others significantly influenced employee knowledge sharing behaviors,

managers need to increase the level of enjoyment that employees experience as they help one another through knowledge sharing. Managers interested in developing and sustaining knowledge sharing should focus on enhancing the positive mood state of employees regarding social exchange (i.e. enjoyment in helping others), which precedes knowledge sharing activities. Moreover, managers should pay more attention to provide useful feedback to improve employee knowledge self-efficacy. For instance, a highly self-efficacious staff can be established being by recruiting and selecting employees who are proactive, and who have high cognitive aptitude and self-esteem and are intrinsically motivated. Second, top management facilitation of knowledge sharing is important to enable a firm with superior competence in knowledge sharing to succeed in innovation performance. However, this study has verified that organizational rewards are not significantly related to knowledge sharing processes. Therefore, this study suggests that do not emphasize organizational rewards (such as salary incentive, bonuses, promotion incentive, or job security) as a primary knowledge sharing mechanism, because extrinsic rewards secure online temporary compliance (Kohn, 1993). This means that organizational rewards may provide temporary incentives for knowledge sharing, but is not fundamental force forming employee knowledge sharing behaviors. Finally, reliance on a techno-centric approach to knowledge sharing is insufficient for achieving the necessary social relationships and interpersonal interactions of employees for facilitating employee willingness to donate knowledge. Therefore, all transitional elements, such as organizational culture, top management support, ICT use, and human resources, should always be considered together when promoting knowledge sharing initiatives. Limitations and directions for future research Future studies should focus on five areas to overcome the limitations of the present study. First, previous research has suggested a significant relationship between individual differences and employee perceptions of knowledge sharing culture (Connelly and Kelloway, 2003). Future research can examine how personal traits (such as age, level of education, and working experiences) and organizational characteristics (such as firm size and industry type) may moderate the relationships between knowledge enablers and processes. Second, the significance of inter-organizational level in relation to knowledge sharing has not been considered. Future research could consider outer knowledge sharing to come from the stakeholders such as customers and suppliers, which represent valuable sources of intelligence and new ideas. Third, this study focused on empirical studies to link knowledge sharing enablers and processes with firm innovation capability. This study, however, did not consider all enablers that are critical for knowledge sharing. Van den Hooff and Van Weenen (2004a) proposed that communication climate and employee affective commitment are antecedents for knowledge sharing. Lee et al. (2006) verified empirically that dimensions of climate maturity (e.g. learning oriented, trust, and employee commitment) had an effect on the knowledge quality and level of knowledge sharing. Further research considering these factors could enhance an understanding of critical determinants for knowledge sharing. Fourth, the sample was drawn from 172 employees in 50 Taiwan organizations. Hence, the research model should be tested further using samples from other countries, since cultural differences among organizations influence employee perceptions regarding knowledge sharing, and

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further testing thus would provide a more robust test of the hypotheses. Finally, an important focus for future research is the long-term effects (i.e. whether the factorable employees reactions were temporary or whether such reactions were sustainable) of motivation on employee knowledge sharing behaviors. Future studies can gather longitudinal data to examine the causality and interrelationships between variables that are important to knowledge sharing processes.

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Knowledge sharing

Appendix

Constructs

Indicators/Items

Enjoyment in helping others

I enjoy sharing my knowledge with colleagues I enjoy helping colleagues by sharing my knowledge It feels good to help someone by sharing my knowledge Sharing my knowledge with colleagues is pleasurable I am confident in my ability to provide knowledge that others in my company consider valuable I have the expertise required to provide valuable knowledge for my company It does not really make any difference whether I share my knowledge with colleagues (reversed coded) Most other employees can provide more valuable knowledge than I can (reversed coded) Top managers think that encouraging knowledge sharing with colleagues is beneficial Top managers always support and encourage employees to share their knowledge with colleagues Top managers provide most of the necessary help and resources to enable employees to share knowledge Top managers are keen to see that the employees are happy to share their knowledge with colleagues Sharing my knowledge with colleagues should be rewarded with a higher salary Sharing my knowledge with colleagues should be rewarded with a higher bonus Sharing my knowledge with colleagues should be rewarded with a promotion Sharing my knowledge with colleagues should be rewarded with an increased job security Employees make extensive use of electronic storage (such as online databases and data warehousing) to access knowledge

Knowledge self-efficacy

Top management support

Organizational rewards

ICT use

Factor loadings

Composite reliability

Average variance extracted

0.77

0.84

0.59

0.86

0.67

0.72

0.51

0.75

0.58

331 0.87 0.71 0.84 0.88 0.85 0.81 0.85 0.80 0.68 0.73 0.67 0.70 0.80 0.75 0.84

0.87

0.83

0.64 (continued)

Table AI. Scale items and measurement model loadings

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332

Table AI.

Indicators/Items

Employees use knowledge networks (such as groupware, intranet, virtual communities, etc.) to communicate with colleagues My company uses technology that allows employees to share knowledge with other persons inside the organization My company uses technology that allows employees to share knowledge with other persons outside the organization Knowledge When I have learned something new, I tell donating my colleagues about it When they have learned something new, my colleagues tell me about it Knowledge sharing among colleagues is considered normal in my company Knowledge I share information I have with colleagues collecting when they ask for it I share my skills with colleagues when they ask for it Colleagues in my company share knowledge with me when I ask them to Colleagues in my company share their skills with me when I ask them to Firm innovation Our company frequently tries out new capability ideas Our company seeks new ways of doing things Our company is creative in its operating methods Our company is frequently the first to market new products and services Innovation is perceived as too risky in our company and is resisted (reversed coded) Our new product introduction has increased during the last five years

Factor loadings

Composite reliability

Average variance extracted

0.78

0.66

0.80

0.61

0.77

0.57

0.81 0.80 0.75 0.72 0.81 0.83 0.75 0.81 0.84 0.70 0.72 0.78 0.82 0.75 0.81 0.77

Notes: All t-values are significant at p , 0.01; Measurement model goodness-of-fit: x2 =df ¼ 417:6=201 ¼ 1:99; GFI ¼ 0:90; AGFI ¼ 0:85; NFI ¼ 0:89; CFI ¼ 0:94; RMSEA ¼ 0:05

About the author Hsiu-Fen Lin is an Assistant Professor of Information Management in the Department of Shipping and Transportation Management, National Taiwan Ocean University. Her primary research interests include electronic commerce, knowledge management and organizational impact of information technology.

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